Street smarts for boss

The Urban Challenge [1] was an autonomous vehicle competition sponsored by the U.S. Defense Advanced Research Projects Agency (DARPA) in November 2007. Contestant robots were required to autonomously execute a series of navigation missions through a simplified urban environment consisting of roads, intersections, and parking lots while obeying road rules and interacting safely and correctly with other traffic. In contrast to previous DARPA challenges, which focused on rough-terrain navigation [2], [3], this competition required a system capable of complex autonomous behaviors, such as waiting for precedence at an intersection or passing a slow-moving vehicle on a multilane road.     

车路协同路侧感知融合方法的研究

随着智能网联汽车技术和产业的不断发展,智能网联汽车逐渐成为人们交通出行的选择之一。但受智能网联汽车自身环境感知系统对特定道路交通场景信息处理的局限,无法实现在所有行驶工况下安全高效的运行,其需车路协同路侧感知技术的辅助方能更安全高效的运行。海量的车路协同感知数据是城市道路和高速公路车路协同、运行分析和科学管理的宝藏,理解和分析这些数据是车路协同路侧感知融合的关键。面对车路协同路侧多传感器的不同数据,如何高效准确地挖掘和提取雷达、视频在不同时间、不同空间维度的数据,实现对重点交通场景(如视野盲区、急转弯道、隧道、桥梁)和交通事件、环境、设施安全等的雷达、视频数据进行快速融合检测、识别与检索,通过蜂窝车联网C-V2X网络在一定时延范围内有效地将路侧感知融合结果数据发送给智能网联汽车,确保其安全高效的行驶,是面向智能网联汽车辅助驾驶的车路协同路侧感知融合的关键问题。基于智能网联汽车其自身环境感知能力,对道路智能基础设施感知网络中的多传感器融合方法进行研究分析,提出了基于误差方差的多传感器融合算法,与非智能道路相比,其效率更高,更加智能化,可有效解决道路交通运行环境中存在的常见问题,为人们提供更加安全、高效、优质的交通出行服务。     

基于遗传算法和数学形态学的道路识别方法

随着将智能车辆适用环境拓展到乡村道路、荒漠、越野环境等非结构道路,机器视觉导航的难度进一步加大,成为当今人们研究的重点。文章针对智能车辆行驶的复杂道路环境提出一种新的道路检测与识别的算法。该算法采用了遗传算法对道路图像进行分割,并在此基础上对已分割图像进行数学形态学处理,然后利用canny算子进行道路边缘检测,最终得到准确清晰的道路边界信息。实验结果表明,该方法可以有效地分割出道路区域,对多种复杂路况具有良好的适应性。     

Road-edge delineation in rural areas: effects on driving behaviour

When driving on lower-category Dutch rural roads without any delineation, drivers are likely to drift off the road with their right-side wheels, thus incurring damage to the pavement edge or even leading to accidents. In two experiments, two types of road-edge delineation, with continuous or dashed edge lines, were compared with two control roads without lines or with only a dashed line on the road axis. The first experiment consisted of non-obtrusive video recordings of passing traffic. Vehicle position on the experimental roads was more to the road's centre than on the control roads. The second experiment was a driving test with an instrumented vehicle, during daytime lighting and during darkness. Again, vehicle lateral position was more central on the experimental roads, especially during darkness. Subjects could safely pass oncoming vehicles. Driving speed increased on the experimental roads compared with the unlined control road, but not beyond speeds found on the axis-lined control road. Driver's mental effort while driving over the experimental roads did not differ from the effort while driving over the control roads. Subjectively rated effort was higher for the unlined control road than for the three other roads. Subjects preferred the edge-lined roads to the unlined control road, but not more than the axis-lined control road. It was concluded that edge-lines may provide a simple and effective way of inducing a more favourable lateral position on rural roads without having negative effects on subjective appraisal, driving performance or mental workload.     

