Pedestrian Detection with Radar and Computer Vision

Instead of detecting other vehicles, some systems are designed to detect pedestrians and other vulnerable road users. Images from a forward-looking camera are analysed to identify shapes and characteristics typical of humans. The way in which they are moving relative to the path of the vehicle is calculated to determine whether or not they are in danger of being struck. If so, the AEB system applies full braking to bring the car to a halt and, at the same time, it may issue a warning to the driver. Predicting human behaviour is difficult and the algorithms used in pedestrian detection systems are very sophisticated. The system must be able to react properly to a valid threat but must not apply the brakes where there is no danger e.g. where a pedestrian is walking to the edge of the pavement but then stops to allow the car to pass. These systems invariably employ a camera combined with a radar– something called sensor fusion. New technologies are appearing on the market that use infra-red and can also operate in very low light conditions.Typical city accidents occur at junctions and roundabouts. A driver is waiting behind other cars approaching a roundabout. He is concentrating on the traffic on the roundabout and sees a gap. He expects the car in front to move forward and accelerates, only to find that the driver in front has not moved. The impact that follows is typical of city driving: low speed, but with a high risk of a debilitating whiplash injury to the driver of the struck vehicle. While injury severities are usually low, these accidents are very frequent and represent 26% of all crashes.Low-speed AEB systems use sensors to monitor the road ahead, typically 6-8m. One common technology is a LIDAR (Light Detection and Ranging) sensor, typically mounted at the top of the windscreen, which determines whether or not there is an object in front of the car which presents a risk. If there is, the AEB system will, typically, pre-charge the brakes so that the car will provide its most efficient braking response, should the driver react. If the driver does not respond, the car will automatically apply the brakes to avoid, or in some cases to mitigate, the accident. If, at any point, the driver intervenes to avoid the accident, by hard braking or avoidance steering, the system will disengage.For its fitment survey, Euro NCAP defines city systems as those which can avoid an impact by autonomous braking at speeds up to 20km/h where 80% of all whiplash injuries occur. These systems look for the reflectivity of a typical vehicle and so are not sensitive to pedestrians or roadside furniture. Since these systems are within the sweep of the wipers they can also operate in poor weather conditions.Similar accident scenarios occur on the open road. A driver on a motorway or a dual carriageway might be distracted and fail to recognise that the traffic in front of him is coming to a stop. By the time he notices the danger, it is too late for him to apply the brakes and avoid the impact or he may misjudge the braking of the car infront and fail to apply sufficient braking force.To work at higher speeds, Inter-Urban AEB systems use long-range radars to ‘look’ further ahead of the vehicle (typically 200m).. The radar data is analysed to determine whether or not the vehicle could potentially collide with any obstacles it sees. If so, the AEB system might typically operate as follows: a warning signal is given to the driver to try to alert him to the danger. If the driver does not respond, a second warning may be given (for example a brake jerk or seatbelt tug) and the brakes will be pre-armed for maximum braking. Again, if there is no reaction from the driver, the system will itself apply heavy braking. Some systems also prepare the restraint systems for optimum performance in the impact, for example by pre-tensioning the seatbelts.Systems fall into this category if they do more than simply warn the driver and operate over the speed range 50-80km/h. Some systems designed to operate primarily at Inter-Urban speeds may also provide benefit in city driving. For example, they may not be able to avoid accidents at low speeds but will be able to warn the driver and provide some mitigation effect. These systems are designed to see other road traffic including, in some cases, motorcycles and trucks. A potential advantage of radar sensors is their ability to function in all weathers and lighting conditions.。

Unit 3 On the moveReadingSelf-driving cars—destination known?自动驾驶汽车----目的地已知吗?Mr Zhang casually glances at the empty driver's seat and says, "Destination Grand Hotel. Family mode. Start." The car responds immediately, easing smoothly into the busy traffic and avoiding obstacles on the road. Inside the car, the family have chosen their entertainment from a pop-up display panel, ready for the journey ahead. This imagined scene provides a likely future reality for self-driving cars, also known as autonomous vehicles(A Vs).张先生漫不经心地看了-眼空荡荡的驾驶座，说道:“目的地格兰酒店。

家庭模式。

出发。

”汽车立即响应，平稳驶入繁忙的车流，避开路上的障碍。

车里面，一家人已经在弹出式显示面板上选好了娱乐项目，为前方的旅途作好准备。

这个想象中的场景展示了自动驾驶汽车，也叫自主汽车，一种可能的未来的现实。

However, before this evolution in transport becomes a revolution, it must be fully understood how self-driving cars work. Put simply, self-driving cars must "see" and "behave" appropriately to be safe on the road. They do this through various hardware and deep-learning AI. Cameras as well as sensors like radar and lidar capture a variety of data from the external environment. Once the data is sent to the AI system, the "brain" of the car, it is analysed and put together like a puzzle so that the self-driving car can "see" its surroundings and determine its position. Meanwhile, the Al system identifies patterns from the data and learns from them. An action plan is then created to instruct the car how to "behave" in real time: stay in the lane, move into another one, speed up or slow down. Next, the necessary mechanical controls, such as the accelerator and brakes, are activated by the AI system, allowing the car to move in line with the action plan.然而，在这种交通方式的变革成为一场革命之前，必须充分了解自动驾驶汽车的工作原理。

无人驾驶英语作文1Unmanned driving is a revolutionary technology that is transforming the way we think about transportation. The principle and working mode of unmanned driving are based on a complex combination of advanced sensors, powerful computer algorithms, and precise control systems.Sensors such as lidar, radar, and cameras are installed on the vehicle to collect information about the surrounding environment. These sensors can detect objects, distances, speeds, and other relevant data. The collected data is then sent to a powerful computer system that runs complex algorithms. These algorithms analyze the data in real-time to understand the situation on the road.For example, when the sensors detect a pedestrian crossing the road, the algorithms calculate the distance and speed of the pedestrian and the vehicle. Based on this analysis, the system makes a decision to slow down or stop the vehicle to avoid a potential collision.The computer algorithms also control the vehicle's steering, acceleration, and braking. They take into account various factors like traffic rules, road conditions, and the behavior of other vehicles to ensure a safe and smooth journey.In conclusion, unmanned driving technology holds great promise forimproving road safety and transportation efficiency. However, there are still many challenges to overcome, such as ensuring the reliability and security of the systems and addressing legal and ethical issues. But with continuous research and development, unmanned driving is likely to become an integral part of our future transportation landscape.2The development of driverless technology has been a remarkable journey. In the early days, it was just a concept in the minds of visionary scientists and engineers. They spent countless hours in laboratories, conducting experiments and making theoretical breakthroughs.As time went by, this technology gradually moved from the realm of theory to practical applications. Some cities began to launch pilot projects to test the feasibility and effectiveness of driverless vehicles on public roads. These pilot projects have achieved certain results. For example, they have shown that driverless cars can significantly improve traffic efficiency and reduce the occurrence of accidents caused by human errors.The current state of driverless technology is both promising and challenging. On one hand, advancements in sensors, artificial intelligence, and communication systems have made driverless vehicles more intelligent and reliable. They can better adapt to complex road conditions and interact with other road users. On the other hand, there are still issues such as legal regulations, ethical considerations, and public acceptance that need to beaddressed.In conclusion, the development of driverless technology is an ongoing process. While it holds great potential to transform the way we travel and commute, it also requires continuous efforts and cooperation from various sectors to ensure its safe and widespread adoption.3Unmanned Driving: A Double-Edged SwordIn recent years, the development of unmanned driving technology has been making remarkable progress. It is a revolutionary innovation that brings both benefits and challenges to our society.The advantages of unmanned driving are quite obvious. Firstly, it can significantly enhance traffic safety. Human errors, such as distraction, fatigue, or drunk driving, are major causes of accidents. Unmanned driving systems, however, are not prone to these mistakes and can make more accurate and timely decisions, thereby reducing the number of traffic accidents and saving countless lives. Secondly, unmanned driving can improve traffic efficiency. The precise control and optimization of routes by intelligent systems can alleviate traffic congestion and make our commutes more smooth and time-efficient.Nevertheless, unmanned driving also has its drawbacks. One of the significant concerns is the potential unemployment it may cause. Many people whose occupations are related to driving, such as taxi drivers andtruck drivers, might lose their jobs as this technology becomes more prevalent. This could lead to economic and social instability for a certain group of people. Additionally, there are technical and ethical issues that need to be addressed. For instance, how to ensure the reliability and security of unmanned driving systems in complex and unexpected situations is a challenging problem.In conclusion, unmanned driving is a double-edged sword. While it holds great promise for improving our lives, we must also carefully consider and address the negative impacts it may bring. Only through comprehensive and thoughtful planning can we fully realize the benefits of this technology and minimize its disadvantages.4With the rapid development of technology, driverless cars have emerged as a revolutionary concept that is set to transform our future transportation and society in profound ways. The impact of this innovation extends far beyond what we can currently imagine.Driverless technology is likely to revolutionize the way we travel. It will offer a higher level of convenience and safety. People no longer need to focus on driving, allowing them to utilize their travel time for work, relaxation or communication. This could significantly enhance the quality of our daily commutes.The advent of driverless cars will also have a considerable influenceon urban planning. Traditional parking spaces could be reduced, as these vehicles can drop off passengers and then park themselves in more efficient locations. This would free up valuable urban land for other purposes such as parks or additional housing.However, this technology also brings about certain challenges. For instance, it raises concerns regarding job losses for professional drivers. There are also issues related to legal and ethical responsibilities in case of accidents.In conclusion, while driverless technology holds great promise for a more efficient and convenient future, it is essential that we address the associated challenges proactively to ensure a smooth transition and a balanced development for our society.5Unmanned driving technology is revolutionizing various fields and holds immense potential for the future. In the realm of logistics and transportation, it promises increased efficiency and reduced costs. Autonomous trucks could operate around the clock, minimizing human errors and optimizing delivery routes. This would lead to faster and more reliable transportation of goods, benefiting both businesses and consumers.However, in public transportation, unmanned driving presents both opportunities and challenges. On one hand, self-driving buses and trains could provide more punctual and consistent services, especially in denselypopulated cities. They could also be programmed to adapt to different passenger demands and traffic conditions, enhancing the overall commuting experience.On the other hand, there are significant hurdles to overcome. Technical glitches and cybersecurity risks pose threats to the safety and reliability of unmanned systems. Moreover, legal and ethical questions arise, such as who is responsible in the event of an accident involving an autonomous vehicle.Despite these challenges, the continuous advancement in technology and research gives hope that unmanned driving will eventually transform our transportation landscape, making it more intelligent, efficient, and sustainable. But for now, a cautious and well-regulated approach is essential to ensure its successful integration into our daily lives.。

