ADAS碰撞和车道偏离检测系统设计与实现

为满足交通运输领域对高级驾驶辅助系统(Advanced Driving Assistance System,ADAS)前车碰撞和车道偏离预警性能的检测和质量评价,解决手动测试中不准确、效率低的问题,设计一种能应用于实车测试的高精度动态检测系统。综合运用高精度定位技术和组合导航技术提高系统的测量精度和可靠性,使用自动化检测软件实现人机交互、测量数据分析及运动轨迹直观显示。实验结果表明,系统工作状态稳定,测量精度高,可准确评价ADAS系统碰撞和车道偏离预警性能。

融合STPA及有限状态机的ADAS触发条件生成机制

现有高级辅助驾驶系统(Advanced Driver Assistance Systems,ADAS)功能不断增多且系统复杂性不断提高,不可避免带来了预期功能安全(Safety of the Intended Functionality,SOTIF)问题。触发条件的识别与生成是预期功能安全活动中重要的一环,然而现有对触发条件识别仅借助系统过程理论分析方法(System Theoretic Process Analysis,STPA)进行分析,未充分考虑系统功能状态转换中存在的问题。本文以知识驱动的方式构建触发条件识别机制,将STPA及有限状态机(Finite State Machine,FSM)理论融合构建拓展型系统控制结构,针对拓展型控制架构及功能状态转换进行安全分析,根据系统存在的功能局限及人为误用,完成触发条件的识别、生成、规范化描述、分类及标签化。最后将本文提出的触发条件生成机制应用于集成式巡航辅助系统(Integrated Cruise Assistance,ICA),得到了该系统的触发条件及其分类,并将本文所提出的生成机制与现有相关触发条件生成方法进行对比分析,证明了本机制的实用性、可行性及有效性。

基于智能汽车ADAS功能辐射抗扰度测试方法研究

随着智能汽车自动驾驶辅助系统(Automatic driving assistance system)功能发展,基于雷达和摄像头或激光雷达传感器的自动紧急制动(Automatic emergency braking)、自适应巡航(adaptive cruise control)、车道偏离预警(Lane Departure Warning)的功能在处于复杂电磁环境中,存在失效风险导致安全事故,验证此类功能在复杂电磁场中的可靠性和准确性至关重要。本文采用目标车辆模拟器和环境模拟装置,提出了一种针对智能汽车ADAS功能电磁干扰的整车抗扰性的全新测试方法,验证智能汽车车载ADAS系统电磁安全性能。

ADAS域控制器HIL仿真测试系统研究与开发

先进驾驶辅助系统(Advanced Driver Assistance Systems,ADAS)在汽车行业中占有重要地位,影响着未来汽车的发展趋势。ADAS是现代汽车的关键特征之一,通过整合传感器、摄像头和计算机视觉技术,提供了一系列驾驶辅助功能,旨在提高驾驶安全性、舒适性和便利性。本文搭建ADAS域控制器与虚拟交通场景相结合的半实物仿真平台,实现整车动力学模型的实时运行,模拟电控系统的输入信号和电气负载;采集电控系统的输出和输入信号,实现对实时模型的闭环控制,完成各种测试验证和评估工作。设计的测试系统可提高测试效率、缩短测试周期、降低测试成本,能够对实车测试中难以实现的极端工况进行测试。

Modeling commercial vehicle drivers’ acceptance of advanced driving assistance system (ADAS)

Purpose–Advanced driving assistance system(ADAS)has been applied in commercial vehicles.This paper aims to evaluate the influence factors of commercial vehicle drivers’acceptance on ADAS and explore the characteristics of each key factors.Two most widely used functions,forward collision warning(FCW)and lane departure warning(LDW),were considered in this paper.Design/methodology/approach–A random forests algorithm was applied to evaluate the influence factors of commercial drivers’acceptance.ADAS data of 24 commercial vehicles were recorded from 1 November to 21 December 2018,in Jiangsu province.Respond or not was set as dependent variables,while six influence factors were considered.Findings–The acceptance rate for FCW and LDW systems was 69.52%and 38.76%,respectively.The accuracy of random forests model for FCW and LDW systems is 0.816 and 0.820,respectively.For FCW system,vehicle speed,duration time and warning hour are three key factors.Drivers prefer to respond in a short duration during daytime and low vehicle speed.While for LDW system,duration time,vehicle speed and driver age are three key factors.Older drivers have higher respond probability under higher vehicle speed,and the respond time is longer than FCW system.Originality/value–Few research studies have focused on the attitudes of commercial vehicle drivers,though commercial vehicle accidents were proved to be more severe than passenger vehicles.The results of this study can help researchers to better understand the behavior of commercial vehicle drivers and make corresponding recommendations for ADAS of commercial vehicles.

