Low cost design of parallel parking assist system based on an ultrasonic sensor

This paper presents a low cost design and implementation of a parallel parking assist system (PPAS) based on ultrasonic sensors. Generally, a PPAS requires several types of sensors, such as an ultrasonic sensor, camera sensor, radar sensor and laser sensor for parking space detection. However, our proposed PPAS only requires two ultrasonic sensors on the front and lateral sides for parking space detection. Moreover, a steering angle sensor and wheel speed sensor installed in the vehicle are used to obtain vehicle position information for localization in ultrasonic range data. The hardware architecture of the PPAS based on an electronic control unit (ECU) module, sensor modules and a human machine interface (HMI) module was proposed. Moreover, the software architecture of the PPAS is based on system initialization, scheduling, recognition and a control algorithm. In particular, a novel sensor algorithm was proposed to minimize the vehicle corner error of the ultrasonic sensor. A prototype of the PPAS based on the proposed architecture was constructed. The experimental results demonstrate that the implemented prototype is robust and successfully performs parking space detection and automatic steering control. Finally, the low cost design and implementation of the PPAS was possible due to the cheap ultrasonic sensors, simple hardware design and low computational complexity of the proposed algorithm.     

Design and development of automotive blind spot detection radar system based on ROI pre-processing scheme

In the conventional 2D-FFT based target detection method, all range-Doppler cells are computed by FFT (Fast Fourier Transform) and scanned by CA-CFAR (Cell-Averaging Constant False Alarm Rate) detection. This results in high computational complexity and long processing time. In this paper, we developed an automotive 24 GHz BSD (Blind Spot Detection) FMCW (Frequency Modulated Continuous Wave) radar with a low complexity target detection architecture based on a ROI (Region Of Interest) pre-processing scheme. In the real BSD zone, because the number of cars to be detected is limited, the designed method only extracts their velocities corresponding to the range ROIs in which real targets exist. Moreover, the presence probability of vehicles with the same range-bin but different velocities is very low. Thus, in the designed method, some Doppler ROIs cells with a high magnitude are only applied for CA-CFAR detection. This architecture can dramatically reduce the amount of data to be processed compared to that of the conventional 2D FFT based method, resulting in enhanced processing time. We developed a 24 GHz FMCW radar system composed a transceiver, antennas, and signal processing module. The designed algorithm was implemented in a tiny micro-processor of the signal processing module. By implementing our proposed algorithm in the developed 24 GHz FMCW radar system in an anechoic chamber and a real road, we verified that the range and velocity of a car occupying the BSD zone were detected. Compared to that of the conventional method, the reduction ratio of the total processing time was measured to be 52.4 %.     

存在车辆干扰的车道线识别

为避免道路上行驶的其它车辆对车道线识别的干扰,提出了一种结合车辆识别的车道线识别方法。融合雷达数据,车辆识别模块首先在图像中识别出车辆占据的区域;对于每一个车道线识别模块挑出的车道线候选点进行判断,去除处于车辆区域的车道线点;如果有效车道线点数目不足,则利用卡尔曼滤波的跟踪结果,确定符合最小风险函数的车道线位置。经过多种工况下的试验验证,该方法能够稳定地对车道线进行识别,准确地提取车道线参数,并且算法对车辆干扰有良好的抵抗能力。     

智能车辆安全辅助驾驶技术研究近况

论述了安全辅助驾驶技术的研究现状、研究的必要性以及研究进展。安全辅助驾驶技术包括车道偏离预警与保持、前方车辆探测及安全车距保持、行人检测、驾驶员行为监测、车辆运动控制与通讯等。分析了各种传感器的优缺点及其在实际应用过程中存在的问题,基于单一传感器不能很好地解决安全辅助驾驶技术可靠性和环境适应能力的要求,应结合激光雷达技术解决图像模糊问题,利用红外传感器增强机器视觉识别的可靠性,未来的安全辅助驾驶技术应该采取多种传感器融合的技术,结合毫米波雷达和激光雷达系统具有深度测量精确的特点,将极大的推动汽车安全辅助驾驶系统的应用和推广。     