from Solving Pedestrian Detection?Mohamed Omran,Jan Hosang,and Bernt SchielePlanck Institute for Informatics Saarbrücken,Germanystname@mpi-inf.mpg.deAbstractEncouraged by the recent progress in pedestrian detec-tion,we investigate the gap between current state-of-the-art methods and the “perfect single frame detector”.We en-able our analysis by creating a human baseline for pedes-trian detection (over the Caltech dataset),and by manually clustering the recurrent errors of a top detector.Our res-ults characterize both localization and background-versus-foreground errors.To address localization errors we study the impact of training annotation noise on the detector performance,and show that we can improve even with a small portion of sanitized training data.To address background/foreground discrimination,we study convnets for pedestrian detection,and discuss which factors affect their performance.Other than our in-depth analysis,we report top perform-ance on the Caltech dataset,and provide a new sanitized set of training and test annotations 1.1.IntroductionObject detection has received great attention during re-cent years.Pedestrian detection is a canonical sub-problem that remains a popular topic of research due to its diverse applications.Despite the extensive research on pedestrian detection,recent papers still show significant improvements,suggest-ing that a saturation point has not yet been reached.In this paper we analyse the gap between the state of the art and a newly created human baseline (section 3.1).The results indicate that there is still a ten fold improvement to be made before reaching human performance.We aim to investigate which factors will help close this gap.We analyse failure cases of top performing pedestrian detectors and diagnose what should be changed to further push performance.We show several different analysis,in-cluding human inspection,automated analysis of problem1Ifyou are interested in our new annotations,please contact Shanshan Zhang.1010101010Figure 1:Overview of the top results on the Caltech-USA pedestrian benchmark (CVPR2015snapshot).At ∼95%recall,state-of-the-art detectors make ten times more errors than the human baseline.cases (e.g.blur,contrast),and oracle experiments (section 3.2).Our results indicate that localization is an important source of high confidence false positives.We address this aspect by improving the training set alignment quality,both by manually sanitising the Caltech training annotations and via algorithmic means for the remaining training samples (sections 3.3and 4.1).To address background versus foreground discrimina-tion,we study convnets for pedestrian detection,and dis-cuss which factors affect their performance (section 4.2).1.1.Related workIn the last years,diverse efforts have been made to im-prove the performance of pedestrian detection.Following the success of integral channel feature detector (ICF)[6,5],many variants [22,23,16,18]were proposed and showed significant improvement.A recent review of pedestrian de-tection [3]concludes that improved features have been driv-ing performance and are likely to continue doing so.It also shows that optical flow [19]and context information [17]are complementary to image features and can further boost 1a r X i v :1602.01237v 1 [c s .C V ] 3 F eb 2016detection accuracy.Byfine-tuning a model pre-trained on external data convolution neural networks(convnets)have also reached state-of-the-art performance[15,20].Most of the recent papers focus on introducing novelty and better results,but neglect the analysis of the resulting system.Some analysis work can be found for general ob-ject detection[1,14];in contrast,in thefield of pedestrian detection,this kind of analysis is rarely done.In2008,[21] provided a failure analysis on the INRIA dataset,which is relatively small.The best method considered in the2012 Caltech dataset survey[7]had10×more false positives at20%recall than the methods considered here,and no method had reached the95%mark.Since pedestrian detection has improved significantly in recent years,a deeper and more comprehensive analysis based on state-of-the-art detectors is valuable to provide better understanding as to where future efforts would best be invested.1.2.ContributionsOur key contributions are as follows:(a)We provide a detailed analysis of a state-of-the-art ped-estrian detection system,providing insights into failure cases.(b)We provide a human baseline for the Caltech Pedestrian Benchmark;as well as a sanitised version of the annotations to serve as new,high quality ground truth for the training and test sets of the benchmark.The data will be public. (c)We analyse how much the quality of training data affects the detector.More specifically we quantify how much bet-ter alignment and fewer annotation mistakes can improve performance.(d)Using the insights of the analysis,we explore variants of top performing methods:filtered channel feature detector [23]and R-CNN detector[13,15],and show improvements over the baselines.2.PreliminariesBefore delving into our analysis,let us describe the data-sets in use,their metrics,and our baseline detector.2.1.Caltech-USA pedestrian detection benchmarkAmongst existing pedestrian datasets[4,9,8],KITTI [11]and Caltech-USA are currently the most popular ones. In this work we focus on the Caltech-USA benchmark[7] which consists of2.5hours of30Hz video recorded from a vehicle traversing the streets of Los Angeles,USA.The video annotations amount to a total of350000bound-ing boxes covering∼2300unique pedestrians.Detec-tion methods are evaluated on a test set consisting of4024 frames.The provided evaluation toolbox generates plotsFilter type MR O−2ACF[5]44.2SCF[3]34.8LDCF[16]24.8RotatedFilters19.2Checkerboards18.5Table1:Thefiltertype determines theICF methods quality.Base detector MR O−2+Context+FlowOrig.2Ped[17]48~5pp/Orig.SDt[19]45/8ppSCF[3]355pp4ppCheckerboards19~01ppTable2:Detection quality gain ofadding context[17]and opticalflow[19],as function of the base detector.for different subsets of the test set based on annotation size, occlusion level and aspect ratio.The established proced-ure for training is to use every30th video frame which res-ults in a total of4250frames with∼1600pedestrian cut-outs.More recently,methods which can leverage more data for training have resorted to afiner sampling of the videos [16,23],yielding up to10×as much data for training than the standard“1×”setting.MR O,MR N In the standard Caltech evaluation[7]the miss rate(MR)is averaged over the low precision range of [10−2,100]FPPI.This metric does not reflect well improve-ments in localization errors(lowest FPPI range).Aiming for a more complete evaluation,we extend the evaluation FPPI range from traditional[10−2,100]to[10−4,100],we denote these MR O−2and MR O−4.O stands for“original an-notations”.In section3.3we introduce new annotations, and mark evaluations done there as MR N−2and MR N−4.We expect the MR−4metric to become more important as de-tectors get stronger.2.2.Filtered channel features detectorFor the analysis in this paper we consider all methods published on the Caltech Pedestrian benchmark,up to the last major conference(CVPR2015).As shown infigure1, the best method at the time is Checkerboards,and most of the top performing methods are of its same family.The Checkerboards detector[23]is a generalization of the Integral Channels Feature detector(ICF)[6],which filters the HOG+LUV feature channels before feeding them into a boosted decision forest.We compare the performance of several detectors from the ICF family in table1,where we can see a big improve-ment from44.2%to18.5%MR O−2by introducingfilters over the feature channels and optimizing thefilter bank.Current top performing convnets methods[15,20]are sensitive to the underlying detection proposals,thus wefirst focus on the proposals by optimizing thefiltered channel feature detectors(more on convnets in section4.2). Rotatedfilters For the experiments involving train-ing new models(in section 4.1)we use our own re-implementation of Checkerboards[23],based on the LDCF[16]codebase.To improve the training time we decrease the number offilters from61in the originalCheckerboards down to9filters.Our so-called Rota-tedFilters are a simplified version of LDCF,applied at three different scales(in the same spirit as Squares-ChnFtrs(SCF)[3]).More details on thefilters are given in the supplementary material.As shown in table1,Ro-tatedFilters are significantly better than the original LDCF,and only1pp(percent point)worse than Checker-boards,yet run6×faster at train and test time. Additional cues The review[3]showed that context and opticalflow information can help improve detections. However,as the detector quality improves(table1)the re-turns obtained from these additional cues erodes(table2). Without re-engineering such cues,gains in detection must come from the core detector.3.Analysing the state of the artIn this section we estimate a lower bound on the re-maining progress available,analyse the mistakes of current pedestrian detectors,and propose new annotations to better measure future progress.3.1.Are we reaching saturation?Progress on pedestrian detection has been showing no sign of slowing in recent years[23,20,3],despite recent im-pressive gains in performance.How much progress can still be expected on current benchmarks?To answer this ques-tion,we propose to use a human baseline as lower bound. We asked domain experts to manually“detect”pedestrians in the Caltech-USA test set;machine detection algorithms should be able to at least reach human performance and, eventually,superhuman performance.Human baseline protocol To ensure a fair comparison with existing detectors,we focus on the single frame mon-ocular detection setting.Frames are presented to annotators in random order,and without access to surrounding frames from the source videos.Annotators have to rely on pedes-trian appearance and single-frame context rather than(long-term)motion cues.The Caltech benchmark normalizes the aspect ratio of all detection boxes[7].Thus our human annotations are done by drawing a line from the top of the head to the point between both feet.A bounding box is then automatically generated such that its centre coincides with the centre point of the manually-drawn axis,see illustration infigure2.This procedure ensures the box is well centred on the subject (which is hard to achieve when marking a bounding box).To check for consistency among the two annotators,we produced duplicate annotations for a subset of the test im-ages(∼10%),and evaluated these separately.With a Intersection over Union(IoU)≥0.5matching criterion, the results were identical up to a single boundingbox.Figure2:Illustration of bounding box generation for human baseline.The annotator only needs to draw a line from the top of the head to the central point between both feet,a tight bounding box is then automatically generated. Conclusion Infigure3,we compare our human baseline with other top performing methods on different subsets of the test data(varying height ranges and occlu-sion levels).Wefind that the human baseline widely out-performs state-of-the-art detectors in all settings2,indicat-ing that there is still room for improvement for automatic methods.3.2.Failure analysisSince there is room to grow for existing detectors,one might want to know:when do they fail?In this section we analyse detection mistakes of Checkerboards,which obtains top performance on most subsets of the test set(see figure3).Since most top methods offigure1are of the ICF family,we expect a similar behaviour for them too.Meth-ods using convnets with proposals based on ICF detectors will also be affected.3.2.1Error sourcesThere are two types of errors a detector can do:false pos-itives(detections on background or poorly localized detec-tions)and false negatives(low-scoring or missing pedes-trian detections).In this analysis,we look into false positive and false negative detections at0.1false positives per im-age(FPPI,1false positive every10images),and manually cluster them(one to one mapping)into visually distinctive groups.A total of402false positive and148false negative detections(missing recall)are categorized by error type. False positives After inspection,we end up having all false positives clustered in eleven categories,shown infig-ure4a.These categories fall into three groups:localization, background,and annotation errors.Background errors are the most common ones,mainly ver-tical structures(e.g.figure5b),tree leaves,and traffic lights. This indicates that the detectors need to be extended with a better vertical context,providing visibility over larger struc-tures and a rough height estimate.Localization errors are dominated by double detections2Except for IoU≥0.8.This is due to issues with the ground truth, discussed in section3.3.Reasonable (IoU >= 0.5)Height > 80Height in [50,80]Height in [30,50]020406080100HumanBaselineCheckerboards RotatedFiltersm i s s r a t eFigure 3:Detection quality (log-average miss rate)for different test set subsets.Each group shows the human baseline,the Checkerboards [23]and RotatedFilters detectors,as well as the next top three (unspecified)methods (different for each setting).The corresponding curves are provided in the supplementary material.(high scoring detections covering the same pedestrian,e.g.figure 5a ).This indicates that improved detectors need to have more localized responses (peakier score maps)and/or a different non-maxima suppression strategy.In sections 3.3and 4.1we explore how to improve the detector localiz-ation.The annotation errors are mainly missing ignore regions,and a few missing person annotations.In section 3.3we revisit the Caltech annotations.False negatives Our clustering results in figure 4b show the well known difficulty of detecting small and oc-cluded objects.We hypothesise that low scoring side-view persons and cyclists may be due to a dataset bias,i.e.these cases are under-represented in the training set (most per-sons are non-cyclist walking on the side-walk,parallel to the car).Augmenting the training set with external images for these cases might be an effective strategy.To understand better the issue with small pedestrians,we measure size,blur,and contrast for each (true or false)de-tection.We observed that small persons are commonly sat-urated (over or under exposed)and blurry,and thus hypo-thesised that this might be an underlying factor for weak detection (other than simply having fewer pixels to make the decision).Our results indicate however that this is not the case.As figure 4c illustrates,there seems to be no cor-relation between low detection score and low contrast.This also holds for the blur case,detailed plots are in the sup-plementary material.We conclude that the small number of pixels is the true source of difficulty.Improving small objects detection thus need to rely on making proper use of all pixels available,both inside the window and in the surrounding context,as well as across time.Conclusion Our analysis shows that false positive er-rors have well defined sources that can be specifically tar-geted with the strategies suggested above.A fraction of the false negatives are also addressable,albeit the small and oc-cluded pedestrians remain a (hard and)significant problem.20406080100120# e r r o r s 0100200300loc a liz a tion ba c k g round a nnota e rrors#e r r o r s (a)False positive sources15304560# e r r o r s (b)False negative sources(c)Contrast versus detection scoreFigure 4:Errors analysis of Checkerboards [23]on the test set.(a)double detectionFigure 5:Example of analysed false positive cases (red box).Additional ones in supplementary material.3.2.2Oracle test casesThe analysis of section 3.2.1focused on errors counts.For area-under-the-curve metrics,such astheones used in Caltech,high-scoring errors matter more than low-scoring ones.In this section we directly measure the impact of loc-alization and background-vs-foreground errors on the de-tection quality metric (log-average miss-rate)by using or-acle test cases.In the oracle case for localization,all false positives that overlap with ground truth are ignored for evaluation.In the oracle tests for background-vs-foreground,all false posit-ives that do not overlap with ground truth are ignored.Figure 6a shows that fixing localization mistakes im-proves performance in the low FPPI region;while fixing background mistakes improves results in the high FPPI re-gion.Fixing both types of mistakes results zero errors,even though this is not immediately visible due to the double log plot.In figure 6b we show the gains to be obtained in MR O −4terms by fixing localization or background issues.When comparing the eight top performing methods we find that most methods would boost performance significantly by fix-ing either problem.Note that due to the log-log nature of the numbers,the sum of localization and background deltas do not add up to the total miss-rate.Conclusion For most top performing methods localiz-ation and background-vs-foreground errors have equal im-pact on the detection quality.They are equally important.3.3.Improved Caltech-USA annotationsWhen evaluating our human baseline (and other meth-ods)with a strict IoU ≥0.8we notice in figure 3that the performance drops.The original annotation protocol is based on interpolating sparse annotations across multiple frames [7],and these sparse annotations are not necessar-ily located on the evaluated frames.After close inspection we notice that this interpolation generates a systematic off-set in the annotations.Humans walk with a natural up and down oscillation that is not modelled by the linear interpol-ation used,thus in most frames have shifted bounding box annotations.This effect is not noticeable when using the forgiving IoU ≥0.5,however such noise in the annotations is a hurdle when aiming to improve object localization.1010−210−110010false positives per image18.47(33.20)% Checkerboards15.94(25.49)% Checkerboards (localization oracle)11.92(26.17)% Checkerboards (background oracle)(a)Original and two oracle curves for Checkerboards de-tector.Legend indicates MR O −2 MR O −4 .(b)Comparison of miss-rate gain (∆MR O −4)for top performing methods.Figure 6:Oracle cases evaluation over Caltech test set.Both localization and background-versus-foreground show important room for improvement.(a)False annotations (b)Poor alignmentFigure 7:Examples of errors in original annotations.New annotations in green,original ones in red.This localization issues together with the annotation er-rors detected in section 3.2.1motivated us to create a new set of improved annotations for the Caltech pedestrians dataset.Our aim is two fold;on one side we want to provide a more accurate evaluation of the state of the art,in particu-lar an evaluation suitable to close the “last 20%”of the prob-lem.On the other side,we want to have training annotations and evaluate how much improved annotations lead to better detections.We evaluate this second aspect in section 4.1.New annotation protocol Our human baseline focused on a fair comparison with single frame methods.Our new annotations are done both on the test and training 1×set,and focus on high quality.The annotators are allowed to look at the full video to decide if a person is present or not,they are request to mark ignore regions in areas cov-ering crowds,human shapes that are not persons (posters,statues,etc.),and in areas that could not be decided as cer-tainly not containing a person.Each person annotation is done by drawing a line from the top of the head to the point between both feet,the same as human baseline.The annot-ators must hallucinate head and feet if these are not visible. When the person is not fully visible,they must also annotate a rectangle around the largest visible region.This allows to estimate the occlusion level in a similar fashion as the ori-ginal Caltech annotations.The new annotations do share some bounding boxes with the human baseline(when no correction was needed),thus the human baseline cannot be used to do analysis across different IoU thresholds over the new test set.In summary,our new annotations differ from the human baseline in the following aspects:both training and test sets are annotated,ignore regions and occlusions are also an-notated,full video data is used for decision,and multiple revisions of the same image are allowed.After creating a full independent set of annotations,we con-solidated the new annotations by cross-validating with the old annotations.Any correct old annotation not accounted for in the new set,was added too.Our new annotations correct several types of errors in the existing annotations,such as misalignments(figure 7b),missing annotations(false negatives),false annotations (false positives,figure7a),and the inconsistent use of“ig-nore”regions.Our new annotations will be publicly avail-able.Additional examples of“original versus new annota-tions”provided in the supplementary material,as well as visualization software to inspect them frame by frame. Better alignment In table3we show quantitative evid-ence that our new annotations are at least more precisely localized than the original ones.We summarize the align-ment quality of a detector via the median IoU between true positive detections and a give set of annotations.When evaluating with the original annotations(“median IoU O”column in table3),only the model trained with original annotations has good localization.However,when evalu-ating with the new annotations(“median IoU N”column) both the model trained on INRIA data,and on the new an-notations reach high localization accuracy.This indicates that our new annotations are indeed better aligned,just as INRIA annotations are better aligned than Caltech.Detailed IoU curves for multiple detectors are provided in the supplementary material.Section4.1describes the RotatedFilters-New10×entry.4.Improving the state of the artIn this section we leverage the insights of the analysis, to improve localization and background-versus-foreground discrimination of our baseline detector.DetectorTrainingdataMedianIoU OMedianIoU N Roerei[2]INRIA0.760.84RotatedFilters Orig.10×0.800.77RotatedFilters New10×0.760.85 Table3:Median IoU of true positives for detectors trained on different data,evaluated on original and new Caltech test.Models trained on INRIA align well with our new an-notations,confirming that they are more precise than previ-ous ones.Curves for other detectors in the supplement.Detector Anno.variant MR O−2MR N−2ACFOriginal36.9040.97Pruned36.4135.62New41.2934.33 RotatedFiltersOriginal28.6333.03Pruned23.8725.91New31.6525.74 Table4:Effects of different training annotations on detec-tion quality on validation set(1×training set).Italic num-bers have matching training and test sets.Both detectors im-prove on the original annotations,when using the“pruned”variant(see§4.1).4.1.Impact of training annotationsWith new annotations at hand we want to understand what is the impact of annotation quality on detection qual-ity.We will train ACF[5]and RotatedFilters mod-els(introduced in section2.2)using different training sets and evaluate on both original and new annotations(i.e. MR O−2,MR O−4and MR N−2,MR N−4).Note that both detect-ors are trained via boosting and thus inherently sensitive to annotation noise.Pruning benefits Table4shows results when training with original,new and pruned annotations(using a5/6+1/6 training and validation split of the full training set).As ex-pected,models trained on original/new and tested on ori-ginal/new perform better than training and testing on differ-ent annotations.To understand better what the new annota-tions bring to the table,we build a hybrid set of annotations. Pruned annotations is a mid-point that allows to decouple the effects of removing errors and improving alignment. Pruned annotations are generated by matching new and ori-ginal annotations(IoU≥0.5),marking as ignore region any original annotation absent in the new ones,and adding any new annotation absent in the original ones.From original to pruned annotations the main change is re-moving annotation errors,from pruned to new,the main change is better alignment.From table4both ACF and RotatedFilters benefit from removing annotation er-rors,even in MR O−2.This indicates that our new training setFigure 8:Examples of automatically aligned ground truth annotations.Left/right →before/after alignment.1×data 10×data aligned withMR O −2(MR O −4)MR N −2(MR N−4)Orig.Ø19.20(34.28)17.22(31.65)Orig.Orig.10×19.16(32.28)15.94(29.33)Orig.New 1/2×16.97(28.01)14.54(25.06)NewNew 1×16.77(29.76)12.96(22.20)Table 5:Detection quality of RotatedFilters on test set when using different aligned training sets.All mod-els trained with Caltech 10×,composed with different 1×+9×combinations.is better sanitized than the original one.We see in MR N −2that the stronger detector benefits more from better data,and that the largest gain in detection qual-ity comes from removing annotation errors.Alignment benefits The detectors from the ICF family benefit from training with increased training data [16,23],using 10×data is better than 1×(see section 2.1).To lever-age the 9×remaining data using the new 1×annotations we train a model over the new annotations and use this model to re-align the original annotations over the 9×portion.Be-cause the new annotations are better aligned,we expect this model to be able to recover slight position and scale errors in the original annotations.Figure 8shows example results of this process.See supplementary material for details.Table 5reports results using the automatic alignment pro-cess,and a few degraded cases:using the original 10×,self-aligning the original 10×using a model trained over original 10×,and aligning the original 10×using only a fraction of the new annotations (without replacing the 1×portion).The results indicate that using a detector model to improve overall data alignment is indeed effective,and that better aligned training data leads to better detection quality (both in MR O and MR N ).This is in line with the analysis of section 3.2.Already using a model trained on 1/2of the new annotations for alignment,leads to a stronger model than obtained when using original annotations.We name the RotatedFilters model trained using the new annotations and the aligned 9×data,Rotated-Filters-New10×.This model also reaches high me-dian true positives IoU in table 3,indicating that indeed it obtains more precise detections at test time.Conclusion Using high quality annotations for training improves the overall detection quality,thanks both to im-proved alignment and to reduced annotation errors.4.2.Convnets for pedestrian detectionThe results of section 3.2indicate that there is room for improvement by focusing on the core background versus foreground discrimination task (the “classification part of object detection”).Recent work [15,20]showed compet-itive performance with convolutional neural networks (con-vnets)for pedestrian detection.We include convnets into our analysis,and explore to what extent performance is driven by the quality of the detection proposals.AlexNet and VGG We consider two convnets.1)The AlexNet from [15],and 2)The VGG16model from [12].Both are pre-trained on ImageNet and fine-tuned over Cal-tech 10×(original annotations)using SquaresChnFtrs proposals.Both networks are based on open source,and both are instances of the R-CNN framework [13].Albeit their training/test time architectures are slightly different (R-CNN versus Fast R-CNN),we expect the result differ-ences to be dominated by their respective discriminative power (VGG16improves 8pp in mAP over AlexNet in the Pascal detection task [13]).Table 6shows that as we improve the quality of the detection proposals,AlexNet fails to provide a consistent gain,eventually worsening the results of our ICF detect-ors (similar observation done in [15]).Similarly VGG provides large gains for weaker proposals,but as the pro-posals improve,the gain from the convnet re-scoring even-tually stalls.After closer inspection of the resulting curves (see sup-plementary material),we notice that both AlexNet and VGG push background instances to lower scores,and at the same time generate a large number of high scoring false positives.The ICF detectors are able to provide high recall proposals,where false positives around the objects have low scores (see [15,supp.material,fig.9]),however convnets have difficulties giving low scores to these windows sur-rounding the true positives.In other words,despite their fine-tuning,the convnet score maps are “blurrier”than the proposal ones.We hypothesise this is an intrinsic limita-tion of the AlexNet and VGG architectures,due to their in-ternal feature pooling.Obtaining “peakier”responses from a convnet most likely will require using rather different ar-chitectures,possibly more similar to the ones used for se-mantic labelling or boundaries estimation tasks,which re-quire pixel-accurate output.Fortunately,we can compensate for the lack of spatial resolution in the convnet scoring by using bounding box regression.Adding bounding regression over VGG,and ap-plying a second round of non-maximum suppression (first NMS on the proposals,second on the regressed boxes),has。

