FPGA-Based Traffic Sign Recognition for Advanced Driver Assistance Systems

This paper presents the implementation of an embedded automotive system that detects and recognizes traffic signs within a video stream. In addition, it discusses the recent advances in driver assistance technologies and highlights the safety motivations for smart in-car embedded systems. An algorithm is presented that processes RGB image data, extracts relevant pixels, filters the image, labels prospective traffic signs and evaluates them against template traffic sign images. A reconfigurable hardware system is described which uses the Virtex-5 Xilinx FPGA and hardware/software co-design tools in order to create an embedded processor and the necessary hardware IP peripherals. The implementation is shown to have robust performance results, both in terms of timing and accuracy.

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。

Seroprevalence Survey of HIV and Hepatitis B Virus and Behavioral Characteristics among Heavy Truck Drivers along Port Sudan-Khartoum Highways

The prevalence of human immunodeficiency virus (AIDS) and hepatitis B virus among heavy truck drivers and their assistants has been well documented globally in correlation with their behavioral characteristics. The present study aimed to screen for human immunodeficiency virus (HIV), hepatitis B virus (HBV), and behavioral characteristics among heavy truck drivers in Port Sudan. A cross-sectional study was conducted on 274 heavy truck drivers and their assistants who used the highway Port Sudan-Khartoum in Port Sudan city during 2019-2021. Data on behavioral characteristics and substance use habits were collected using a structured questionnaire, and an ELISA test was used to screen for HIV and HBV infections in the study participants. The chi-square test, odds ratio, and confidence intervals were used to find the association between behavioral characteristics and seropositive HIV/HBV. Of the 274 enrolled participants, the seroprevalence rates of HIV were 2.7% and HBV was 23.7%. Ninety-four (34.3%) of them had a history of high-risk sexual behavior outside of marriage;only two (0.7%) used condoms;14.2% of participants reported alcohol use;and 1.1% reported drug use. Univariate analysis revealed that having a sex history outside of marriage with ≥1 sex partner and never using a condom with a spouse or casual partner were significant risk factors for HIV and HBV among drivers. Fortunately, we found that most of the drivers reported low alcohol and drug use. Concerning this study, the seroprevalence of HIV and HBV is highly associated with a history of having sex outside of marriage and sexual behavior among truck drivers and assistances. Additional studies are needed to further investigate other STIs and behavioral characteristics associated with factors in truck drivers/assistance in different truck stop regions in Sudan.

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。

基于电波暗室的整车ADAS功能EMC测试技术研究

文章旨在深入探讨智能新能源汽车高级驾驶辅助系统ADAS功能的电磁兼容性能测试方法。通过采用光学与雷达波模拟器相互结合的方式,搭建自适应巡航ACC、自动紧急制动AEB以及交通标识识别TSR功能的电磁抗扰性测试系统,成功达成电波暗室内多传感器融合的ADAS功能的稳定触发,验证在复杂电磁环境下ADAS功能电磁抗扰性测试的可行性。

Vehicles, Advanced Features, Driver Behavior, and Safety: A Systematic Review of the Literature

Driver errors contribute to more than 94% of traffic crashes. Automotive companies are striving to enhance their vehicles to eliminate driver errors and reduce the number of crashes. Various advanced features like lane departure warning (LDW), blind spot warning (BSW), over speed warning (OSW), forward collision warning (FCW), lane keep assist (LKA), adaptive cruise control (ACC), cooperative ACC (CACC), and automated emergency braking (AEB) are designed to assist with, or in some cases take over, certain driving maneuvers. They can be broadly categorized into advanced driver assistance system (ADAS) and automated features. Each of these advanced features focuses on addressing a particular task of driving, thereby, aiding the driver, influencing their behavior, and enhancing safety. Many vehicles with these advanced features are penetrating into the market, yet the total reported number of crashes has increased in recent years. This paper presents a systematic review of these advanced features on driver behavior and safety. The review is categorized into 1) survey and mathematical methods to assess driver behavior, 2) field test methods to assess driver behavior, 3) microsimulation methods to assess driver behavior, 4) driving simulator methods to assess driver behavior, and 5) driver understanding and the effectiveness of advanced features. It is followed by conclusions, knowledge gaps, and need for further research.

