基于三维虚拟危险势能图的道路车辆紧急避让驾驶行为决策与G矢量运动协调控制研究

项目名称： 基于三维虚拟危险势能图的道路车辆紧急避让驾驶行为决策与G矢量运动协调控制研究

项目编号： No.61304189

项目类型： 青年科学基金项目

立项/批准年度： 2014

项目学科： 自动化技术、计算机技术

项目作者： 冀杰

作者单位： 西南大学

项目金额： 24万元

中文摘要： 由道路交通事故引起的生命财产损失已经成为影响我国社会生产、居民安全的重要原因之一，车辆主动安全技术的提出和实践能够有效降低道路交通事故的发生率，但目前对紧急避让工况下的车辆行驶安全状态表达决策以及面向主动安全性的底盘子系统协调控制机理尚未阐明。因此，基于信息融合技术对道路车辆的危险行驶状态信息及动力学参数进行感知和估计，并以车辆运动状态、道路结构参数及V2X危险关系为变量构建动态的三维虚拟危险势能图，实现对车辆行驶安全状态的综合描述和定量评估。同时，对典型的紧急避让行为及意图进行判断和识别，基于有限状态机理论构建符合人类逻辑思维过程的驾驶行为决策系统。另外，基于三维虚拟危险势能图及综合加速度G矢量指数构建车辆运动学与动力学安全综合监督指标，并通过整车动力学任务的分配机制及底盘子系统的协调控制，从而保证驾驶操作指令的准确执行及综合监督指标的实现，为降低道路交通事故发生率提供一条新的研究思路。

中文关键词： 智能车辆；危险势能场；路径规划跟踪；模型预测控制；主动差速器

英文摘要： Vehicle safety in transportation is becoming increasingly important because of the great volume of traffic accidents on the roads, and active safety has been used to assist the driver in avoiding accidents by preventing unstable vehicle behavior. This project develops a threat assessment algorithm to improve the safety of decision making of autonomous and human-operated vehicles navigating in dynamic and uncertain environments, where the source of uncertainty is in the predictability of the nearby vehicles' future trajectories.The algorithm implements a data-fusion architecture of multiple sensors and related applications for different driving conditions and vehicle's surround information. Then, A dangerous potential map framework for active safety technology that seeks to prevent accidents from obstacle avoiding is presented in this project. The method is expressed in the form of an algorithm for obstacle avoidance based on the 3D virtual potential field map. In the following step a coordinated controller for chassis system is designed in order to control the vehicle along designed trajectory and improve the stability in accident avoiding conditions. Digital simulation results obtained for the complete dynamic vehicle model and experiments on road well validate the algorithm. Depending on the proposed algorith

英文关键词： Intelligent Vehicle；Dangerous potential field；Path Planning and tracking；Model Predictive Control；Active differential