Honda Sensing ®A driver support system which employs the use of two distinctly different kinds of sensors, a radar sensor located in the lower bumper and a front sensor camera mounted to the interior side of the windshield, behind the rearview mirror.These are the components of Honda Sensing ®:Adaptive Cruise Control with Low Speed Follow (ACC with Low Speed Follow)*1:Helps maintain a constant vehicle speed and a set following-interval behind a vehicle detected ahead of yours and, if the detected vehicle comes to a stop, can decelerate and stop your vehicle, without you having to keep your foot on the brake or the accelerator.Adaptive Cruise Control (ACC)*1: Helps maintain a constant vehicle speed and a set following interval behind a vehicle detected ahead of yours, without you having to keep your foot on the brake or the accelerator.Lane Keeping Assist System (LKAS): Provides steering input to help keep the vehicle in the middle of a detected lane and provides tactile and visual alerts if the vehicle is detected drifting out of its lane.Road Departure Mitigation (RDM) System : Alerts and helps to assist you when the system detects a possibility of your vehicle unintentionally crossing over detected lane markings and/or leaving the roadway altogether.Collision Mitigation Braking System ™ (CMBS ™): Can assist you when there is a possibility of your vehicle colliding with a vehicle or a pedestrian detected in front of yours. The CMBS ™ is designed to alert you when a potential collision is determined, as well as to reduce your vehicle speed to help minimize collision severity when a collision is deemed unavoidable.n "Some Driver Systems Cannot Operate" Information MessageHonda Sensing ®is deactivated and this message appears when:Anything covers the radar sensor cover or the area around the front sensor camera preventing detection of a vehicle in front. May appear when driving in bad weather (rain, snow, fog, etc.).•Stop your vehicle in a safe place and clear the area using a soft cloth.*1 - If equipped DRIVING•Have your vehicle checked by a dealer if the message does not disappear even after you clean the area.Adaptive Cruise Control (ACC) *1with Low Speed Follow *1Helps maintain a constant vehicle speed and a set following interval behind a vehicle detected ahead of yours . When the vehicle ahead changes speed, ACC senses the change and accelerates or decelerates to maintain a set interval.and, if the detected vehicle comes to a stop, can decelerate and stop your vehicle, without you having to keep your foot on the brake or the accelerator.When ACC with Low Speed Follow slows your vehicle by applying the brakes,your vehicle's brake lights will illuminate.*1 - If equipped DRIVINGn Activating and Setting the Vehicle Speed1.Press the MAIN button. The ACCindicator appears in the driverinformation interface.2.Accelerate to the desired speed(above 25 mph/40 km/h). Take your foot off the pedal and press theSET/- button to set the speed.DRIVINGn Adjusting the Vehicle SpeedPress the RES/+ button to increasespeed or the SET/- button to decreasespeed. Each time you press the button, the vehicle speed is increased ordecreased by about 1 mph (1.6 km/h). If you keep the button pressed, thevehicle speed increases or decreasesby 5 mph or 5 km/h until you release it.n Adjusting the Vehicle DistancePress the Interval button to change the following interval. Each time you pressthe button, the setting cycles throughextra long, long, middle, and short.n During OperationIf a vehicle is detected ahead of youwhen ACC is turned on, the systemmaintains, accelerates, or deceleratesyour vehicle’s set speed to keep thevehicle’s set following interval from the vehicle ahead.If a vehicle detected ahead of youslows down abruptly, or if another DRIVINGvehicle cuts in front of you, a beep sounds and BRAKE appears on the driver information interface to alert you .ACC has limited braking capability.When your vehicle speed drops below 22 mph (35 km/h), ACC will automatically cancel and no longer will apply your vehicle’s brakes.n Canceling ACC You can press the CANCEL button,MAIN button or the brake pedal. The ACC with Low Speed Follow indicator goes off.Certain conditions may cause ACC to cancel automatically. When this happens, appears on the driver information interface.Improper use of ACC can lead to a crash.Use ACC only when traveling on open highways in good weather.n Switching to Standard Cruise ControlPress and hold the Interval button for one second.Cruise Mode Selected appears in the driver information interface for two seconds, and then the mode switches to Cruise. Press and hold the intervalbutton again to switch back to ACC .ACC Mode Selected appears on the driver information interface display for two seconds.Lane Keeping Assist System (LKAS)Provides steering input to help keep the vehicle in the middle of a detected lane and provides audible and visual alerts if the vehicle is detected drifting out of its lane while driving between 45–90 mph (72–145 km/h).DRIVINGn Turning the System On or Off1.Press the MAIN button. LKAS appears in the driver information interface.2.Press the LKAS button. Lane outlines appear in the driver information interface. Dotted lane lines turn solid when the system activates.3.Press the MAIN button or the LKASbutton to turn the system off.DRIVINGn Important Safety ReminderLKAS is for your convenience only. It is not a substitute for your vehicle control. The system does not work if you take your hands off the steering wheel or fail to steer the vehicle.Do not place an object on the top of the instrument panel. It may reflect onto the windshield and prevent the system from detecting lane lines properly.Road Departure Mitigation (RDM)Alerts and helps to assist you if the system determines a possibility of your vehicle unintentionally crossing over detected lane markings and/or leaving the roadway altogether while driving between 45-90 mph (72-145km/h).n Turning the System On or OffPress the RDM button to turn the system on or off. A green indicator appears on the button when the system is on.n Changing Settings1.From the Home screen, select Settings.2.Select Vehicle Settings.3.Select Driver Assist System Setup.DRIVING4.Select Road Departure Mitigation Setting.n Important Safety ReminderThe RDM system has limitations. Over-reliance on it may result in a collision. It is always your responsibility to keep your vehicle within the driving lane.Collision Mitigation Braking System ™ (CMBS ™)Can assist you when there is a possibility of your vehicle colliding with a vehicle or a pedestrian detected in front of yours. The CMBS ™ is designed to alert you when a potential collision is determined, as well as to reduce your vehicle speed to help minimize collision severity when a collision is deemed unavoidable.n Alert Stages The system has three alert stages for a possible collision. Depending on the circumstances or CMBS ™ settings, CMBS ™ may not go through all of the stages before initiating the last stage.Stage 1:Visual and audible warning.Stage 2:Visual and audible warning, light brake application.Stage 3:Visual and audible warning, strong brake application.n Changing Settings1.From the Home screen, select Settings..2.Select Vehicle Settings.3.Select Driver Assist System Setup.4.Select Forward Collision Warning Distance.nTurning the System On or Off DRIVINGPress and hold the CMBS ™OFF button.A beep sounds and a message appears in the Multi-Information DisplayDriver Information Interface. The CMBS ™indicator appears when the system is off.n Important Safety ReminderCMBS is designed to reduce the severity of an unavoidable collision. It does not prevent collisions nor stop the vehicle automatically. It is still your responsibility to operate the brake pedal and steering wheel appropriatelyaccording to the driving conditions.DRIVING。

