警钟长鸣: 自动驾驶行业漫长的“寒冬期”

前不久,美国电动车及能源公司特斯拉向车主推送了最新的系统更新,自动辅助驾驶系统(Autopilot)功能进一步升级,新增“相邻车道速度调整”和“自动巡航停车标志警告”功能,支持汽车根据道路上其他车辆的速度调整自身速度与感应前方的行驶状态。据推特用户上传的更新说明图片可知,“相邻车道速度调整”功能是指汽车可根据道路上其他车辆的速度来调整自身的速度。     

基于驾驶模拟实验的车道宽度影响分析

为研究上海市沿江通道越江隧道3.5 m车道的安全性,借助交通行为与协同虚拟现实实验系统(即驾驶模拟器),利用实验采集的数据,从车辆轨迹偏移量角度,分析3.5 m车道与3.75 m车道的运行安全性差异。     

嵌入式一体化车道机在高速公路收费系统的应用

车道机是高速公路收费系统的重要组成部分,主要用于对高速公路车道收费系统以及相关外围设备采集的各种信号进行控制和处理。文章介绍了嵌入式一体化车道机所具备的功能及工作原理,阐述了其技术特性,并通过与传统人工收费车道机的对比,分析了嵌入式一体化车道机的应用优势与前景。     

威伯科最新技术大观

比利时布鲁塞尔/德国汉诺威讯—威伯科(WABCO)汽车控制系统(NYSE:WBC),全球领先的商用汽车电子制动、稳定、悬挂和变速控制系统的供应商,于近期携其卡车、客车和挂车的最新技术,亮相2012年德国汉诺威商用车博览会(IAA)。这些技术标志着威伯科在商用汽车行车安全及效率技术领域的领先地位,现在让我们看看这些产品的独到之处。OnLaneTM是适用于卡车和客车的车道偏离预警系统(LDWS),标志着威伯科在先进的驾驶员辅助驾驶系统领域迈出了新的步伐。当车辆意外偏离车道时,该系统可以向驾驶员发出声光报警或座椅震动式报警(可选),从而提高行车安全。意外偏离车道是商用车最常见的事故原因之一。     

基于RSA加密算法的车道收费软件授权认证管理系统

车道收费软件授权认证管理系统是高速公路联网收费系统的一个重要组成部分。文章阐述了部署车道收费软件授权认证管理系统的目的和必要性、功能流程、授权认证流程以及RSA密钥管理方法,并介绍了车道收费软件授权认证管理系统的编程开发与应用情况。     

高速公路ETC车道防邻道及跟车干扰研究

在ETC车道系统中,相邻两个ETC车道之间的OBU与RSU相互影响,RSU与非本车道上的OBU进行通讯并完成交易,引起邻道干扰;工程实施、设备等原因引起跟车干扰,造成ETC收费失败。针对存在的问题,分析工程安装、设备原因,提出切实可行的解决方案,保证ETC车道功能的正确性、完整性,是交通运输行业节能减排绿色发展成果之一。     

高速公路收费系统功能方案设计概述

收费系统是高速公路有效实施运营、管理的重要方面,详细探讨高速公路收费系统中计算机网络系统、结算中心系统、收费中心和收费分中心系统、收费车道系统、闭路电视监视系统、UPS电源等各组成部分应具有的功能.     

货车自动驾驶技术及标准法规发展分析

目前,自动驾驶技术在乘用车领域已获得突破性发展;为提高通行效率和出行安全起到了极大的作用。自动驾驶技术在商用车领域的应用,有望较好解决高昂的人力成本和难以提高的效率等问题。然而,目前自动驾驶技术在货车的应用大多采用跟乘用车同样的标准进行规范,这在实际应用中存在着诸多的问题;例如,在FCW和AEB功能中货车在相同车速的制动距离要远大于乘用车,而其所采用标准规定的碰撞预警时间却并无多大不同,这在实际场景中存在着较大的安全隐患[1,2]。此外,货车质量和体积较大,且较多应用于长途运输,运输过程中会经历包括高温、严寒、山区等多种复杂气候场景,这些都将对货车自动驾驶技术在车辆制动效能、能耗以及多场景应用等方面提出更有针对性规范的要求。本文针对货车的应用场景特点,为其自动驾驶技术应用标准化提出了建议。     