Improving the Accuracy of Under-Fog Driving Assistance System

Driving in fog condition is dangerous. Fog causes poor visibility on roads leading to road traffic accident (RTA). RTA in Albaha is common because of its rough terrain, in addition to the climate that is mainly rainy and foggy. The rain season in Albaha region begins in October to February characterized by rainfall and fog. Many studies have reported the adverse effects of the rain on RTA which results in an increased rate of crashes. On the other hand, Albaha region is not supported by a proper intelligent transportation system and infrastructure. Thus, a Driver Assistance System (DAS) that requires minimum infrastructure is needed. A DAS under fog called No_Collision has been developed by a researcher in Albaha University. This paper discusses an implementation of adaptive Kalman Filter by utilizing Fuzzy logic system with the aim to improve the accuracy of position and velocity prediction of the No_Collision system. The experiment results show a promising adaptive system that reduces the error percentage of the prediction up to 56.58%.

Pedestrian detection in driver assistance using SSD and PS-GAN

Pedestrian detection is a critical challenge in the field of general object detection,the performance of object detection has advanced with the development of deep learning.However,considerable improvement is still required for pedestrian detection,considering the differences in pedestrian wears,action,and posture.In the driver assistance system,it is necessary to further improve the intelligent pedestrian detection ability.We present a method based on the combination of SSD and GAN to improve the performance of pedestrian detection.Firstly,we assess the impact of different kinds of methods which can detect pedestrians based on SSD and optimize the detection for pedestrian characteristics.Secondly,we propose a novel network architecture,namely data synthesis PS-GAN to generate diverse pedestrian data for verifying the effectiveness of massive training data to SSD detector.Experimental results show that the proposed manners can improve the performance of pedestrian detection to some extent.At last,we use the pedestrian detector to simulate a specific application of motor vehicle assisted driving which would make the detector focus on specific pedestrians according to the velocity of the vehicle.The results establish the validity of the approach.

ADAS中红外场景下的行人车辆检测技术研究

近年高级驾驶辅助系统(ADAS)发展迅猛,但其行人车辆检测模块在恶劣环境下存在误测。针对现今红外场景下目标检测算法精准度较低的问题,使用CLAHE算法增强和BM3D算法降噪,对原始红外图像进行预处理,改进YOLOv5s网络的激活函数、损失函数。对YOLOv5s算法主干网络的末端采用Transformer模块,增加了一层160×160的检测层。实验结果表明,算法在FLIR热红外数据集上平均精度(mAP)达到92.12%,相较于原YOLOv5s算法提升了6.16%,加强了红外场景下的车辆、行人等特征的提取能力,提高了模型的鲁棒性。

驾驶情绪对交通安全的影响及其识别与调节策略综述

不良情绪可能会导致驾驶人注意力分散、反应迟缓和决策失误,增加交通安全隐患。为降低由负面驾驶情绪造成的交通安全影响,系统梳理和分析了情绪对驾驶行为的影响与诱发机制、驾驶情绪识别方法相关的理论基础,并聚焦驾驶情绪调节策略,探讨各方向的研究热点和未来发展趋势。结果表明:现有研究大多是分析某个特定离散情绪对驾驶行为的影响,复合情绪对驾驶行为影响的研究较少;性格特征、天气影响及多种因素协同作用对驾驶情绪的诱发机制尚未完全阐明;驾驶情绪的识别主要基于面部表情、语音文本、驾驶行为和生理信号,由于单一特征的情绪识别结果鲁棒性较差、精度较低,当前的研究趋势逐渐向多模态特征耦合识别方向倾斜,其中,特征层融合策略由于能够更高效地处理多源异构信号,应用最为广泛;驾驶人可通过视觉、听觉、触觉和嗅觉调节情绪状态,触觉和嗅觉调节方法能减少驾驶人的注意力分散,调节作用效果明显,可应用于智能座舱的驾驶辅助系统。未来需研究复合情绪对驾驶行为的影响机制,重视驾驶风格等多因素协同作用对驾驶情绪的诱发机制,特别关注性格及智能车载设备的影响;根据驾驶人行为数据、交通状况和道路信息建立精准的驾驶风险预测模型;深入考虑多源信息融合的人机共驾情绪识别,实现实时监测;研发情感交互的智能汽车座舱,依据驾驶人的驾驶偏好、习惯和行为模式个性化调节驾驶负面情绪。

