Autonomous vehicle perception: The technology of today and tomorrow

Perception system design is a vital step in the development of an autonomous vehicle (AV). With the vast selection of available off-the-shelf schemes and seemingly endless options of sensor systems implemented in research and commercial vehicles, it can be difficult to identify the optimal system for one’s AV application. This article presents a comprehensive review of the state-of-the-art AV perception technology available today. It provides up-to-date information about the advantages, disadvantages, limits, and ideal applications of specific AV sensors; the most prevalent sensors in current research and commercial AVs; autonomous features currently on the market; and localization and mapping methods currently implemented in AV research. This information is useful for newcomers to the AV field to gain a greater understanding of the current AV solution landscape and to guide experienced researchers towards research areas requiring further development. Furthermore, this paper highlights future research areas and draws conclusions about the most effective methods for AV perception and its effect on localization and mapping. Topics discussed in the Perception and Automotive Sensors section focus on the sensors themselves, whereas topics discussed in the Localization and Mapping section focus on how the vehicle perceives where it is on the road, providing context for the use of the automotive sensors. By improving on current state-of-the-art perception systems, AVs will become more robust, reliable, safe, and accessible, ultimately providing greater efficiency, mobility, and safety benefits to the public.     

Virtual 3D city model as a priori information source for vehicle localization system

This paper aims at demonstrating the usefulness of integrating virtual 3D models in vehicle localization systems. Usually, vehicle localization algorithms are based on multi-sensor data fusion. Global Navigation Satellite Systems GNSS, as Global Positioning System GPS, are used to provide measurements of the geographic location. Nevertheless, GNSS solutions suffer from signal attenuation and masking, multipath phenomena and lack of visibility, especially in urban areas. That leads to degradation or even a total loss of the positioning information and then unsatisfactory performances. Dead-reckoning and inertial sensors are then often added to back up GPS in case of inaccurate or unavailable measurements or if high frequency location estimation is required. However, the dead-reckoning localization may drift in the long term due to error accumulation. To back up GPS and compensate the drift of the dead reckoning sensors based localization, two approaches integrating a virtual 3D model are proposed in registered with respect to the scene perceived by an on-board sensor. From the real/virtual scenes matching, the transformation (rotation and translation) between the real sensor and the virtual sensor (whose position and orientation are known) can be computed. These two approaches lead to determine the pose of the real sensor embedded on the vehicle. In the first approach, the considered perception sensor is a camera and in the second approach, it is a laser scanner. The first approach is based on image matching between the virtual image extracted from the 3D city model and the real image acquired by the camera. The two major parts are: 1. Detection and matching of feature points in real and virtual images (three features points are compared: Harris corner detector, SIFT and SURF). 2. Pose computation using POSIT algorithm. The second approach is based on the on–board horizontal laser scanner that provides a set of distances between it and the environment. This set of distances is matched with depth information (virtual laser scan data), provided by the virtual 3D city model. The pose estimation provided by these two approaches can be integrated in data fusion formalism. In this paper the result of the first approach is integrated in IMM UKF data fusion formalism. Experimental results obtained using real data illustrate the feasibility and the performances of the proposed approaches.     

基于模型预测控制的智能车轨迹跟踪控制研究

为了完成智能车的轨迹跟踪,提出一种基于模型预测控制的轨迹跟踪方法,利用将运动学模型这个非线性系统线性化的方案,来获得必须的线性时变系统,采取模型预测控制的三要素来设计控制器。并且基于MPC在控制过程中能增加多种约束的优点,建立基于车辆运动学模型的约束做轨迹跟踪仿真实验,最后,基于山东理工大学智能车平台上GPS提供的定位信息,在校园中采集路线并对前提规划好的的轨迹进行实车验证。实验结果表明:基于MPC算法所设计的控制器能快速且稳定地跟踪期望轨迹。     

转换轨长度及位置探讨

通过对列车在转换轨上的定位、筛选、轮径校准过程的分析,进行了转换轨长度及位置的探讨,提出了转换轨长度按200m设计和靠车辆段设计转换轨的建议,便于地铁信号设计人员开展转换轨的设计。     

国产化城轨交通列车自动驾驶系统车载设备研究与设计

介绍了国产化城市轨道交通列车自动驾驶（ATO）系统车载设备的研究与设计。具体描述了国产化ATO的原理、结构及自动驾驶原理,同时着重介绍了ATO车载设备的设计与实现。通过测试表明,ATO能够在ATP的监控下很好地按照给定的策略行车,并达到较好的停车精度。     

德州市构建BRT系统的适用性分析

共交通模功能定位道路条件快速公交系统（BRT）是一种投资低、建设周期短、公共运输服务水平高、环保节能的新型城市公式,的基等）,被誉为“城市轨道交通替代方案”、“未来公共交通解决方案”。本文在确定德州市BRT系统的础上,通过分析外部影响因素（城市经济、城市空间结构和政策等）和内部影响因素（客流、对德州市构建BRT系统的适用性进行了初步分析。     

磁浮交通运行控制系统中通信子系统的分析和研究

介绍并分析了磁浮交通运行控制系统中通信子系统的功能及网络构成,就如何对关键技术进行国产化进行了研究。     

调车监控系统车列定位技术的研究

从无线调车机车信号和监控系统中采用的各种不同的车列初始定位方法出发,对调车车列位置的跟踪算法进行研究,并通过对地面设备和车载设备所能提供的车列位置信息的分析,在保证故障-安全的前提下完善车列位置跟踪算法,以进一步提高系统的可用性。     

君威轿车本土化快速自主开发及其关键技术

泛亚汽车技术中心在多年引进消化国际先进技术基础上,以中高级轿车产品为突破点,集中对市场分析与本土化产品定义、产品平台的再次开发、多品种混线生产与垂直换型、数字化设计与实验分析、产品开发过程的全球协同管珲等5个方而的火键技术进行了研究和开发,形成了完整的中高级轿车本土化快速自主开发平台、技术能力和管理体系,并成功应用到君威系列轿车开发中,使合资企业在自主开发上开拓出新的途径。     

动车组洗车机的国产化研究及应用

对动车组洗车机引进技术的国产化历程进行介绍,从技术因素、环境因素、用户要求等方面分析引进技术的不足,基于国内车型多、使用环境复杂、用户要求严格等条件,根据设计总结和实际工程经验,在充分消化吸收引进技术的基础上,在兼容性、功能性、安全性、可靠性、标准化设计等方面进行必要的改进,实现动车组清洗机生产技术的国产化改进和技术性能的完善。通过技术改进,不仅提高产品质量,也提高制造、安装、调试的效率,操作、维修方便,具有良好的经济效益。