Blind Spot Information System (BLIS) • Helps avoid collisions with unsighted vehicles • Alerts the driver using a light in the door mirror if sensors detect a vehicle in the driver’s blind spotCross Traffic Alert• Warns the driver of oncoming vehicles and bicycles when reversing out of a parking bay • Uses radar sensorsto detect oncoming hazards beyond the driver’s viewSide Wind Assist• Helps the vehicle main-tain its intended path when there is a strong gust of wind from the side • Applies the brakes on one side if needed to help the driver maintain controlLane-Keeping System• Lane-Keeping Alert vibrates the steering wheel andvisually warns the driver if the van drifts out of lane• Lane-Keeping Aid adds steering torque, to helpkeep the vehicle within the marked laneIntelligent Adaptive Cruise Control• Uses information from Traffic Sign Recognition toautomatically set vehicle speed• Radar technology can automatically adjust vehiclespeed to maintain a preset gap from the vehicle in frontPre-Collision Assist with Pedestrian Detection• Uses cameras and radar sensors to warnof imminent collisions with hazards ahead• The system can detect pedestrians at nightwhen illuminated by the headlightsSeat BeltReminder• Detects if the driver’sseat belt is fastenedwhile the vehicle ismoving• Alerts the driver withaudio and visualwarnings if the seatbelt is not buckledDriver Alert• Alerts the driver withaudio and visualwarnings if it detectsreduced alertness levels• Uses a front-facingcamera to detect lanemarkings and vehicleposition on the roadSpeed Limiter• Enables drivers to set amaximum speed from20 km/h to 110 km/h• If the driver lifts off theaccelerator, the van willgradually slow down as innormal driving。

