极限工况下无人驾驶车辆运动规划策略研究

针对无人驾驶车辆在极限工况下的运动规划问题,提出一种适应极限工况的无人驾驶车辆运动规划策略。首先,建立了准确描述车辆运动的动力学模型,并采用修正的非线性轮胎模型反映轮胎与不同路面之间的动力学特性;其次,提出一种基于安全制动距离的自适应势场模型,以适应极限工况下外界条件与车辆参数的变化;再次,考虑到车辆在极限工况下易发生横向失稳,设计出横向稳定性指标(Lateral stability index,LSI)作为关键优化参数,并展开车辆横向稳定性分析;然后,基于模型预测控制方法(Model predictive control,MPC),将极限工况下的运动规划问题转化为多目标优化问题;最后,构建出PreScan-Simulink-CarSim联合仿真平台,并在冰雪路面等多种极限工况下对所提出的运动规划策略进行了验证。仿真结果表明,该策略有效提升了无人驾驶车辆在极限工况下的安全性与稳定性。

Research on the Motion Planning Strategy for Autonomous Vehicles in Extreme Conditions

The research content is the motion planning of autonomous vehicles in extreme conditions, and a motion planning strategy for autonomous vehicles adapting to extreme conditions is proposed. Firstly, a dynamic model which can accurately describe the motion of vehicle is established, and a modified nonlinear tire model is used to reflect the mechanical properties between the tire and road surfaces. Secondly, an adaptive potential field model based on safe braking distance is proposed to adapt the change of external conditions and vehicle parameters in extreme conditions. Furthermore, considering the vehicle prone to lateral instability under extreme conditions, the lateral stability index(LSI) is designed as the key optimization parameter, and the lateral stability of vehicle is analyzed. Then, based on model predictive control(MPC) method, the motion planning problem in the extreme conditions is transformed into a multi-objective optimization problem. Finally, the PreScan-Simulink-CarSim co-simulation platform is built, and the proposed motion planning strategy is verified in various extreme conditions, such as snow and ice covered road. The simulation results show that the strategy effectively improves the safety and stability of the autonomous vehicle in the extreme conditions.