Road-edge delineation in rural areas: effects on driving behaviour

When driving on lower-category Dutch rural roads without any delineation, drivers are likely to drift off the road with their right-side wheels, thus incurring damage to the pavement edge or even leading to accidents. In two experiments, two types of road-edge delineation, with continuous or dashed edge lines, were compared with two control roads without lines or with only a dashed line on the road axis. The first experiment consisted of non-obtrusive video recordings of passing traffic. Vehicle position on the experimental roads was more to the road's centre than on the control roads. The second experiment was a driving test with an instrumented vehicle, during daytime lighting and during darkness. Again, vehicle lateral position was more central on the experimental roads, especially during darkness. Subjects could safely pass oncoming vehicles. Driving speed increased on the experimental roads compared with the unlined control road, but not beyond speeds found on the axis-lined control road. Driver's mental effort while driving over the experimental roads did not differ from the effort while driving over the control roads. Subjectively rated effort was higher for the unlined control road than for the three other roads. Subjects preferred the edge-lined roads to the unlined control road, but not more than the axis-lined control road. It was concluded that edge-lines may provide a simple and effective way of inducing a more favourable lateral position on rural roads without having negative effects on subjective appraisal, driving performance or mental workload.     

Effect of road layout and road environment on driving performance,drivers' physiology and road appreciation

Abstract

Infrastructural changes were implemented on rural 80km/h roads in The Netherlands in an effort to reduce speeding. The road infrastructure changes were designed to produce discomfort for the speeding driver by providing noxious auditory and haptic feedback. On experimental roads, smooth-surface road width was reduced by using blocks of gravel chippings placed along the centre line and at intervals on road edges. It was predicted that these changes would increase mental load while driving, and thereby decrease speeding. In a field experiment 28 subjects drove an instrumented vehicle over experimental and control roads. A decrease in driving speed and swerving behaviour was found on the experimental roads, and this was coupled with a decrease in heart rate variability, consistent with an increase in mental load. Roads in two different road-side environments (woodland vs. moorland) were also tested. There were differences in driver appraisal of the two environments, but no interactions were observed between these appraisals and driving performance on the experimental roads. It is concluded that the infrastructural measures have a useful role to play in road safety through a reduction in driver speeding.     

基于改进PSO_LSSVM机械结构疲劳裂纹扩展预测

为了利用仿真技术研究智能车在行进时的实时测控过程, 先由法线法构建近似等距曲线作为仿真道路的边界; 再利用非线性规划给出了道路内满意矩形安全邻域的寻找方法; 通过道路边界变异, 构建了有障碍道路的仿真模型。整个过程形成了一套基于矩形安全邻域的智能车移动仿真方法, 并对智能车实时搜索矩形安全邻域及据之决策行进的过程进行了仿真。结果表明, 该方法可以实时规划智能车的行驶路线, 有效地应对突发行驶障碍等问题。     

三维虚拟场景在车辆导航系统中的应用

城市道路交通网络的日益复杂,使得现有的二维电子地图无法直观的反映道路之间的交错关系,基于二维电子地图的车辆导航系统在复杂的道路交叉口的路径引导也显得无能为力.提出建立复杂道路口的三维虚拟场景和实现场景的指定路径漫游,并以立交桥为例在VC十 6.0中实现二维电子地图与三维虚拟场景的互响应,充分结合二维电子地图在车辆导航过程中的整体性、宏观性与局部三维虚拟场景的直观易懂性等优点,提高现有的车辆导航系统的路径引导效率.     

Vision and navigation for the Carnegie-Mellon Navlab

A distributed architecture articulated around the CODGER (communication database with geometric reasoning) knowledge database is described for a mobile robot system that includes both perception and navigation tools. Results are described for vision and navigation tests using a mobile testbed that integrates perception and navigation capabilities that are based on two types of vision algorithms: color vision for road following, and 3-D vision for obstacle detection and avoidance. The perception modules are integrated into a system that allows the vehicle to drive continuously in an actual outdoor environment. The resulting system is able to navigate continuously on roads while avoiding obstacles     

一种低成本动态车载导航坡度识别方法

针对车载导航系统对行驶过程中道路坡度、相对高度变化识别的需要,提出了1种基于里程仪、单轴加速度计的坡度与相对高度估计算法,克服了传统倾角仪无法在加速运动下测量的不足,能够满足车辆任意运动状态下的测量要求,并在此基础上设计了与倾角仪测量简单融合算法与坡度行车判别策略,可以实现坡度行车的准确快速判断与测量,为复杂情况下车载导航仪道路自动判断提供有力支持。     