Individual optionsTechnical dataStandard optionsVehicle pictures Vehicle informationS-GO 801EP o r s c h e T a y c a n4C r o s s T u r i s m oI m p o r t a n t I n f o r m a t i o nA l t h o u g h t h i s i m a g e i s i n t e n d e d t o r e f l e c t y o u r a c t u a l v e h i c l e c o n f i g u r a t i o n,t h e r e m a y b e s o m e v a r i a t i o n b e t w e e n t h i s p i c t u r e a n d t h e a c t u a l v e h i c l e.S o m e i t e m s s h o w n a r e E u r o p e a n s p e c i f i c a t i o n s.T e c h n i c a l d a t aS i n g l e -S p e e d T r a n s m i s s i o n o n t h e F r o n t A x l e , 2-S p e e d T r a n s m i s s i o n o n t h e R e a r A x l eP o w e r u n i tP o w e r u p t o (k W )280 kW P o w e r u p t o (P S )380 PS P o w e r u p t o (H P ) (o n l y f o r N A R )375 hpO v e r b o o s t P o w e r w i t h L a u n c h C o n t r o l u p t o (k W )350 kW O v e r b o o s t P o w e r w i t h L a u n c h C o n t r o l u p t o (P S )476 PS O v e r b o o s t P o w e r w i t h L a u n c h C o n t r o l u p t o (H P ) (o n l y f o r N A R )469 hpM a x . t o r q u e w i t h L a u n c h C o n t r o l500 NmC o n s u m p t i o n /E m i s s i o n sE l e c t r i c i t y c o n s u m p t i o n c o m b i n e d28.1 kWh/100 kmC o n s u m p t i o n /E m i s s i o n s W L T PE l e c t r i c a l c o n s u m p t i o n l o w (W L T P )21.9 - 19.1 kWh/100 km E l e c t r i c a l c o n s u m p t i o n m e d i u m (W L T P )21.4 - 18.4 kWh/100 km E l e c t r i c a l c o n s u m p t i o n h i g h (W L T P )22.4 - 18.9 kWh/100 km E l e c t r i c a l c o n s u m p t i o n e x t r a -h i g h (W L T P )28.4 - 24.0 kWh/100 km E l e c t r i c a l c o n s u m p t i o n c o m b i n e d (W L T P )26.4 - 22.4 kWh/100 km E l e c t r i c a l c o n s u m p t i o n C i t y (W L T P )21.6 - 18.7 kWh/100 km C O 2-e m i s s i o n c o m b i n e d (W L T P )0 - 0 g/kmR a n g eR a n g e c o m b i n e d (W L T P )389 - 456 km R a n g e C i t y (W L T P )463 - 541 km L o n g -d i s t a n c e r a n g e360 kmC h a r g i n gG r o s s b a t t e r y c a p a c i t y 93.4 kWh N e t b a t t e r y c a p a c i t y83.7 kWh M a x i m u m c h a r g i n g p o w e r w i t h d i r e c t c u r r e n t (D C )270 kW C h a r g i n g t i m e f o r a l t e r n a t i n g c u r r e n t (A C ) w i t h 9.6k W (0 t o u p t o 100%)10.5 h C h a r g i n g t i m e f o r a l t e r n a t i n g c u r r e n t (A C ) w i t h 11k W (0 t o u p t o 100%)9.0 h C h a r g i n g t i m e f o r a l t e r n a t i n g c u r r e n t (A C ) w i t h 22k W (0 t o u p t o 100%)5.0 h C h a r g i n g t i m e f o r d i r e c t c u r r e n t (D C ) w i t h 50k W f o r u p t o 100k m (W L T P )28.5 min C h a r g i n g t i m e f o r d i r e c t c u r r e n t (D C ) w i t h 50k W (5 t o u p t o 80%)93.0 minT e c h n i c a l d a t a (c o n t i n u e d )S i n g l e -S p e e d T r a n s m i s s i o n o n t h e F r o n t A x l e , 2-S p e e d T r a n s m i s s i o n o n t h e R e a r A x l eC h a r g i n g t i m e f o r d i r e c t c u r r e n t (D C ) w i t h m a x i m u m c h a r g i n g p o w e r f o r u p t o 100k m (W L T P )5.25 min C h a r g i n g t i m e f o r d i r e c t c u r r e n t (D C ) w i t h m a x i m u m c h a r g i n g p o w e r (5 t o u p t o 80%)22.5 minB o d yL e n g t h4,974 mm W i d t h1,967 mm W i d t h (w i t h m i r r o r s )2,144 mm H e i g h t 1,409 mm W h e e l b a s e2,904 mm F r o n t t r a c k 1,718 mm R e a r t r a c k1,698 mm U n l a d e n w e i g h t (D I N )2,245 kg U n l a d e n w e i g h t (E U )2,320 kg P e r m i s s i b l e g r o s s w e i g h t 2,885 kg M a x i m u m l o a d640 kg M a x i m u m p e r m i s s i b l e r o o f l o a d w i t h P o r s c h e r o o f t r a n s p o r t s y s t e m75 kgC a p a c i t i e sL u g g a g e c o m p a r t m e n t v o l u m e , f r o n t84 lO p e n l u g g a g e c o m p a r t m e n t v o l u m e (u p t o t h e u p p e r e d g e o f t h e r e a r s e a t s )446 l L a r g e s t l u g g a g e c o m p a r t m e n t v o l u m e (b e h i n d f r o n t s e a t s ,u p t o r o o f )1,212 lP e r f o r m a n c eT o p s p e e d220 km/h A c c e l e r a t i o n 0 - 60 m p h w i t h L a u n c h C o n t r o l4.8 s A c c e l e r a t i o n 0 - 100 k m /h w i t h L a u n c h C o n t r o l5.1 sA c c e l e r a t i o n 0 - 160 k m /h w i t h L a u n c h C o n t r o l 10.1 s A c c e l e r a t i o n 0 - 200 k m /h w i t h L a u n c h C o n t r o l 15.6 s A c c e l e r a t i o n (80-120k m /h ) (50-75 m p h )2.6 sS t a n d a r d o p t i o n sP o w e r u n i t• Porsche E-Performance Powertrain with a Permanent Magnet Synchronous Motor on the Front and Rear Axle • Single-Speed Transmission on the Front Axle• Performance Battery Plus• 2-Speed Transmission on the Rear Axle• Porsche Traction Management (PTM)• Porsche Recuperation Management (PRM)• Sport Mode for the Activation of dynamic Performance Settings including Launch Control• Range Mode for the Activation of efficiency-oriented Settings• Gravel Mode for the Activation of Settings with increased Bad Road CapabilitiesC h a s s i s• Aluminium Double Wishbone Front Axle• Aluminium Multi-Link Rear Axle• Vehicle Stability System Porsche Stability Management (PSM) with ABS and extended Brake Functions • Integrated Porsche 4D Chassis Control• Adaptive Air Suspension including Porsche Active Suspension Management (PASM) and Smart Lift• Increased Ground Clearance in Comparison to Taycan Limousine (+20 mm)• Power SteeringW h e e l s• 19-Inch Taycan Aero Wheels• Wheel Centres with monochrome Porsche Crest• Tyre Pressure Monitoring (TPM)B r a k e s• 6-Piston Aluminium Monobloc fixed Brake Calipers at Front• 4-Piston Aluminium Monobloc fixed Brake Calipers at Rear• Brake Discs internally vented with 360 mm Diameter at Front and 358 mm Diameter at Rear• Brake Calipers painted in Black• Anti-Lock Brake System (ABS)• Electric Parking Brake• Brake Pad Wear Indicator• Auto Hold Function• Multi-Collision BrakeB o d y• Fully galvanised Steel-Aluminium-Hybrid lightweight Bodyshell• Bonnet, Tailgate, Doors, Side Sections and front Wings in Aluminium• Roof in Aluminium, contoured Design (with dynamic Recess Profile)S t a n d a r d o p t i o n s(c o n t i n u e d)• Full-surface aerodynamic Underbody Panelling• Upper Valance with vertical Air Intakes (Air Curtain)• Auto-deploying Door Handles• Side Window Trims in Black• Door Sill Guards in Black• Exterior Mirror Lower Trims including Mirror Base in Black• ‘PORSCHE' Logo in Glass Look integrated into Light Strip• Model Designation on Tailgate in Silver• Wheel Arch Cover in Black• Porsche Active Aerodynamics (PAA) with active Air Intake Flaps• Roof Spoiler painted in Black (high-gloss)• Cross Turismo specific Lower Valance with Inlay painted in Brilliant Silver• Cross Turismo specific Sideskirts in Black with Inlays painted in Brilliant Silver• Cross Turismo specific Rear Diffusor in Louvered Design with Inlay painted in Brilliant SilverL i g h t s a n d v i s i o n• LED headlights• Four-Point LED Daytime Running Lights• Automatic Headlight Activation including ‘Welcome Home’ lighting• Light Strip• Third Brake Light• LED-Innenraumbeleuchtungskonzept: Abschaltverzögerung, Innenleuchte (Dachkonsole) vorne mit Lesespots rechts und links, Auflicht in der Dachkonsole, beleuchteter Make-up-Spiegel in den Sonnenblenden (Fahrer- undBeifahrerseite), Leseleuchten hinten links und rechts, Auflicht in den Leseleuchten, Fußraumleuchte vorne und hinten, Gepäckraumleuchten vorne und hinten, Handschuhkastenleuchte, Türfachbeleuchtung• Automatically dimming Interieur and Exterior Mirrors• Illuminated Vanity Mirror for Driver and Front Passenger• Electrically adjustable and heatable Exterior Mirrors, aspherical on Driver’s Side• Front Wiper System including Rain Sensor and Washer Jets• Rear Wiper including Washer Jet• Heated Rear Screen with "Auto-Off" FunctionA i r c o n d i t i o n i n g a n d g l a z i n g• Advanced Climate Control (2 Zone) with separate Temperature Settings and Air Volume Control for Driver and Front Passenger, automatic Air-Recirculation Mode including Air Quality Sensor as well as comfortable Control of the Airflow via PCM• Parking Pre-Climatisation including Pre-Conditioning of the Battery• Thermally insulated Glass all round• Particle/pollen filter with active carbon filter, traps particles, pollen and odours and thoroughly filters fine dust out of the outside airS t a n d a r d o p t i o n s(c o n t i n u e d)S e a t s• Comfort seats in front (8-way, electric) with electric adjustment of seat height, squab and backrest angle and Fore/Aft position• Integrated Headrests front• Rear Seats with 2 Seats in Single-Seat Look, fold-out Centre Armrest and split-folding Backrests (60:40)S a f e t y a n d s e c u r i t y• Active Bonnet System Note: only in markets with legal requirements• 4 Doors with integrated Side Impact Protection• Bumpers comprising high-strength Cross Members and two Deformation Elements each with two threaded Fixture Points for Towing Eye contained in on-board Tool Kit• Full-size Airbags for Driver and Front Passenger• Knee Airbags for Driver and Front Passenger• Side Airbags in front• Curtain Airbags along entire Roof Frame and Side Windows from the A-Pillar to the C-Pillar• Rollover Detection for Activation of Curtain Airbags and Seat Belt Pretensioners• Three-Point automatic Seat Belts with Pretensioners (front and outer rear Seats) and Force Limiters• Manual Adjustment of Seat Belt Height for Driver and Front Passenger Seats• Seat Belt Warning System for Driver, Front Passenger and Rear Seat System• Immobiliser with Remote Central Locking, Alarm System with radar-based Interior Surveillance• ISOFIX Mounting System for Child Seats on outer Rear SeatsA s s i s t a n c e s y s t e m s• Lane Keeping Assist including Traffic Sign Recognition• Cruise Control including adaptive Speed Limiter• Warn and Brake Assist incl. Pedestrian protection Detects the area ahead of the vehicle. Within the system limitations, an impending frontal collision with other vehicles, pedestrians or cyclists can be detected both in the urban and extra-urban speed range. The system warns the driver visually, acoustically and if necessary through a braking jolt. Where required, the system can support the driver's braking or initiate partial or full deceleration in order to reduce the collision speed or prevent the collision in some circumstances.• ParkAssist (front and rear) with visual and audible Warning• Keyless Drive• Driver Personalisation for Ergonomic, Comfort, Infotainment and Lighting Functions as well as Assistance and Display Systems Note: Country-specific availability• Distance warning If the system detects a safety hazard due to following too close, the system can warn the driver in a vehicle speed range from approx. 65 – 250 km/h (40 – 156 mph) by displaying the symbol on the instrument clusterI n s t r u m e n t s• 16.8-Inch Curved Display - contains up to five different and freely configurable views, depending on the equipment -including external touchscreen control panels for controlling the light and chassis functions• Centre Console with Direct Touch Control - climate settings - opening and closing of the charge port doors - battery level indicator - handwriting panelS t a n d a r d o p t i o n s(c o n t i n u e d)I n t e r i o r• Partial Leather Interior• 'Taycan' Badge in the Centre Console• Accent Package Black• Storage Package Additional storage compartments in vehicle interior: - storage tray below the ascending centre console in front - storage tray on the middle tunnel in rear - net and bag hook in rear luggage compartment• Fabric roof lining• Multifunction Sports Steering Wheel Leather• Centre Console Armrest front with integrated Storage Compartment• Floor Mats• Sun Visors for Driver and Front PassengerA u d i o a n d c o m m u n i c a t i o n• Porsche Communication Management (PCM) including Online Navigation¹ - high-resolution 10.9-Inch touchscreen display in full HD resolution - multi-touch gesture control: for example, you can control the size of the map view with two fingers using the PCM touchscreen display or Direct Touch Control in the handwriting input field in the centre console -mobile phone preparation with Bluetooth® interface for telephone and music - two USB-C connectivity and charge ports in the storage compartment in the centre console, for example for connecting various iPod® and iPhone®models², as well as two USB-C charge ports in the rear - radio with RDS twin-tuner and Diversity for optimum reception - control of vehicle and comfort functions such as charging timers and climate settings - central display of notifications from the vehicle and connected external devices - voice control with natural speech interaction, activation via “Hey Porsche” and multimodal map operation Online navigation¹ with: - maps for most European countries - 3D map display and 3D navigation map supporting city³ and terrain models with satellite image overlay - dynamic route calculation with online real-time traffic and route monitor for a clear overview of charging stops and traffic conditions Note: ¹ requires Porsche Connect ² for information on compatibility with the latest iPod® and iPhone® models, please contact your Porsche Centre ³ not available in all cities• LTE Communication Module with embedded SIM Card, Internet Access and Smartphone Compartment including Inductive Charging (Qi Standard)• Porsche Connect with Apple® CarPlay - online navigation (see Porsche Communication Management) - musicstreaming and online radio - Remote Services - E-mobility services including charge management, control of vehicle parking pre-climatisation or range management - a wide range of other Porsche Connect Services Note: Porsche Connect includes a free subscription period of 36 months. The full range of Porsche Connect services or individual services thereof may not be available in some countries. An integrated LTE-enabled SIM card with data allowance for use of selected Porsche Connect services will be included in some countries. For use of the WiFi hotspot via the integrated, LTE-enabled SIM card, in some of these countries a data package is available to purchase from thePorsche Connect Store. For further information on free subscription periods, follow-on costs and availability ofindividual services in your country, please visit /connect or consult your Porsche Centre.• 2 USB-C Connectivity and Charge Ports in the Storage Compartment in the Centre Console• 2 USB-C Charge Ports in the Rear• Sound Package Plus with 10 Speakers and a total Output of 150 Watts• Digital Radio Note: Standard EU 28S t a n d a r d o p t i o n s(c o n t i n u e d)L u g g a g e c o m p a r t m e n t• Luggage Compartment front and rear• Automatic Tailgate• Tailgate Button• Storage Compartments - glove compartment - storage compartment in the front centre console - storage tray below the ascending centre console in front - storage tray between the rear seats - storage tray on the middle tunnel in rear -storage compartments in the doors front and rear - storage compartments in the sides of the rear luggage compartment and luggage compartment recess - net and two fastener straps in rear luggage compartment - bag hooks in rear luggage compartment• 12 V Electrical Socket in Storage Compartment in the Centre Console• 12 V Electrical Socket in Luggage Compartment rear• Two integrated Cupholders front and rear• Clothes Hook at B-Pillars on Driver's and Passenger's Side• Functional Luggage Compartment Cover, foldableC o l o u r s• Solid Paint Exterior Colours - White (0Q) - Black (A1)E-P e r f o r m a n c e• Charge Port on Driver and Front Passenger Side• On-Board AC-Charger with 11 kW for Alternating Current (AC)• On-Board DC-Charger with up to 150 kW for Direct Current (DC) at public Charging Stations with a Voltage of 400 V • Charging with Direct Current (DC) at public Charging Stations with a Voltage of 800 V• Mobile Charger Plus (11 kW) for charging at household and industrial electrical outlets. Compatible with the Home Energy Manager. 4.5 m cable• Supply Cable for Domestic Electrical Socket• Supply Cable for Red Industrial Electrical Outlet (400 V, 32 A, 5 Pin)I n d i v i d u a l o p t i o n sO r d e r n o.M o d e l y e a r V e h i c l eY1BBD12021Taycan 4 Cross TurismoI n d i v i d u a l i s a t i o nC a t e g o r y O r d e r n o.I n d i v i d u a l e q u i p m e n tExterior Colour R7Neptune BlueInterior Colour QA Two-Tone Leather-Free Interior,Black/Slate GreyEquipment Packages2JZ Offroad Design Package incl. Inlayspainted in Black (high-gloss) Exterior3S2Roof Rails in Black Aluminium6XV Electric folding Exterior Mirrors6FJ Exterior Mirror Lower Trims painted inExterior Colour including Mirror Basepainted in Black (high-gloss) PorscheExclusive ManufakturQJ4Side Window Trims in Black (high-gloss)6JA Door Release Levers painted in Black(high-gloss) Porsche Exclusive ManufakturNG1Preliminary Setup for Rear Bike Carrier Drive train / Chassis G1X Single-Speed Transmission on the FrontAxle, 2-Speed Transmission on the RearAxleGM3Porsche Electric Sport SoundGH3Porsche Torque Vectoring Plus (PTVPlus)8LC Sport Chrono Package includingCompass Display on Dashboard1LZ Porsche Surface Coated Brake (PSCB),Brake Calipers with White Finish0N5Rear-Axle Steering including PowerSteering PlusWheels53Y20-Inch Taycan Turbo Aero DesignWheelsWheel Accessories1G8Tyre Sealing Compound and Electric AirCompressorLights and vision4L6Automatically Dimming Interieur andExterior Mirrors3FG Panoramic Roof, fixedVW6Thermally and Noise insulated Glassincluding Privacy GlassComfort and assistance systems KA6ParkAssist including Surround ViewP49Adaptive Cruise Control4F2Comfort AccessInterior KH5Advanced Climate Control (4-Zone)I n d i v i d u a l i s a t i o n(c o n t i n u e d)C a t e g o r y O r d e r n o.I n d i v i d u a l e q u i p m e n t2V4Ioniser3L4Driver Memory PackageQQ1Ambient LightingQ1G Comfort Seats in Front (8-Way, electric)4A3Seat Heating (front)4X4Side Airbags in Rear CompartmentGT5Accent Package DarksilverInterior Race-Tex6NC Roof Lining Race-TexInterior Carbon5MH Carbon matt Interior Package2PS Steering Wheel Trim Carbon matt andSteering Wheel Rim Race-Tex includingSteering Wheel Heating (i.c.w. SportChrono Package and Leather-freeInterior) Porsche Exclusive Manufaktur7M8Door Sill Guards Carbon matt, illuminatedPorsche Exclusive ManufakturAudio / Comm.JH1Passenger DisplayE-Performance2W9Electric Charging CoverKB4On-Board AC-Charger with 22 kW9M3Heat PumpQW5Porsche Intelligent Range ManagerNW2Mobile Charger ConnectEH2Cable Connection between Control Unitand Vehicle: 7.5m76H Charging Cable (Mode 3)Y o u r P o r s c h e C o d e /PM6YI6M5I m p o r t a n t i n f o r m a t i o nThe models illustrated show equipment for the Federal Republic of Germany. For example they also include special equipment which is not supplied as standard and is only obtainable for an additional charge. Not all models are available in every country as there may be regulations and orders which are country-specific. Please obtain information about the models available through your Porsche dealer or importer. We reserve the right to change design, equipment and delivery specifications as well as vary colours.。

