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.

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.

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。

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。

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。

Approach for improved development of advanced driver assistance systems for future smart mobility concepts

To use the benefits of Advanced Driver Assistance Systems(ADAS)-Tests in simulation and reality a new approach for using Augmented Reality(AR)in an automotive vehicle for testing ADAS is presented in this paper.Our procedure provides a link between simulation and reality and should enable a faster development process for future increasingly complex ADAS tests and future mobility solutions.Test fields for ADAS offer a small number of orientation points.Furthermore,these must be detected and processed at high vehicle speeds.That requires high computational power both for developing our method and its subsequent use in testing.Using image segmentation(IS),artificial intelligence(AI)for object recognition,and visual simultaneous localization and mapping(vSLAM),we aim to create a three-dimensional model with accurate information about the test site.It is expected that using AI and IS will significantly improve performance as computational speed and accuracy for AR applications in automobiles.

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。

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。

尾灯特征的ADAS系统前方车辆识别

为可靠识别汽车安全辅助驾驶系统(ADAS)前方车辆目标,提出了基于车辆尾灯特征提取的径向基神经网络车辆识别方法。构建了车辆尾灯对宽度限制、高度约束及左右尾灯面积比等约束条件,提取了车辆的尾灯信息并获取尾灯的质心位置,经过形态学运算后确定了尾灯的标记区域,再结合车辆图像与实际尺寸比例得到车辆可能存在的感兴趣标记区域。针对感兴趣标记区域进行了车辆存在性的进一步确认,提取了感兴趣区域的边缘特征与区域特征共计19个特征参数(6个独立不变矩、8个余弦变换描述子和5个区域特征参数),构建了由19维输入神经元和2个输出量组成的三层径向基神经网络车辆识别器。利用采集的车辆样本图像,分别验证了正常天气与雨天、雪天及夜间行车环境下基于尾灯特征获取车辆区域检测方法的可行性。对采集的1700张车辆正、负样本,完成了径向基神经网络的训练,通过训练误差性能曲线得知本文设计的网络达到了训练精度要求。随机选取了60%的样本,完成了径向基神经网络车辆识别器的测试,实现了检测区域内车辆的进一步确认,也针对停车场及并行车辆等情形进行了确认测试,其正确率达到94%,有效弥补单一尾灯特征实施车辆检测的漏检或误检现象,大大提高了车辆检测的可靠性。

基于多传感器融合的ADAS前车识别研究

高级辅助驾驶系统中环境感知模块的研究是智能汽车发展过程中的重要一步,基于单一种类传感器的环境感知系统因传感器特性而存在弊端。文章对各种用于前车识别的车载传感器做了较为深入的研究,整理了各自的优缺点,参考了大量的相关文献,对国内外主流融合研究方法进行了综述,推断出融合相机和毫米波雷达这两种传感器探测到的目标信息是现阶段有效的前车识别手段,并提出一种合理的融合方案。

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。

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。

Methodical Approach to Integrate Human Movement Diversity in Real-Time into a Virtual Test Field for Highly Automated Vehicle Systems

Recently, virtual realities and simulations play important roles in the development of automated driving functionalities. By an appropriate abstraction, they help to design, investigate and communicate real traffic scenario complexity. Especially, for edge cases investigations of interactions between vulnerable road users (VRU) and highly automated driving functions, valid virtual models are essential for the quality of results. The aim of this study is to measure, process and integrate real human movement behaviour into a virtual test environment for highly automated vehicle functionalities. The overall system consists of a georeferenced virtual city model and a vehicle dynamics model, including probabilistic sensor descriptions. By motion capture hardware, real humanoid behaviour is applied to a virtual human avatar in the test environment. Through retargeting methods, which enable the independency of avatar and person under test (PuT) dimensions, the virtual avatar diversity is increased. To verify the biomechanical behaviour of the virtual avatars, a qualitative study is performed, which funds on a representative movement sequence. The results confirm the functionality of the used methodology and enable PuT independence control of the virtual avatars in real-time.

Advances in Vision-Based Lane Detection：Algorithms,Integration,Assessment,and Perspectives on ACP-Based Parallel Vision

Lane detection is a fundamental aspect of most current advanced driver assistance systems(ADASs). A large number of existing results focus on the study of vision-based lane detection methods due to the extensive knowledge background and the low-cost of camera devices. In this paper, previous visionbased lane detection studies are reviewed in terms of three aspects, which are lane detection algorithms, integration, and evaluation methods. Next, considering the inevitable limitations that exist in the camera-based lane detection system, the system integration methodologies for constructing more robust detection systems are reviewed and analyzed. The integration methods are further divided into three levels, namely, algorithm, system,and sensor. Algorithm level combines different lane detection algorithms while system level integrates other object detection systems to comprehensively detect lane positions. Sensor level uses multi-modal sensors to build a robust lane recognition system. In view of the complexity of evaluating the detection system, and the lack of common evaluation procedure and uniform metrics in past studies, the existing evaluation methods and metrics are analyzed and classified to propose a better evaluation of the lane detection system. Next, a comparison of representative studies is performed. Finally, a discussion on the limitations of current lane detection systems and the future developing trends toward an Artificial Society, Computational experiment-based parallel lane detection framework is proposed.

Vision-Based Vehicle Detection for a Forward Collision Warning System

A weather-adaptive forward collision warning (FCW) system was presented by applying local features for vehicle detection and global features for vehicle verification. In the system, horizontal and vertical edge maps are separately calculated. Then edge maps are threshold by an adaptive threshold value to adapt the brightness variation. Third, the edge points are linked to generate possible objects. Fourth, the objects are judged based on edge response, location, and symmetry to generate vehicle candidates. At last, a method based on the principal component analysis (PCA) is proposed to verify the vehicle candidates. The proposed FCW system has the following properties: 1) the edge extraction is adaptive to various lighting condition;2) the local features are mutually processed to improve the reliability of vehicle detection;3) the hierarchical schemes of vehicle detection enhance the adaptability to various weather conditions;4) the PCA-based verification can strictly eliminate the candidate regions without vehicle appearance.

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.