大型营运客车在高速公路的换道安全性辨识

为了给大型营运客车换道预警系统设计提供参考,采用毫米波雷达、激光雷达、车道线识别传感器、GPS、视频监控系统以及控制器局域网（CAN）总线数据采集仪等设备,基于小型乘用车搭建浮动车采集平台。通过在试验线路上进行1.5×10⁴ km的驾驶试验,获取1 200余次营运客车的真实换道数据。以Jula提出的换道安全性模型为基础,结合营运客车的换道行为特征,通过分析换道进程结束后客车需要与周围车辆保持的安全距离,建立适合于营运客车的3类换道安全性识别模型（客车与自车道前方车辆、目标车道前方车辆、目标车道后方车辆）,并利用真实数据对3类模型进行验证。研究结果表明：客车换道持续时间均值为10.4 s,换道起始时刻与目标车道后方车辆的距离为10.0~40.0 m;所有换道样本中,73.3%的换道过程中客车速度要高于目标车道后方车辆,且超过90%的换道过程是由前方慢车引起;不同的速度区间下,车速和航向角联合变化情况下,驾驶人控制营运客车的横向偏移速度保持稳定,可认为客车驾驶人的心理预期换道进程存在固定经验模式,这与小型车换道的研究结论存在较大差异,传统的TTC预警算法识别率较低,在不同速度区间情况下,所提出的模型对客车与自车道前方车辆、目标车道前方车辆、目标车道后方车辆的换道安全识别评价准确率均超过了90%。     

基于分时复用原理的多通道探地雷达时序控制模块设计及应用

为实现对隧道衬砌空洞和地质病害的无损、高效检测，基于分时复用原理，通过FPGA编程和时序电路设计实现一种瞬态脉冲型多通道探地雷达时序控制模块，并应用于多频多通道隧道病害检测探地雷达系统。该时序控制模块可以产生多路同频等相位差时钟，分时触发各个通道脉冲信号的单次收发，从时间上分离通道间串扰信号和有效雷达回波，实现探地雷达多频段多通道的高效工作。试验测试和实地验证结果表明： 该时序控制模块在脉冲重复频率为500 kHz时，能实现8通道雷达测试系统相邻通道间240 ns左右的触发时差，并在触发时差范围内保证观察时窗内雷达回波信号的稳定性； 同时，该模块也能应用于3频段6通道隧道病害检测探地雷达系统，并在距离介质分界面65 cm时反映出实际隧道预埋病害的深度和尺寸信息。     

基于扩展卡尔曼滤波的车道融合跟踪

车道检测算法的研究是智能车辆自动导航的首要环节。与目前基于视觉的车道检测与跟踪系统不同,本文提出一种基于扩展卡尔曼滤波的车道融合跟踪方法。该方法利用毫米波雷达探测到前方车辆的距离信息,并采用扩展卡尔曼滤波技术和图像处理技术,建立车道跟踪的动态视觉窗口,提取车道边界,并判断前方车辆相对于车道的位置。该方法大大缩减了处理时间,且增强了系统的鲁棒性。     

Vehicle detection system design based on stereo vision sensors

Vehicle detection is a crucial issue for driver assistance system as well as for autonomous vehicle guidance function and it has to be performed with high reliability to avoid any potential collision. The vision-based vehicle detection systems are regarded promising for this purpose because they require little infrastructure on a highway. However, the feasibility of these systems in passenger car requires accurate and robust sensing performance. In this paper, a vehicle detection system using stereo vision sensors is developed. This system utilizes feature extraction, epipoplar constraint and feature matching in order to robustly detect the initial corresponding pairs. The proposed system can detect a leading vehicle in front and can estimate its position parameters such as the distance and heading angle. After the initial detection, the system executes the tracking algorithm for the vehicles in the lane. The proposed vehicle detection system is implemented on a passenger car and its performances are verified experimentally.     