DRIVINGCollision Mitigation Braking System™ (CMBS™)*1Can assist you when there is a possibility of your vehicle colliding with a vehicle or a pedestrian detected in front of yours.The system can give you visual, audible,and tactile alerts when a potential collisionis determined, and reduce your vehiclespeed to help minimize collision severitywhen a collision is deemed unavoidable.When a potential collision with a detectedoncoming vehicle is determined, thesystem also alerts you with rapid vibrationson the steering wheel.Steering vibrationsn Alert StagesThe system has three alert stages for a possible collision. Depending on the circumstances or CMBS settings, CMBS may not go through all of thestages before initiating the last stage.Stage 1: Visual and audible warning , steering wheel vibrationsStage 2: Visual and audible warning, light brake applicationStage 3: Visual and audible warning, strong brake applicationn Changing Settings1.From the HOME screen, select Settings.2.Select Vehicle. The vehicle must be in Park (P).*1 - If equipped3.Select Forward Collision Warning Distance.4.Select Long, Normal, or Short.5.Press BACK to exit the menu.n Turning CMBS On or OffPress and hold the CMBS OFF button. Abeep sounds and a message appears inthe MID. The CMBS indicator appearswhen the system is off.n Important Safety ReminderCMBS is designed to reduce the severity of an unavoidable collision. It does not prevent collisions nor stop the vehicle automatically. It is still yourresponsibility to operate the brake pedal and steering wheel appropriately according to the driving conditions.HondaSensing®HondaSensing® is a driver support systemwhich employs the use of two distinctlydifferent kinds of sensors, a radar sensorlocated in the front grille and a front sensorcamera mounted to the interior side of thewindshield, behind the rear view mirror.These are the components of HondaSensing:Adaptive Cruise Control (ACC)*1: Helpsmaintain a constant vehicle speed and aset following interval behind a vehicledetected ahead of yours, without youhaving to keep your foot on the brake or theaccelerator.Road Departure Mitigation (RDM) System: Alerts and helps to assist you when the system detects a possibility of your vehicle unintentionally crossing over detected lane markings and/or leaving the roadway altogether.Lane Keeping Assist System (LKAS): Provides steering input to help keep the vehicle in the middle of a detected lane and provides tactile and visual alerts if the vehicle is detected drifting out of its lane.Collision Mitigation Braking System™ (CMBS™): Can assist you when there is a possibility of your vehicle colliding with a vehicle or a pedestrian detected in front of yours. The CMBS™ is designed to alert you when a potential collision is*1 - If equippedDRIVINGdetermined, as well as to reduce your vehicle speed to help minimize collision severity when a collision is deemed unavoidable.。