沃尔沃推碰撞预警系统

沃尔沃卡车开发出具备应急制动功能的碰撞预警系统以避免因注意力分散而造成的严重碰撞事故。 公路事故中追尾碰撞占比很大，为此沃尔沃卡车开发出具备应急制动功能的碰撞预警系统。这套系统将能帮助驾驶员有效避免和减少追尾碰撞事故的发生，目前已经应用在沃尔沃卡车新FH系列上。     

基于车联网的高速公路安全预警系统设计研究

近年来,车联网与高速公路的结合程度加深,基于车联网模式设计安全预警系统成为可能,首先,对车联网的实现步骤与应用场景作出描述;其次,根据高速公路车辆运行特点,设计了安全预警系统,在预警系统的设计领域中,重点关注层次结构设计是否合理,同时,采取更为科学的信息获取与传输技术方案,并完成主程序的设计优化;最后,系统设计发挥了预期作用,不仅能够提升高速公路的安全性能,还能为交通领域相关课题的研究提供参考。     

Optimal location of advance warning for mandatory lane change near a two-lane highway off-ramp

Improper mandatory lane change (MLC) maneuvers in the vicinity of highway off-ramp will jeopardize traffic efficiency and safety. Providing an advance warning for lane change necessity is one of the efficient methods to perform systematic lane change management, which encourages smooth MLC maneuvers occurring at proper locations to mitigate the negative effects of MLC maneuvers on traffic flow nearby off-ramp. However, the state of the art indicates the lack of rigorous methods to optimally locate this advance warning so that the maximum benefit can be obtained. This research is motivated to address this gap. Specifically, the proposed approach considers that the area downstream of the advance warning includes two zones: (i) the green zone whose traffic ensures safe and smooth lane changes without speed deceleration (S-MLC); the start point of the green zone corresponding to the location of the advance warning; (ii) the yellow zone whose traffic leads to rush lane change maneuvers with speed deceleration (D-MLC). An optimization model is proposed to search for the optimal green and yellow zones. Traffic flow theory such as Greenshield model and shock wave analysis are used to analyze the impacts of the S-MLC and D-MLC maneuvers on the traffic delay. A grid search algorithm is applied to solve the optimization model. Numerical experiments conducted on the simulation model developed in Paramics 6.9.3 indicate that the proposed optimization model can identify the optimal location to set the advance MLC warning nearby an off-ramp so that the traffic delay resulting from lane change maneuvers is minimized, and the corresponding capacity drop and traffic oscillation can be efficiently mitigated. Moreover, the experiments validated the consistency of the green and yellow zones obtained in the simulation traffic flow and from the optimization model for a given optimally located MLC advance warning under various traffic regimes. The proposed approach can be implemented by roadside mobile warning facility or on-board GPS for human-driven vehicles, or embedded into lane change aid systems to serve connected and automated vehicles. Thus it will greatly contribute to both literature and engineering practice in lane change management.     

An integrated solution for lane level irregular driving detection on highways

Global Navigation Satellite Systems (GNSS) has been widely used in the provision of Intelligent Transportation System (ITS) services. Current meter level system availability can fulfill the road level applications, such as route guide, fleet management and traffic control. However, meter level of system performance is not sufficient for the advanced safety applications. These lane level safety applications requires centimeter/decimeter positioning accuracy, with high integrity, continuity and availability include lane control, collision avoidance and intelligent speed assistance, etc. Detecting lane level irregular driving behavior is the basic requirement for these safety related ITS applications. The two major issues involved in the lane level irregular driving identification are accessing to high accuracy positioning and vehicle dynamic parameters and extraction of erratic driving behaviour from this and other related information. This paper proposes an integrated solution for the lane level irregular driving detection. Access to high accuracy positioning is enabled by GNSS and Inertial Navigation System (INS) integration using filtering with precise vehicle motion models and lane information. The detection of different types of irregular driving behaviour is based on the application of a Fuzzy Inference System (FIS). The evaluation of the designed integrated systems in the field test shows that 0.5 m accuracy positioning source is required for lane level irregular driving detection algorithm and the designed system can detect irregular driving styles.     

