摘要
为减少自动驾驶车辆产生的交通事故,提高车辆在仿真环境中的安全测试效率,提出一种基于深度强化学习的自动驾驶碰撞测试场景构建方法。首先,通过设置状态、动作和奖励函数,将车辆的驾驶过程映射为马尔可夫决策过程;然后,基于搭建的仿真平台(CARLA-DRL)训练智能体完成车辆碰撞任务,生成碰撞测试场景;最后,随机进行500次碰撞仿真试验,根据智能体与自动驾驶车辆的相对距离,分析碰撞成功率、碰撞时间和冲突能量。结果表明:智能体生成符合车辆动力学的碰撞轨迹,能够构建精细化、多类型的碰撞测试场景;智能体与自动驾驶车辆的平均碰撞成功率为62.20%,平均碰撞时间为127.25 s,平均冲突能量值为175.98 kJ。该方法能够构建高频、高效和高危的自动驾驶车辆碰撞测试场景,增加仿真场景中偶发高风险场景的发生概率,提高自动驾驶车辆对于碰撞事故的安全测试效率。
To reduce traffic accidents caused by autonomous vehicles and improve the efficiency of vehicle safety testing in simulation environments,an autonomous driving collision test scenario construction method was proposed based on deep reinforcement learning.Firstly,the vehicle's driving process was mapped to a Markov decision process by setting the state,action,and reward functions.Then,the agent was trained to complete the vehicle collision task and generate the collision test scenarios based on the built simulation platform(CARLA-DRL).Finally,500 random collision simulation tests were conducted to analyze the collision success rate,collision time,and collision energy based on the relative distance between the agent and the autonomous vehicle.The results indicated that the agent generated collision trajectories that conformed to vehicle dynamics and could construct refined and multi-type collision test scenarios.The average collision success rate between the agent and the autonomous vehicle was 62.20%,the average collision time was 127.25 s,and the average collision energy value was 175.98 kJ.The proposed method can construct high-frequency,high-efficient,and high-risk autonomous driving vehicle collision test scenarios,increasing the probability of occasional high-risk scenarios in simulation scenarios and enhancing the efficiency of safety testing for autonomous vehicle collision incidents.
作者
赵尧华
陈延展
郑亮
李树凯
ZHAO Yaohua;CHEN Yanzhan;ZHENG Liang;LI Shukai(School of Traffic and Transportation Engineering,Central South University,Changsha Hunan 410075,China;State Key Laboratory of Rail Traffic Control and Safety,Beijing Jiaotong University,Beijing 100044,China)
出处
《中国安全科学学报》
CAS
CSCD
北大核心
2024年第7期211-218,共8页
China Safety Science Journal
基金
国家自然科学基金面上项目资助(72371251)
湖南省自然科学基金杰出青年项目资助(2024JJ2080)
轨道交通控制与安全国家重点实验室(北京交通大学)开放课题基金资助(RCS2022K004)。
关键词
自动驾驶
安全测试
深度强化学习
碰撞测试场景
仿真试验
autonomous driving
safety testing
collision test scenarios
simulation experiment
deep reinforcement learning