The Evolution of Smart Cars and TheirImpact on the Future of TransportationIn the not-so-distant future, the landscape of transportation will be transformed by the advent of smart cars. These innovative vehicles, equipped with cutting-edge technology, are revolutionizing the way we travel, enhancing safety, efficiency, and even the overall driving experience.At the heart of the smart car revolution is advanced artificial intelligence (AI). These vehicles are capable of understanding and responding to their surroundings in real-time, thanks to a combination of sensors, cameras, and radar systems. They can navigate roads with precision, avoid obstacles, and even predict the behavior of other drivers and pedestrians.One of the most significant benefits of smart cars is their ability to reduce accidents. By continuously monitoring the road and other vehicles, they can identify potential hazards and take evasive action before acollision occurs. This not only saves lives but also reduces the economic burden of accidents on society.Moreover, smart cars are optimizing traffic flow and reducing congestion. Through intelligent routing and communication with other vehicles and infrastructure, they can create smoother, more efficient traffic patterns. This not only saves time for individual drivers but also reduces the environmental impact of transportation by cutting down on emissions and fuel consumption.The impact of smart cars extends beyond the roads. They are also changing the way we think about car ownership and transportation services. With the rise of autonomous vehicles, the need for personal car ownership may decrease as shared, on-demand transportation becomes more prevalent. This could lead to significant changes in urban planning and infrastructure, with less space dedicated to parking and more focus on pedestrian-friendly environments.However, the emergence of smart cars also poses challenges and ethical questions. Issues such as data privacy, cybersecurity, and the potential displacement of jobs in the transportation sector need to be carefully addressed. Moreover, the transition to autonomous vehiclesrequires widespread public acceptance and trust, which may take time to build.Despite these challenges, the future of transportation looks increasingly bright with the advent of smart cars. As technology continues to evolve and become more affordable, we can expect to see more and more smart cars on the road, revolutionizing the way we travel and connect with each other.**智能汽车的发展与对未来交通的影响**在不远的未来，智能汽车的兴起将彻底改变交通的面貌。

车载毫米波雷达基本成像原理Millimeter wave radar is a key technology in the field of automotive safety and autonomous driving. It operates in the frequency range of 24 GHz to 77 GHz and provides high-resolution images of the vehicle's surroundings. 毫米波雷达是汽车安全和自动驾驶领域的关键技术。

它在24 GHz至77 GHz的频率范围内工作，并提供车辆周围高分辨率的图像。

One basic principle of millimeter wave radar imaging is the use of electromagnetic waves to detect objects and create a detailed picture of the environment. These radar waves are emitted by the radar system and then bounce off objects in the vehicle's vicinity, such as other vehicles, pedestrians, or road obstacles. 毫米波雷达成像的一个基本原理是利用电磁波来探测物体，并创建环境的详细图像。

这些雷达波由雷达系统发射，然后反射在车辆附近的物体上，如其他车辆、行人或道路障碍物。

Another fundamental aspect of millimeter wave radar imaging is its ability to generate 3D images of the surrounding environment. This3D capability allows the radar system to not only detect the presenceof objects but also to accurately measure their distance and speed. In addition, the high resolution of the images enables the radar to distinguish between different types of objects, such as a car versus a pedestrian, and accurately identify their positions relative to the vehicle. 毫米波雷达成像的另一个基本方面是其能够生成周围环境的3D图像。

雷达的用途英语作文Title: The Applications of Radar Technology。

Radar technology, short for Radio Detection and Ranging, has revolutionized various fields since its inception. Its versatile applications span across military, aviation, meteorology, and even everyday life. In this essay, wedelve into the myriad uses of radar technology and its significance in contemporary society.In the realm of military, radar plays a pivotal role in surveillance, reconnaissance, and target tracking. Itsability to detect objects at long ranges, irrespective of weather conditions, gives it a strategic advantage in monitoring airspace and maritime activities. Radar systems such as the AN/SPY-1 employed in naval vessels enable early warning against incoming threats, enhancing defense capabilities.Moreover, radar facilitates navigation and air trafficcontrol systems, ensuring safe and efficient travel in the skies. Ground-based radar installations guide aircraft during takeoff, landing, and en-route phases, minimizing the risk of collisions and aiding in airspace management. Doppler radar, with its capability to measure velocity and precipitation intensity, aids meteorologists in predicting severe weather phenomena like thunderstorms and hurricanes, thereby saving lives and mitigating property damage.In the scientific domain, radar technology finds applications in remote sensing and geological exploration. Synthetic Aperture Radar (SAR) satellites provide high-resolution images of the Earth's surface, enabling monitoring of environmental changes, urban development, and natural disasters. Ground-penetrating radar assists archaeologists in uncovering hidden structures andartifacts buried beneath the earth's surface, unraveling mysteries of the past.Furthermore, radar is instrumental in search and rescue operations conducted in challenging terrains. Radar-equipped aircraft and drones can locate missing persons ordistressed vessels by detecting their electromagnetic signatures, even in remote or obscured locations. This capability proves invaluable in emergency situations, facilitating swift response and saving lives.In the automotive industry, radar technology isintegral to advanced driver assistance systems (ADAS) and autonomous vehicles. Radar sensors, combined with cameras and LiDAR, enable vehicles to perceive their surroundings, detect obstacles, and anticipate potential collisions. Adaptive cruise control, collision avoidance systems, and pedestrian detection mechanisms rely on radar's ability to accurately gauge distances and relative velocities, enhancing road safety and reducing accidents.Moreover, radar plays a crucial role in industrial applications such as manufacturing and agriculture. Through the use of ground-based or drone-mounted radar systems, farmers can monitor crop health, assess soil moisture levels, and optimize irrigation practices, thereby increasing agricultural productivity and sustainability. In manufacturing, radar-based sensors facilitate precisionmeasurement, quality control, and automation, streamlining production processes and improving efficiency.In security and surveillance, radar technology aids in perimeter monitoring, intrusion detection, and border surveillance. Ground surveillance radar systems detect and track unauthorized movements in sensitive areas, alerting security personnel to potential threats. This application is particularly critical in safeguarding national borders, critical infrastructure, and high-security installations.In conclusion, radar technology stands as a testament to human ingenuity and innovation, with its applications permeating various facets of modern society. From safeguarding national security to enhancing transportation safety and enabling scientific exploration, radar continues to redefine the boundaries of possibility. As technology advances further, the potential for radar's utility and impact is boundless, promising a future shaped by its continuous evolution and adaptation to new challenges.。

