面向智能网联车辆碰撞风险规避的互动速度障碍算法

Reciprocal velocity obstacle algorithm for collision risk avoidance of intelligent connected vehicles

针对多智能车辆协同驾驶中的动态避碰问题,构建了一种面向智能网联车辆碰撞风险检测与协同避碰路径规划的互动速度障碍算法;基于人工势场理论构建了车辆碰撞风险势场,量化了车辆碰撞风险强度与碰撞风险区域;基于车辆驾驶行为交互作用构建了互动速度障碍算法,确定了冲突车辆碰撞风险的协同规避条件与规则;基于车辆动力学约束构建了动态窗口法,确定了碰撞风险规避可行速度解集;基于模型预测控制原理,应用最优化理论构建了车辆碰撞风险规避路径规划模型;通过构建智能网联环境下单冲突车辆、多冲突车辆、瓶颈区冲突车流避碰仿真场景,测试了提出的碰撞风险规避算法的有效性,并与其他避碰算法进行了控制效果对比。研究结果表明:相较于其他对比算法,互动速度障碍算法控制下的安全性能提升了8.6%以上,效率性能提升了9.6%以上,说明提出的互动速度障碍算法通过协同冲突车辆的避碰行为可有效降低冲突车辆避碰速度与轨迹波动,可有效规避非线性速度与轨迹冲突车辆间的碰撞冲突,并可避免瓶颈区多车辆碰撞事故与明显车流波动;在瓶颈区大范围车辆冲突中,相较于其他避碰算法,提出的避碰算法可使车辆的通行效率提升10.42%,使车辆的碰撞风险降低47.32%。由此可见,该算法在协同大规模冲突车辆的避碰行为、降低车辆碰撞风险与运行延误上具有良好性能。

A reciprocal velocity obstacle(RVO)algorithm for collision risk detection and collaborative path planning for collision avoidance of intelligent connected vehicles was constructed to address the dynamic collision avoidance in the collaborative driving among multiple intelligent vehicles.Based on the artificial potential field(APF)theory,a vehicle collision risk potential field(CRPF)was built to quantify both the collision risk intensity and risk area.According to the interactive effect of vehicle driving behavior,an RVO algorithm was constructed to determine the conditions and rules for collaborative collision risk avoidance among conflicting vehicles.Based on the vehicle dynamics constraints,a dynamic window approach was established to identify the feasible velocity solution set for collision risk avoidance.Based on the principle of model predictive control,the optimization theory was employed to build a path planning model for the vehicle collision risk avoidance.The effectiveness of the proposed collision risk avoidance algorithm was tested and compared with other collision avoidance algorithms by constructing the collision avoidance simulation scenarios for the single conflicting vehicle,multiple conflicting vehicles,and conflicting traffic flow in bottleneck areas under an intelligent connected environment.Research results show that compared to other comparative algorithms,the security performance and efficiency performance of the RVO algorithm improves by more than 8.6%and 9.6%,respectively,indicating that the proposed RVO algorithm can effectively reduce the collision avoidance velocity and trajectory fluctuations for conflicting vehicles via the collaborative collision avoidance behavior,effectively avoid the collision conflicts among vehicles with nonlinear speeds and trajectories and mitigates the multiple vehicle collisions and significant traffic flow fluctuations in bottleneck areas.The proposed collision avoidance algorithm outperforms other algorithms in bottleneck areas with large-scale vehicle conflicts,enhancing the vehicle traffic efficiency by 10.42%and reducing the vehicle collision risk by 47.32%.Thus,the algorithm has sound performance in coordinating the collision avoidance behavior of large-scale conflict vehicles and reducing the vehicle collision risks and operation delays.2 tabs,20 figs,41 refs.