基于驾驶人不满度的高速公路自动驾驶换道决策

为了实现高速公路的自由换道行为决策，并满足行车安全高效性、决策结果平稳无震荡、与运动规划模块结合引导车辆行驶等要求，提出了一种基于驾驶人不满度的换道行为决策方法。首先，根据驾驶人的速度期望建立了驾驶人不满度累积模型，并基于驾驶人速度不满累积度产生换道意图。其次，依据不同车道障碍车的运动状态，设计了2种目标车道选择策略，通过预测引擎对各个待选车道进行预测和评估，选取其中行车效率较高的车道作为目标车道，同时建立换道最小安全距离模型，用以在换道全过程中判断换道的可行性。然后，将换道行为决策的结果以目标车道的形式传递给基于改进人工势场的运动规划模块，用于运动规划模块目标的选取，以引导车辆横纵向运动。最后，在CarSim/PreScan/Simulink的联合仿真平台和硬件在环平台上建立多种测试场景，验证换道行为决策算法。试验结果表明：换道行为决策算法能够依据驾驶人速度不满累积度产生稳定的换道意图，进而根据所设计的换道策略选取具有更高行车效率的目标车道，并在换道过程中持续判断换道的可行性，以应对障碍车辆突然加减速等突发状况，保证换道过程的高效性和安全性；换道行为决策算法通过目标车道的转换，引导运动规划模块调整车辆的运动，实现跟车、换道等行为。     

车路协同下避让紧急车辆协同换道策略

为加快紧急车辆抵达事故现场的速度，同时减少紧急车辆优先权对其他车辆的影响，运用车路协同系统，提出避让紧急车辆协同换道策略，通过调整紧急车辆下游车辆位置，实现紧急车辆高效通过路段。以紧急车辆前车（DV）及其相邻目标车道车辆为控制对象，根据相邻车道车辆间距与车车通信范围，搜索DV可换道空间间隙集。以交通流整体恢复稳定时间最小为目标，确定DV换道轨迹和相邻车道协作车辆的速度变化，引导车辆完成协同合流，既能保障车辆安全换道，还能降低换道造成的速度振荡传递。同时，为快速恢复DV换道造成的目标车道车辆速度波动，对上游车辆（UV）采取先进先出规则的换道控制策略。所提协同避让紧急车辆的策略考虑了车辆协同换道对交通流的整体影响，并在原有换道策略的基础上提出了减少速度波动传递的控制方法。案例分析结果表明:采用上下游协同换道策略最短换道时间为6s，此时紧急车辆距前车78.66 m时发送避让信号。同时研究发现，恢复交通流速度稳定所需的时间为29 s，比未采用上下游协同换道策略降低了34%。      

基于改进RepVGG网络的车道线检测算法

为提高自动驾驶系统中车道线检测的速度和精度,提出了基于可解耦训练状态与推理状态的车道线检测算法.在结构重参数化VGG(RepVGG)主干网络中引入注意力机制压缩-激励(SE)模块,增强对重要车道线信息的特征提取;同时设计并行可分离的辅助分割分支,对局部特征进行建模以提高检测精度.采用行方向位置分类车道线检测方式,在主干...     

Autonomous Lateral Control of Vehicles in an Automated Highway System

Lateral control of vehicles in IVHS requires the installation of on-board sensors as well as the installation of roadway hardware such as cables, magnets, etc. Existing control approaches in PATH require road curvature and vehicle lateral position (with respect to the center of the lane) information. Hence these approaches rely on roadway sensors to obtain relative lateral position. These methods will necessitate infrastructural changes to the highway.

This paper introduces the concept of autonomous lateral control or auto-tracking. The method allows us to use only line-of-sight sensor information to effect vehicle control. We present a detailed vehicle model. Controllers have been proposed to demonstrate the effectiveness of the proposed auto-tracking scheme. We also examine the possibilities of using this method for lane change purposes in an automated highway system.     

Road boundary detection and tracking for structured and unstructured roads using a 2D lidar sensor

Road boundaries can give useful information for evaluating safe vehicle paths in intelligent vehicles. Much previous research has studied road boundary detection, using different types of sensors such as vision, radar, and lidar. Lidar sensors, in particular, show advantages for road boundary extraction including high resolution and wide field of view. However, none of the previous studies examined the problem of detecting road boundaries when roads could be either structured or unstructured. In this study, we developed a road boundary detection and tracking algorithm using lidar sensing for both structured and unstructured roads. The algorithm extracts road features as line segments in polar coordinates relative to the lidar sensor. The extracted road features are then tracked with respect to a vehicle’s local coordinates using a nearest neighbor filter. The proposed algorithm accurately detected the road boundaries regardless of the road type.     