.基于GIS的城市道路交通流信息发布系统应用研究*

根据基于GPRS/GPS/GSM浮动车交通流信息采集系统实时采集的交通流瞬时速度、路段速度、车辆定位等信息,借助面向对象技术、GIS技术、数据库技术设计、开发了基于GIS的城市交通流信息发布系统,系统采用数据库中的存储过程技术处理海量交通流信息与GIS信息等数据表的多表连接,实现了高效率信息查询,并融合GIS专题地图技术和数据库技术实现浮动车辆历史轨迹动态回放、交通流路段速度查询、可视化显示。开发的系统在天津市部分路段进行试验,系统能稳定、可靠地实现交通流信息发布,并能快速有效查询城市道路交通流信息及识别城市道路网络路况瓶颈,可以辅助交通管理者进行交通管理决策。     

引导电动汽车用户驾驶行为的充电导航策略

电动汽车充电导航便于用户合理选择充电站,降低用户自身的时间成本和经济成本,缓解配电网端的负荷压力.在电网分时电价的基础上,考虑电动汽车充电路径的选择与车主的驾驶行为密切相关,通过对电动汽车的负荷设备分类建模,根据不同设备类型的重要程度及用户的电动汽车实际工况和地形因素,利用遗传算法分析最佳出行路径,提出以时间成本与经济成本之和最优为目标,引导用户驾驶行为的充电导航策略.在20kmtimes10km含3个充电站的区域内,通过3种不同充电导航策略仿真结果对比,验证所提出的导航策略的可行性和有效性.     

基于步行GPS轨迹的路网提取方法

准确提取和及时更新路网信息,对于道路规划和车辆导航等方面至关重要。目前,基于GPS轨迹的路网提取方法一般是从浮动车或出租车的GPS轨迹中挖掘城市主干路网。然而,现有方法忽略了小路的自动提取,它对于抗震救灾、小区导航或乡村游览等场合非常重要。因此,本文提出基于步行GPS轨迹的路网提取方法,分为数据预处理、道路中心线生成和路网精度评价3个部分。其中,先后采用轨迹点聚类、聚类点分割和中心线拟合等方法生成道路中心线。通过自行采集的步行GPS数据进行实验,结果表明,本文方法能够准确提取路网,覆盖率可达96.21%,而误检率仅3.26%;并且能够提取小路和更新路网。     

Automatic LightBeam Controller for driver assistance

In this article, we present an effective system for detecting vehicles in front of a camera-assisted vehicle (preceding vehicles traveling in the same direction and oncoming vehicles traveling in the opposite direction) during night-time driving conditions in order to automatically change vehicle head lights between low beams and high beams avoiding glares for the drivers. Accordingly, high beams output will be selected when no other traffic is present and will turn low beams on when other vehicles are detected. In addition, low beams output will be selected when the vehicle is in a well lit or urban area. LightBeam Controller is used to assist drivers in controlling vehicle’s beams increasing its correct use, since normally drivers do not switch between high beams and low beams or vice versa when needed. Our system uses a B&W forward looking micro-camera mounted in the windshield area of a C4-Picasso prototype car. Image processing techniques are applied to analyse light sources and to detect vehicles in the images. Furthermore, the system is able to classify between vehicle lights and road signs reflections or nuisance artifacts by means of support vector machines. The algorithm is efficient and able to run in real time. The system has been tested with different video sequences (more than 7 h of video sequences) under real night driving conditions in different roads of Spain. Experimental results, a comparison with other representative state of the art methods and conclusions about the system performance are presented.     

Finding multiple lanes in urban road networks with vision and lidar

This paper describes a system for detecting and estimating the properties of multiple travel lanes in an urban road network from calibrated video imagery and laser range data acquired by a moving vehicle. The system operates in real-time in several stages on multiple processors, fusing detected road markings, obstacles, and curbs into a stable non-parametric estimate of nearby travel lanes. The system incorporates elements of a provided piecewise-linear road network as a weak prior. Our method is notable in several respects: it detects and estimates multiple travel lanes; it fuses asynchronous, heterogeneous sensor streams; it handles high-curvature roads; and it makes no assumption about the position or orientation of the vehicle with respect to the road. We analyze the system’s performance in the context of the 2007 DARPA Urban Challenge. With five cameras and thirteen lidars, our method was incorporated into a closed-loop controller to successfully guide an autonomous vehicle through a 90 km urban course at speeds up to 40 km/h amidst moving traffic.     