Emission evaluation of inter-vehicle safety warning information systems

Driver inattentiveness is one of critical factors contributing to vehicle crashes. The inter-vehicle safety warning information system (ISWS) is a technology to enhance driver attentiveness by providing warning messages about upcoming hazards using connected vehicle environments. A novel feature of the proposed ISWS is its ability to detect hazardous driving events, such as abrupt accelerations and lane changes, which are defined as moving hazards with a higher potential of causing crashes. This study evaluated the effectiveness of the ISWS in reducing vehicle emissions and its potential for traffic congestion mitigation. This study included a field experiment that documented actual vehicle maneuvering patterns for abrupt accelerations and lane changes, which were used for more realistic simulation evaluations, in addition to normal accelerations and lane changes. Probe vehicles equipped with customized on-board units consisting of a global positioning system (GPS) device, accelerometer, and gyro sensor were used to obtain the vehicle maneuvering data. A microscopic simulator, VISSIM, was used to simulate a driver’s responsive behavior when warning messages were delivered. A motor vehicle emission simulator (MOVES) was then used to estimate vehicle emissions. The results show that reduction in vehicle emissions increased when the ISWS’s market penetration rate (MPR) and the congestion level of the traffic conditions increased. The maximum CO and CO₂ emission reductions achieved were approximately 6% and 7%, respectively, under LOS D traffic conditions. The outcomes of this study can be valuable for deriving smarter operational strategies for ISWS to account for environmental impacts.     

An on-road evaluation of connected motorcycle crash warning interface with different motorcycle types

Crash warning systems have been deployed in the high-end vehicle market segment for some time and are trickling down to additional motor vehicle industry segments each year. The motorcycle segment, however, has no deployed crash warning system to date. With the active development of next generation crash warning systems based on connected vehicle technologies, this study explored possible interface designs for motorcycle crash warning systems and evaluated their rider acceptance and effectiveness in a connected vehicle context. Four prototype warning interface displays covering three warning mode alternatives (auditory, visual, and haptic) were designed and developed for motorcycles. They were tested on-road with three connected vehicle safety applications - intersection movement assist, forward collision warning, and lane departure warning - which were selected according to the most impactful crash types identified for motorcycles. Combined auditory and haptic displays showed considerable promise for implementation. Auditory display is easily implemented given the adoption rate of in-helmet auditory systems. Its weakness of presenting directional information in this study may be remedied by using simple speech or with the help of haptic design, which performed well at providing such information and was also found to be attractive to riders. The findings revealed both opportunities and challenges of visual displays for motorcycle crash warning systems. More importantly, differences among riders of three major motorcycle types (cruiser, sport, and touring) in terms of rider acceptance of a motorcycle crash warning system were revealed. Based on the results, recommendations were provided for an appropriate crash warning interface design for motorcycles and riders in a connected vehicle environment.     

驾驶员反应时间研究

对于预警类智能辅助驾驶系统,驾驶员的反应时间是一个重要参数。本文针对不同的驾驶员类型、不同的预警方式,在实车测试平台上进行驾驶员反应时间的测量。通过实验可以得出各种情况下不同的驾驶员反应时间,该研究的成果将对预警类智能辅助驾驶系统中预警方式和预警时间的选取起到积极的作用。     

Utilizing naturalistic driving data for in-depth analysis of driver lane-keeping behavior in rain: Non-parametric MARS and parametric logistic regression modeling approaches