辅助驾驶汽车信息安全检验关键技术研究及测试验证

随着辅助驾驶汽车技术的不断发展和普及,信息安全问题已经成了一个重要的研究方向。本文针对辅助驾驶汽车面临的信息安全隐患,研究提出信息安全检验关键技术,包括检验指标、检验方法和检验标准等,为辅助驾驶汽车安全性评估提供参考;提高辅助驾驶汽车安全性,推动行业的规范化发展,为辅助驾驶汽车的安全性和可靠性提供支持。

智能网联汽车技术与标准发展研究

随着科学技术的快速发展,智能网联汽车也已经逐渐开始成为新时期下汽车发展的主要趋势,其主要是通过多种信息技术、智能终端设备等来实现车辆的智能化操作。其具有多个系统组成,主要包括车载通信系统、车载传感系统、车辆控制系统以及驾驶辅助系统等,其中包括车载检测系统、传感系统以及通讯系统等,还有一些其他的子系统构成。智能网联汽车也是通过智能化技术来对汽车的行驶进行控制,从而实现自动驾驶,进而实现自动化驾驶,减少交通事故发生概率。另外,智能网联汽车主要是通过人工智能以及互联网技术来实现对车辆的管理与控制,从而实现智能化的操作。

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。

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.

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。

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。

基于模型预测的智能汽车辅助驾驶控制研究

辅助驾驶控制是智能汽车的重要功能,可有效协助驾驶员完成驾驶操作,减少驾驶疲劳并提升驾驶安全。智能车辆的辅助驾驶控制需要在不干扰驾驶员的情况下,提供合适的车辆运动控制量并防止可能发生的碰撞。文章提出了一种基于模型预测控制的智能汽车辅助驾驶控制方法。该控制方法在驾驶员正常操作时符合驾驶员的操作特性,在可能发生碰撞时纠正驾驶员的转向操作,能够保证安全性且不干扰驾驶员。为了避免与周围车辆的碰撞,基于周围车辆位置建立约束条件,并引入模型预测辅助驾驶控制设计。通过仿真研究验证了所提出的辅助驾驶控制方法,结果表明,所提出的预测控制方法可以帮助驾驶员避免与附近车辆可能发生的碰撞,从而保证智能汽车的行驶安全。

基于实车试验的ADAS测试评价方法研究

介绍了国内外汽车先进辅助驾驶系统(ADAS)的测试评价方法,着重分析了2021版C-NCAP测试评价方法的改动内容和变化趋势,在此基础上设置了2个对比测试场景和4个性能测试场景,并使用2台实车进行了场地验证测试。测试结果表明,夜间、偏置等场景变量会一定程度影响车辆ADAS的性能表现;不同系统的LKA、BSD等新增加功能的表现也存在明显差异,增加场景变量和新功能的场景对测试评价方法的发展意义重大。

纯电动汽车驾驶员离座安全辅助系统设计

提出一种纯电动汽车的驾驶员离座安全辅助系统。当系统检测到车辆在上电且非驻车状态下驾驶员离座时,系统会发出离座声光预警信号且自动激活双闪灯,同时系统将禁止整车动力输出和控制整车主动刹车。当车辆被刹停后,系统在主动启动自动驻车的同时会将车辆挡位设置成空挡,并主动切断整车高压电,以达到安全行车的目的。

考虑城市交通环境的多模式自适应巡航控制策略

为了适应城市交通环境,提出多模式自适应巡航控制策略。在上层控制器中,设计恒速巡航、稳态跟随、接近/变道、强加速、强减速和避免碰撞6种模式策略;下层控制器采用PID控制结合查找表方法,设计油门控制器与制动控制器之间的过渡区。仿真结果表明:上层控制器能够在复杂工况下选择合适的行车模式,下层控制器能够满足下坡/上坡加速度控制的预期要求,符合车辆驱动系统特性,可以应用于实车驾驶辅助系统,提高车辆的行驶安全性。