一种基于道路知识的矢量地图数据校正方法

准确完整的交通矢量地图是车辆导航、路径寻优等应用工作的良好基础.为了解决交通矢量地图的错误数据信息问题,在分析城市道路分布特点的基础上,提出了一种基于道路知识的矢量地图数据校正方法.将矢量数据信息中普遍存在的错误进行分类,通过对城市道路设计规范和城市道路相关知识的归纳,抽取出对各种类型错误的判定规则和校正算法,为矢量地图的校正提供了一种较新的思路.实验结果表明,该算法能够快速有效的检测和校正矢量地图数据信息中存在的各类问题,能够保证矢量地图拓扑的准确性和完整性.     

Estimation of Vehicle Pose and Position with Monocular Camera at Urban Road Intersections

With the rapid development of urban, the scale of the city is expanding day by day. The road environment is becoming more and more complicated. The vehicle ego-localization in complex road environment puts forward imperative requirements for intelligent driving technology. The reliable vehicle ego-localization, including the lane recognition and the vehicle position and attitude estimation, at the complex traffic intersection is significant for the intelligent driving of the vehicle. In this article, we focus on the complex road environment of the city, and propose a pose and position estimation method based on the road sign using only a monocular camera and a common GPS (global positioning system). Associated with the multi-sensor cascade system, this method can be a stable and reliable alternative when the precision of multi-sensor cascade system decreases. The experimental results show that, within 100 meters distance to the road signs, the pose error is less than 2 degrees, and the position error is less than one meter, which can reach the lane-level positioning accuracy. Through the comparison with the Beidou high-precision positioning system L202, our method is more accurate for detecting which lane the vehicle is driving on.     

基于改进K—Means算法的车辆识别方法

车辆的检测和识别一直是道路监控、安全辅助驾驶、车辆自主导航等领域的重要研究内容。文中基于机器视觉方法,在Lab颜色空间通过对经典K—Means算法的聚类中心、聚类数和距离测度三方面的改进实现了L分量的聚类,从而达到图像分割的目的。提取图像的矩形度、Hu矩和Affine矩的特征,针对不同的车型建立各自的模板,利用改进Hu不变矩和仿射不变矩的组合不变矩对分割后图像进行车型的识别。实验结果表明,文中提出的方法对于复杂环境下的车辆检测和识别具有良好的可靠性和鲁棒性。     

基于激光散射特性的路面识别智能车设计

为了实现自主驾驶车辆的不同路面识别功能,该文首先设计制作了电驱动自动驾驶智能车,包含基于STC12C5A60S2芯片的主控电路,7.2 V转5 V^6 V电源分配模块,基于L298芯片的驱动电路,舵机HG14-M实现转向功能,欧姆龙E6A2-CW3C编码器实现速度反馈,无线模块NRF24L01与上位机通讯。进行激光后向散射特性理论研究与实验,制作以BH1750FVI为核心的高分辨率数字型光强度采集模块,设计安装车载激光后向散射测量装置,单片机与上位机LabVIEW监控系统进行无线通讯,实现不同路面识别。实验结果表明,智能车可以对不同粗糙度路面进行区分。该方法对于提高自动驾驶车辆的安全性具有重要意义。     

Fuzzy model of vehicle delay to determine the level of service of two-lane roads

The level of service (LOS) on two-lane highways and, therefore, the quality of traffic flow, is currently estimated based on the delay of the vehicles and, in certain types of roads, the average travel speed. Speed is relatively easy to measure. However, it is important, and not so simple, to determine whether a vehicle is delayed. Traditional methods, generally based on quantitative measurements of average time between vehicles and thresholds, fail to take into account the inherent vagueness of the driving process. In this paper, we have developed a fuzzy model that gives a new and reliable method for determining such vehicle state on two-way two-lane roads, based on drivers’ perceptions. The proposed system is composed of seven fuzzy subsystems that take into account imprecise knowledge, human factors, and subjective perceptions regarding the road, the car, the driver, environmental conditions, etc. Simulation results of the system have been successfully compared with the behavior of two-lane road drivers who were interviewed. The level of service of these facilities is obtained using the estimated vehicle delay state and the overtaking maneuver. Therefore, this proposal makes it possible to introduce these existing driving experiences into LOS assessment and accordingly, it is potentially a step forward since LOS must be related, by definition, to user experience. These results could be used in future frameworks. In addition, an extension of the possible states of a vehicle is defined. This approach takes into account the drivers point of view regarding overtaking desire and in this sense, it is closer to reality.