红外热成像技术在ADAS系统中的应用

针对高级驾驶辅助系统(ADAS)要求的驾驶安全与现有的汽车传感器套件无法充分检测汽车或行人的矛盾,研究红外热成像技术在ADAS系统中的应用。首先讨论了在典型的汽车传感器套件中添加红外热像仪的必要性,然后对图像进行预处理,同时结合深度学习下的目标检测YOLOv5算法进行模型训练,最后用实验数据证明该算法能在复杂的驾驶环境中可以更好地检测和分类交通目标,从而帮助ADAS系统实现兼顾精度与实时性的目标检测。

ADAS领域专利状况分析

根据对先进驾驶辅助技术领域的专利申请分析,对相关研发和产业情况的发展进行研究。发现我国企业在该领域存在的问题、差距。分析产业的发展方向,为我国实现具有自主知识产权的ADAS技术提供策略和建议。

用于ADAS实时目标车辆检测的改进SSD算法

以实际交通场景中存在重叠小目标车辆为重点,为提升汽车辅助驾驶系统(ADAS)对目标车辆检测的准确性,建立了一种实时目标车辆检测改进算法SSD-P。该算法基于2种方法:1)通过增加小目标特征的提取数量,提出了一种浅层特征图像分辨率重建的方法;2)在非极大抑制中嵌入特征向量进行二次判定方法,以克服单发多盒探测器(SSD)算法对小目标检测精度不高、重叠目标检测能力弱的问题。在PASCAL VOC2012数据集、虚拟交通场景以及实际交通场景中,进行了相关实验验证。结果表明:用该SSD-P算法进行目标车辆检测的平均精度(mAP)为92.4%,比改进前的SSD算法精度提升了4.8%。因此,该改进算法能够改善ADAS的准确性。

用于汽车ADAS系统测试的软目标车研究进展

软目标车在汽车高级驾驶辅助系统(Advanced Driving Assistance System,ADAS)测试中发挥着重要作用,它提高了试验的安全性和效率。介绍了软目标车研发的关键技术和研究进展,通过分析乘用车的电磁散射特性、视觉及光学特性,论述了软目标车对于毫米波雷达、视觉传感器以及激光雷达在特征识别方面的设计要求,重点阐述了软目标车对毫米波雷达的探测及识别目标的响应特征——雷达散射截面(Radar Cross Section,RCS)的特征分布及其测量和数据处理方法,为软目标车的开发和使用提供参考依据。

三种先进驾驶辅助系统ADAS方案

先进驾驶辅助系统ADAS,是一种在车辆行驶过程中全程帮助驾驶员的主动安全辅助系统。ADAS系统设计为提高车辆安全,更广泛的应用为提高道路交通安全。ADAS系统采用多种传感器来收集车辆及车辆周围环境的物理数据。采用目标检测、识别与跟踪等处理技术来评估危险性。向驾驶员提供车辆运行状况,提醒驾驶员注意潜在危险,从而提高行车安全性。

信息融合技术在ADAS中的应用

文章通过对ADAS功能的解析,为了保证先进驾驶辅助系统在安全性及智能性方面的进一步应用,提出了一种信息融合的解决方案。该方案通过将多源信息的输入进行融合,通过对不同信息的处理融合为数据信息的可靠分析提供决策依据,从而提高准确率,降低漏检率和虚警率,从而实现辅助驾驶系统的自学习和自检,最终实现智能驾驶、安全驾驶的最终目标。

基于硬件在环平台的ADAS功能及故障诊断测试研究

高级辅助驾驶系统是一种复杂的智能控制系统,从系统开发到落地应用需要经过充分的验证测试。硬件在环仿真技术能够很好地解决实车验证测试成本高、测试场景复杂、周期长和危险性高等问题。采用Vector CANoe软硬件和Carmaker仿真软件构建智能驾驶硬件在环仿真平台,实现高级驾驶辅助系统(ADAS)控制算法验证和故障诊断验证。结果表明,硬件在环平台能够有效地验证ADAS控制系统,对解决ADAS测试问题具有参考价值。

Model calibration concerning risk coefficients of driving safety field model

Driving safety field(DSF) model has been proposed to represent comprehensive driving risk formed by interactions of driver-vehicle-road in mixed traffic environment. In this work, we establish an optimization model based on grey relation degree analysis to calibrate risk coefficients of DSF model. To solve the optimum solution, a genetic algorithm is employed. Finally, the DSF model is verified through a real-world driving experiment. Results show that the DSF model is consistent with driver's hazard perception and more sensitive than TTC. Moreover, the proposed DSF model offers a novel way for criticality assessment and decision-making of advanced driver assistance systems and intelligent connected vehicles.