[英语作文]汽车的创新Title: Innovations in the Automotive IndustryThe automotive industry has always been at the forefront of technological innovation, shaping not only the way we travel but also the broader context of society and the environment. Over the past century, advancements in automobile design, power systems, safety features, and connectivity have revolutionized the way we perceive and experience transportation. This essay will explore some of the most significant innovations in the automotive industry that are paving the way for a smarter, safer, and more sustainable future on the roads.Electric PowertrainsOne of the most impactful innovations in recent years is the shift from traditional internal combustion engines to electric vehicles (EVs). Electric cars offer numerous benefits, such as lower operating costs, reduced emissions, and improved energy efficiency. With advances in battery technology resulting in longer ranges and faster charging times, EVs are becoming increasingly practical and popular for everyday use.Autonomous DrivingThe development of self-driving cars is perhaps the most anticipated innovation in the automotive sector. Through the use of advanced sensors, cameras, radar, and artificial intelligence, these vehicles have the potential to drastically reduce human error caused accidents, improve traffic flow, and enhance mobility for those unable to drive. Although fully autonomous vehicles are not yet mainstream, many current models already incorporate semi-autonomous features such as lane-keeping assistance and adaptive cruise control.Connected CarsThe rise of connected cars, or vehicles equipped with internet access and networking capabilities, opens up a world of possibilities for drivers. These cars can provide real-time traffic updates, remote control features, and integration with smart devices and homes. Additionally, connected cars can communicate with each other and with infrastructure, paving the way for intelligent transportation systems that optimize routes, decrease congestion, and improve safety.Advanced Safety FeaturesSafety has always been a priority in car design, and modern vehicles are equipped with a host of advanced safety features. Systems like Electronic Stability Control (ESC), anti-lock braking systems (ABS), and multiple airbagsare now standard in many new vehicles. More recently, features such as automatic emergency braking, pedestrian detection, and blind-spot monitoring are becoming more widespread, significantly reducing the risk of accidents.Sustainable Materials and ManufacturingThe push towards sustainability has led to the use of lighter, stronger, and more environmentally friendly materials in car production. For example, carbon fiber and aluminum are increasingly used instead of steel to reduce vehicle weight and improve fuel efficiency. Moreover, car manufacturers are adopting cleaner production methods and recycling techniques to minimize the environmental impact of the manufacturing process.ConclusionThe automotive industry's ongoing commitment to innovation ensures that the ways we interact with our vehicles continue to evolve. From electric and self-driving cars to advanced safety features and sustainable practices, these innovations are not only enhancing the driving experience but also contributing to a safer and more sustainable future for all. As technology continues to advance at an unprecedented pace, there is no doubt that the automobile of tomorrow will be smarter, greener, and more integrated into our daily lives than ever before.。

Vol. 36 No. 12Doe. 2020第 36 卷#第 12 期2020年12月信号处理Journal of Signal Processing文章编号：1003-0530(2020)12-2043-09水下声学滑翔机海上目标探测试验与性能评估王超1;2孙芹东1;2张林1;2王文龙W 张小川W(1.海军潜艇学院，山东青岛266199； 2.青岛海洋科学与技术试点国家实验室，山东青岛266237)摘要：针对传统海上目标探测效费比低且受恶劣海况影响严重的问题，亟需构建安全有效、多维立体的海上目 标探测体系。

水下声学滑翔机凭借其隐蔽性能好、环境适应强、成本低等优点，可广泛应用于海上目标探测。

海上目标探测受海洋环境影响很大，需要进行系统、长期、深入研究。

开展水下声学滑翔机海上目标探测试验 并获取对不同海上目标的探测能力，是有效推进该研究的重要手段。

该文重点围绕水下声学滑翔机对不同水面 航船目标探测试验情况，从目标探测距离、目标估计方位精度和-3 dB 波束宽度三个方面进行了性能评估和总 结。

最后，对前期试验存在问题进行了梳理，并对下一步试验关注方向做了展望。

关键词：目标探测；声学滑翔机；探测距离；方位精度中图分类号：TB566 文献标识码：A DOI ： 10. 16798/j. issn. 1003-0530. 2020.12.010引用格式：王超，孙芹东，张林，等.水下声学滑翔机海上目标探测试验与性能评估[J ].信号处理，2020, 36 (12) : 2043-2051. D0I ： 10. 16798/j. issn. 1003-0530.2020.12.010.Reference format : Wang Chao ， Sun Qindong ， Zhang Lin ，et ai. Undeevater Acoustic Glider Tacet Detection Experi ­ments and PeCormanco Evaluation in the South China Sev [ J ]. Journai of Signai Processing ，2020，36(12) : 2043-2051.DOI : 10.16798/j. ion. 1003-0530. 2020. 12. 010.Underwater Acoustie Glider Target Detection Experiments andPerformance Evaluation id ttr Soutt Chiea SeaWang Chao 1，2 Sun Qindong 1，2 Zhang Lin 1，2 Wang Wenlong 1，2 Zhang Xiaochuvn 1，2(1. Nava Submarine Academy ，Qingdao ，Shandong 266199，China ； 2. Pilot National Laboctoc forMarine Science and Technology ( Qingdao )，Qingdao ，Shandong 266237，China )Abstract : Aiming a- the problem of taraet detection for traditionai means with Iow-electiveness ratios and severety a/ectedby harsh sea conditions. It uraent need to build a safe and elective ，mulU-dimensionai sea taraet detection system. Under ­water acoustic gliders can be widety used in sea taraet detection due to their advantages of good concealment peCormanco ， strong environment adaptability and low cost. Sea taraet detection is greatla a/ected by the complex marine environment ，and it is necessae to care out systematic ，long-term ，and in-depth research. CaiTying out undeevater acoustic glider sea taraet detection expeegents and acquiring detection capabilities for digeent maepme taraets is an irnpoCant means to effee-tivea advance the research. Thg paper focuses on the peCormanco of undeevater acoustic glider on digeent suCace ship taraet detection experiments ，and evaluates and summarizes peCormanco from thee aspects : taraet detection distance ，tar-get azirnuth estinia/on accuracy ，and -3 dB beam width. Fin/ly ，the problems in the previous teals were sorted out ，and the focus of the futue research directions was prospected.Key wordt : taraet detection ； undercater acoustic glider ； detection distance ； azimuth estirnation accuracy引言 滑翔机是一种具有在位时间长、持续航程远、噪声引言水平低、隐蔽性能好、成本低、可操作等优点的水下作为一种新型的水下无人移动观测平台，水下潜器，已经被广泛应用于海洋环境监测领域，与潜标、收稿日期：2020-03-03 "修回日期：2020-05-27基金项目：国家重点研发计划(2019YFC0311700)；青岛海洋科学与技术试点国家实验室问海计划(2017WHZZB0601)2044信号处理第36卷浮标等传统观测手段相比，水下滑翔机在海洋环境观测方面的应用具有明显优势A4］。

JERUSALEM — Last month, on a freeway from Jerusalem to the Dead Sea, I sat in the driver's seat of an Audi A7 while software connected to a video camera on the windshield drove the car at speeds up to 65 miles an hour — making a singularstatement about the rapid progress in the development of self-driving cars.耶路撒冷——上月，在从耶路撒冷到死海的高速路上，我坐在一辆奥迪(Audi)A7的驾驶座上，而与装在挡风玻璃上的摄像头相连的软件，则在驾驶这辆汽车以每小时65英里（约合104.6公里）的速度前进，显示着自动驾驶汽车发展的神速。

While the widely publicized Google car and other autonomous vehicles are festooned with cameras, radar and the laser range finders called lidars, this one is distinctive because of the simplicity and the relatively low cost of its system — just a few hundred dollars' worth of materials. "The idea is to get the best out of camera-only autonomous driving," said Gaby Hayon, senior vice president for research and development at Mobileye Vision Technologies, the Israeli company that created the system in the Audi.受到广泛宣传的谷歌(Google)汽车和其他自动驾驶汽车装满了摄像头、雷达和被称作“激光雷达”(lidar)的激光测距仪，而这部汽车的独到之处却在于简洁，以及成本相对较低的系统，材料只需几百美元。

智能驾驶辅助系统英文题The Evolution and Future of Intelligent Driving Assistance Systems.The automotive industry is on the cusp of a revolution, and intelligent driving assistance systems (IDAS) are leading the charge. These systems, which utilize a range of sensors, algorithms, and advanced computing capabilities, aim to enhance the driving experience, improve safety, and even automate certain aspects of vehicle operation.1. The Need for Intelligent Driving Assistance Systems.The need for IDAS is underscored by the growing complexity of road networks, increasing vehicle traffic, and the human factor. Drivers are subject to fatigue, distractions, and other factors that can lead to accidents. IDAS aim to mitigate these risks by providing real-time assistance and alerts, enabling drivers to make informed decisions and respond quickly to changing road conditions.2. Key Components of IDAS.Sensors: These are the eyes and ears of the system, providing critical information about the vehicle's surroundings. Common sensors include radar, lidar, and cameras.Algorithms: Complex algorithms process the data collected by sensors to interpret road conditions, detect obstacles, and predict potential hazards.Advanced Computing: High-performance computing units enable real-time processing of sensor data, ensuring quick and accurate responses.3. Functions of IDAS.Collision Warning Systems: These systems detect potential collisions and warn drivers, allowing them to take corrective action.Automated Braking: In certain scenarios, such as an impending collision with a pedestrian or another vehicle, IDAS can automatically engage the brakes.Lane Keeping Assistance: This feature helps maintain the vehicle within its lane, reducing the risk of accidents caused by lane departures.Adaptive Cruise Control: This feature automatically adjusts the vehicle's speed to maintain a safe distance from the car ahead, reducing the need for constant manual adjustments.4. Benefits of IDAS.Improved Safety: The most significant benefit of IDAS is the potential to significantly reduce accidents and save lives.Enhanced Driving Experience: Features like adaptive cruise control and lane keeping assistance make driving smoother and less stressful.Increased Efficiency: By automating certain tasks, IDAS can improve fuel efficiency and reduce wear and tear on vehicle components.5. Challenges and Future Challenges.Technology Limitations: Current IDAS still rely heavily on human input and supervision. Fully autonomous driving remains a challenge due to technologicallimitations and ethical considerations.Data Privacy and Security: As IDAS collect vast amounts of data about drivers and their surroundings, there are concerns about data privacy and security.Regulations and Standards: Developing and implementing uniform regulations and standards for IDAS is crucial to ensure their safe and effective deployment.6. Conclusion.Intelligent driving assistance systems are poised to transform the automotive industry, making driving safer, more efficient, and more enjoyable. However, to fully realize their potential, it's crucial to address the technological, ethical, and regulatory challenges that lie ahead. With continued research and development, IDAS promise to revolutionize how we travel.。