一种用于大货车换道安全性提示系统

针对大货车视野盲区大以及在货车车道变换时存在潜在追尾等问题,提出一种用于大货车换道安全性提示系统。该系统采用毫米波雷达探测货车后方车辆和本车之间的位置关系,用不同TTC值确定货车换道安全性等级。该系统可明显降低货车在换道时追尾事故的发生。     

Autonomous Lateral Control of Vehicles in an Automated Highway System

SUMMARY

Lateral control of vehicles in IVHS requires the installation of on-board sensors as well as the installation of roadway hardware such as cables, magnets, etc. Existing control approaches in PATH require road curvature and vehicle lateral position (with respect to the center of the lane) information. Hence these approaches rely on roadway sensors to obtain relative lateral position. These methods will necessitate infrastructural changes to the highway.

This paper introduces the concept of autonomous lateral control or auto-tracking. The method allows us to use only line-of-sight sensor information to effect vehicle control. We present a detailed vehicle model. Controllers have been proposed to demonstrate the effectiveness of the proposed auto-tracking scheme. We also examine the possibilities of using this method for lane change purposes in an automated highway system.     

地质雷达探测溶洞等地质体机理研究及实例分析

阐述了地质雷达的探测原理，对雷达探测溶洞等地质体的可行性进行了分析，提出了提高雷达判别准确度的相关措施。对雷探技术用于隧道短距离超前地质预报有一定的借鉴。     

融合改进YOLO和背景差分的道路抛洒物检测算法

针对现有道路抛洒物检测算法识别准确率低、识别种类有限、实时检测效率低的问题，探索了将深度学习目标检测和传统图像处理相结合的抛洒物检测算法。提出在YOLOv5s目标检测算法基础上，对模型结构进行修改以满足实时性需求。具体地，使用卷积优化YOLO中的降采样模块，采用Ghost网络替代原始的特征提取网络以减少计算量，根据抛洒物检测对象的特点设计符合数据集的锚框以提高目标识别准确度。使用优化后的YOLO检测道路场景中车辆、行人作为交通参与者得到检测框，在检测框周围设定感兴趣区域，并在感兴趣区域内用背景差分算法实现前景目标识别。计算前景目标与YOLO检测结果的交并比，排除交通参与者目标后实现道路抛洒物的识别。针对交通参与者检测的实验结果表明，改进后的YOLO检测算法在整体识别精度没有损失的情况下单帧检测速度为20.67 ms，比原始YOLO检测算法速度提升16.42%。真实道路抛洒物实验结果表明，在没有抛洒物训练数据情况下，传统混合高斯模型算法平均精度值为0.51，采用融合改进YOLO和背景差分的抛洒物检测算法平均精度值为0.78，算法检测精度提高52.9%。改进后算法可适用于没有抛洒物数据或正样本数据稀少的情况。该算法在嵌入式设备Jetson Xavier NX上单帧检测速度达到24.4 ms，可实现抛洒物的实时检测。      

基于模仿学习和强化学习的智能车辆换道行为决策

本文中提出了一种基于模仿学习和强化学习的智能车辆换道行为决策方法.其中宏观决策模块通过模仿学习构建极端梯度提升模型,根据输入信息在车道保持、左换道和右换道中选择宏观决策指令,以此确定所需求解的换道行为决策子问题;各细化决策子模块通过深度确定性策略梯度强化学习方法得到优化策略,求解相应换道行为决策子问题,以确定车辆运动目...     

基于改进Hough变换的车道线检测

针对车道线检测中存在的诸多问题,在道路图像预处理的基础上,对Canny算子中的双阈值选取进行改进,能够自适应精确提取车道线边缘特征,并利用带极角约束的Hough变换完成直线检测。算法能够在不同道路环境中准确检测车道线标识,降低光照等因素对车道线检测结果的影响。     

智能车辆的障碍物检测研究方法综述

按照使用传感器的不同类型来分类，对智能车辆的障碍物检测和识别技术进行了综述，并分析各种障碍物检测方法。这些方法中主要包括基于立体视觉方法、基于激光雷达的方法：基于彩色机器视觉的方法及基于结构光的方法等等，同时作者指出任何一种有效的障碍物检测系统不能只依靠单一传感器进行环境感知，因此利用多种传感器信息融合技术检测智能车辆前方障碍物，是未来该领域的研究重点与难点。另外，还介绍了近几年一些研究机构在该领域的研究成果，并对所使用的一些算法进行简要的概括，为我国在智能车辆的障碍物检测领域的发展提供借鉴。