A Survey and Tutorial of EEG-Based Brain Monitoring for Driver State Analysis

The driver’s cognitive and physiological states affect his/her ability to control the vehicle.Thus,these driver states are essential to the safety of automobiles.The design of advanced driver assistance systems(ADAS)or autonomous vehicles will depend on their ability to interact effectively with the driver.A deeper understanding of the driver state is,therefore,paramount.Electroencephalography(EEG)is proven to be one of the most effective methods for driver state monitoring and human error detection.This paper discusses EEG-based driver state detection systems and their corresponding analysis algorithms over the last three decades.First,the commonly used EEG system setup for driver state studies is introduced.Then,the EEG signal preprocessing,feature extraction,and classification algorithms for driver state detection are reviewed.Finally,EEG-based driver state monitoring research is reviewed in-depth,and its future development is discussed.It is concluded that the current EEGbased driver state monitoring algorithms are promising for safety applications.However,many improvements are still required in EEG artifact reduction,real-time processing,and between-subject classification accuracy.

Methodical Approach to the Development of a Radar Sensor Model for the Detection of Urban Traffic Participants Using a Virtual Reality Engine

New approaches for testing of autonomous driving functions are using Virtual Reality (VR) to analyze the behavior of automated vehicles in various scenarios. The real time simulation of the environment sensors is still a challenge. In this paper, the conception, development and validation of an automotive radar raw data sensor model is shown. For the implementation, the Unreal VR engine developed by Epic Games is used. The model consists of a sending antenna, a propagation and a receiving antenna model. The microwave field propagation is simulated by a raytracing approach. It uses the method of shooting and bouncing rays to cover the field. A diffused scattering model is implemented to simulate the influence of rough structures on the reflection of rays. To parameterize the model, simple reflectors are used. The validation is done by a comparison of the measured radar patterns of pedestrians and cyclists with simulated values. The outcome is that the developed model shows valid results, even if it still has deficits in the context of performance. It shows that the bouncing of diffuse scattered field can only be done once. This produces inadequacies in some scenarios. In summary, the paper shows a high potential for real time simulation of radar sensors by using ray tracing in a virtual reality.

Characterization of Driving Style and the Influence of Distraction Based on Non-intrusive Driving Parameters

It is difficult to model human behavior because of the variability in driving styles and driving skills. However, for some driver assistance systems, it is necessary to have knowledge of that behavior to discriminate potentially hazardous situations, such as distraction, fatigue or drowsiness. Many of the systems that look for driver distraction or drowsiness are based on intrusive means （analysis of the electroencephalogram--EEG） or highly sensitive to operating conditions and expensive equipment （eye movements analysis through artificial vision）. A solution that seeks to avoid the above drawbacks is the use of driving parameters This article presents the conclusions obtained after a set of driving simulator tests with professional drivers with two main objectives using driving variables such as speed profile, steering wheel angle, transversal position on the lane, safety distance, etc., that are available in a non-intrusive way： （1） To analyze the differences between the driving patterns of individual drivers; and （2） To analyze the effect of distraction and drowsiness on these parameters. Different scenarios have been designed, including sequences with distractions and situations that cause fatigue. The analysis of the results is carried out in time and frequency domains in order to identify situations of loss of attention and to study whether the evolution of the analyzed variables along the time could be considered independent of the driver.

