基于车-路协同的汽车主动安全性关键技术研究

The project focuses on the disadvantage of current vehicle active safety which depends on its equipments heavily. Once the safety space and physical conditions are restricted, the capability of these equipments for vehicle active safety becomes invalid. So, a new technique based on Vehicle-Infrastructure cooperative driving for vehicle active safety is proposed in the project. Firstly, the important point is to know vehicles' accurate positions. Accurate vehicle position will be realized by two ways with the application of GPS. The first is achieved by developing a revised algorithm based on multi-GPS architecture which is installed in vehicle to acquire accurate vehicle position. The other is by correction data communicated in On Board Unit (OBU) and Road Side Unit (RSU) by cooperative driving. OBU in vehicles can receive the correction data sent by RSU. The correct data is computed by the embedded unit in RSU and the raw data is from a high precise GPS equipped in RSU. OBU uses the correct date to obtain accurate position of its vehicle. With the help of cooperative driving, the information of accurate vehicle position can be exchanged between vehicles. So, vehicle with an OBU can obtain the position information of the neighborly vehicles clearly and certainly. Then, the decision-making mechanism for vehicle active safety under cooperative driving will be investigated based on the known vehicles' accurate positions. The point is to study drivers’ intention of the near-by vehicles based on the known dynamic parameters of these vehicles. Then, a strategy to predict the dynamic space constraints comprised of these vehicles will be developed for decision-making. A polynomial curve accounted for dynamic vehicle parameters will be explored and used in local path planning. The next research aims at the characteristics of an active steering system consists of planetary gear structure for vehicle lane-changing. Furthermore, A control strategy base on dynamic vehicle parameters for tracking the local path of vehicle lane-changing will be developed. Finally, a set of experiments using real vehicles with a simple Vehicle-Infrastructure cooperative driving architecture will be conducted to illustrate the integration of the above mentioned and developed algorithms or technologies and validate the advantages of the architecture in improving vehicle active safety. The project will give much useful foundation for further research on vehicle active safety by using cooperative driving, intelligent vehicles and its application in ITS.

项目针对当前汽车主动安全仅依靠自车，当空间及外界条件限制，无法进一步提升其性能的不足，拟构建基于车-路协同的汽车主动安全性技术。首先，利用车-路的信息交流来实现精确的车辆定位。重点研究基于多个GPS或有高精度GPS嵌入的路侧单元来发送修正数实现精确车辆定位的方法，为车辆主动安全控制提供可能。再进行自车主动安全的决策机制及控制策略的研究。研究基于周围车辆信息的多车驾驶员意图的识别方法，预测多车的动态空间边界，构建含多车系统的决策模式，摸索计及动态边界的多项式局部路径规划方法。之后进行包含多车动态信息的主动转向系统以及差动制动集成的研究。重点探索一种行星齿轮机构的主动转向系统的路径跟踪控制方法。最后在一种简单的车-路协同驾驶架构下进行上述关键技术的集成，完成实车试验，为后续的基于车-路协同的汽车主动安全性技术研究、智能网联汽车以及融入智能交通系统提供理论基础。

智能网联汽车将传统汽车通过信息和通信技术进行赋能，使得提升车辆的运行安全和环境保护等成为可能。. 项目针对智能网联汽车基于车路协同系统在提高主动安全性方面开展了研究。整个研究内容包含三个层面，一为车辆底盘稳定性控制，其次围绕智能汽车的感知-决策-控制等三个环节进行了主要技术的研究，第三则在实现车路协同的数据交换装置上开展了设计。. 在提高底盘稳定性方面，提出参考转角概念，进行了基于主动转向的稳定控制策略的研究。基于双阈值进行了底盘附加横摆力矩控制算法的设计。在智能车辆的感知上进行了多GPS定位精度的矫正方法的研究。还研究了基于视频信息提升车辆识别精度以及分析驾驶意图的相关算法。通过将传统的电子地图进行拓展，给出了一种基于车道信息的高精度电子地图制作方法。针对车辆的运行特征，将车辆的驾驶行为统一为广义的变道行为，实现车道级的全局路径规划方法。基于车道和Frenet坐标系将复杂的车辆间距问题降维为一维问题，提出了基于目标车道的驾驶行为决策控制策略。在策略中还引入了协同驾驶，以提升车辆的行驶安全性。在对智能车辆的执行机构特性以及控制器设计的基础上，实现了多种控制方法下的车辆运动控制。在实现车路协同功能方面，基于巴龙5000进行了OBU的设计和制作。. 基于对相关算法的验证，进行了模块化的仿真平台和试验平台的构建。通过仿真试验和实车试验验证了提出的算法的可行性。项目的研究工作在一定程度上实现了简易可行的车路协同系统的集成，所提出的控制算法可以满足车路协同车辆行为的测试。其中，车路系统能实现基于JSON文件的车辆、道路等数据的交换。给出的车道级全局路径规划方法和驾驶行为控制策略能实现基本交通规则下车辆变道行为的决策。智能车辆的侧向运动控制精度满足了典型道路和工况的运行精度要求。由于模块化的设计优势，获得的集成系统能够为后续的智能网联汽车关键技术的研发以及验证提供有力的支撑。