It is known that adverse weather conditions can affect driver performance due to reduction in visibility and slippery surface conditions. Lane keeping is one of the main factors that might be affected by weather conditions. Most of the previous studies on lane keeping have investigated driver lane-keeping performance from driver inattention perspective. In addition, the majority of previous lane-keeping studies have been conducted in controlled environments such as driving simulators. Therefore, there is a lack of studies that investigate driver lane-keeping ability considering adverse weather conditions in naturalistic settings. In this study, the relationship between weather conditions and driver lane-keeping performance was investigated using the SHRP2 naturalistic driving data for 141 drivers between 19 and 89 years of age. Moreover, a threshold was introduced to differentiate lane keeping and lane changing in naturalistic driving data. Two lane-keeping models were developed using the logistic regression and multivariate adaptive regression splines (MARS) to better understand factors affecting driver lane-keeping ability considering adverse weather conditions. The results revealed that heavy rain can significantly increase the standard deviation of lane position (SDLP), which is a very widely used method for analyzing lane-keeping ability. It was also found that traffic conditions, driver age and experience, and posted speed limits have significant effects on driver lane-keeping ability. An interesting finding of this study is that drivers have a better lane-keeping ability in roadways with higher posted speed limits. The results from this study might provide better insights into understanding the complex effect of adverse weather conditions on driver behavior.     

Using geometric primitives to calibrate traffic scenes

In this paper, we address the problem of recovering the intrinsic and extrinsic parameters of a camera or a group of cameras in a setting overlooking a traffic scene. Unlike many other settings, conventional camera calibration techniques are not applicable in this case. We present a method that uses certain geometric primitives commonly found in traffic scenes, such as straight and curved lanes, lane markings, and poles in order to recover calibration parameters. We show experimentally that these primitives provide the needed redundancy and are capable of achieving accurate results suitable for most traffic monitoring applications.     

Modeling the impacts of mandatory and discretionary lane-changing maneuvers

In this paper, a novel mesoscopic multilane model is proposed to enable simultaneous simulation of mandatory and discretionary lane-changing behaviors to realistically capture multilane traffic dynamics. The model considers lane specific fundamental diagrams to simulate dynamic heterogeneous lane flow distributions on expressways. Moreover, different priority levels are identified according to different lane-changing motivations and the corresponding levels of urgency. Then, an algorithm is proposed to estimate the dynamic mandatory and discretionary lane-changing demands. Finally, the lane flow propagation is defined by the reaction law of the demand–supply functions, which can be regarded as an extension of the Incremental-Transfer and/or Priority Incremental-Transfer principles. The proposed mesoscopic multilane cell transmission model is calibrated and validated on a complex weaving section of the State Route 241 freeway in Orange County, California, showing both the positive and negative impact of lane changing maneuvers, e.g., balancing effect and capacity drop, respectively. Moreover, the empirical study verifies that the model requires no additional data other than the cell transmission model does. Thus, the proposed model can be deployed as a simple simulation tool for accessing dynamic mesoscopic multilane traffic state from data available to most management centers, and also the potential application in predicting the impact of traffic incident or lane control strategy.     

Lane change algorithm for autonomous vehicles via virtual curvature method

This paper addresses the lane changing problem of autonomous vehicles when there is no road infrastructure support. The autonomous vehicle should drive from the current lane to the adjacent lane in the absence of a reference path to guide the vehicle to the new lane. We suggest an algorithm that incorporates a virtual road curvature with bicycle model for lane change guidance. As the name suggests, the virtual road curvature does not physically exist. It is a user assigned radius of a curved path which connects the current lane to the adjacent lane. Since the lateral sensor readings during lane changing maneuver are erroneous, the steering angle along with the virtual curvature is fed into a bicycle model to estimate the lateral position during the transition to the next lane. Details of the algorithm and the virtual road curvature determination are presented in the paper. In contrast to other lane changing methods, controller switching is not required and the same controller is for both lane keeping and lane changing. The algorithm is verified experimentally and the results are comparable with lane changing with physical transition lane.