A New Speed Limit Recognition Methodology Based on Ensemble Learning:Hardware Validation

Advanced DriverAssistance Systems(ADAS)technologies can assist drivers or be part of automatic driving systems to support the driving process and improve the level of safety and comfort on the road.Traffic Sign Recognition System(TSRS)is one of themost important components ofADAS.Among the challengeswith TSRS is being able to recognize road signs with the highest accuracy and the shortest processing time.Accordingly,this paper introduces a new real time methodology recognizing Speed Limit Signs based on a trio of developed modules.Firstly,the Speed Limit Detection(SLD)module uses the Haar Cascade technique to generate a new SL detector in order to localize SL signs within captured frames.Secondly,the Speed Limit Classification(SLC)module,featuring machine learning classifiers alongside a newly developed model called DeepSL,harnesses the power of a CNN architecture to extract intricate features from speed limit sign images,ensuring efficient and precise recognition.In addition,a new Speed Limit Classifiers Fusion(SLCF)module has been developed by combining trained ML classifiers and the DeepSL model by using the Dempster-Shafer theory of belief functions and ensemble learning’s voting technique.Through rigorous software and hardware validation processes,the proposedmethodology has achieved highly significant F1 scores of 99.98%and 99.96%for DS theory and the votingmethod,respectively.Furthermore,a prototype encompassing all components demonstrates outstanding reliability and efficacy,with processing times of 150 ms for the Raspberry Pi board and 81.5 ms for the Nano Jetson board,marking a significant advancement in TSRS technology.

老龄化视角下的驾驶行为分析与智能辅助装置研究综述

为了设计更具包容性的智能车辆,需要开展老年人驾驶行为分析以及相关智能辅助装置开发。本文综述了相关研究,总结了老年人的交通事故特点以及生理、心理因素对老年驾驶行为的影响,阐述了融合高级驾驶辅助系统(ADAS),以及物联网技术(IoT)的健康监测系统、基于智能语言模型的机器人驾驶员代理系统等智能辅助装置。本文所述研究为老年驾驶员提供了驾驶行为分析与智能辅助装置的创新解决方案,同时为智能车辆和交通系统的未来发展提供了有益的参考。

Intelligent speed adaptation for visibility technology affects drivers’speed selection along curves with sight limitations

Sight obstructions along road curves can lead to a crash if the driver is not able to stop the vehicle in time.This is a particular issue along curves with limited available sight,where speed management is necessary to avoid unsafe situations(e.g.,driving off the road or invading the other traffic lane).To solve this issue,we proposed a novel intelligent speed adaptation(ISA)system for visibility,called V-ISA(intelligent speed adaptation for visibility).It estimates the real-time safe speed limits based on the prevailing sight conditions.V-ISA comes with three variants with specific feedback modalities(1)visual and(2)auditory information,and(3)direct intervention to assume control over the vehicle speed.Here,we investigated the efficiency of each of the three V-ISA variants on driving speed choice and lateral behavioural response along road curves with limited and unsafe available sight distances,using a driving simulator.We also considered curve road geometry(curve direction:rightward vs.leftward).Sixty active drivers were recruited for the study.While half of them(experimental group)tested the three V-ISA variants(and a V-ISA off condition),the other half always drove with the V-ISA off(validation group).We used a linear mixed-effect model to evaluate the influence of V-ISA on driver behaviour.All V-ISA variants were efficient at reducing speeds at entrance points,with no discernible negative impact on driver lateral behaviour.On rightward curves,the V-ISA intervening variant appeared to be the most effective at adapting to sight limitations.Results of the current study implies that V-ISA might assist drivers to adjust their operating speed as per prevailing sight conditions and,consequently,establishes safer driving conditions.

辅助驾驶系统对重型卡车驾驶员长途驾驶中疲劳程度的影响

乘用车领域的过往研究发现,相较于手动驾驶使用辅助驾驶系统的驾驶员会更容易出现疲劳,因此有必要探究辅助驾驶系统对重型卡车驾驶员长途驾驶疲劳程度的影响。基于超过120 h的自然驾驶实验,采用多项生心理指标(如心率、心率变异性等)对比了重型卡车驾驶员手动驾驶车辆时和在辅助驾驶系统帮助下驾驶车辆时的疲劳程度。辅助驾驶情况下驾驶员平均心率、呼吸频率、呼吸深度以及瞳孔直径均高于手动驾驶情况下,辅助驾驶情况下驾驶员连续二次心跳(RR)间隔均方根、低高频比例、眨眼频率、眨眼时长以及眼睑闭合百分比(PERCLOS)均低于手动驾驶情况下。当驾驶员处于手动驾驶情况时,驾驶时间每增加2 h,反应时间会增加0.032 s;当驾驶员处于辅助驾驶情况时,反应时间不随驾驶时间发生显著变化。重型卡车驾驶员在辅助驾驶系统帮助下驾驶车辆时的疲劳程度低于手动驾驶,本研究可以为辅助驾驶系统在重型卡车中的安全应用提供理论支持。