汽车工程 ›› 2022, Vol. 44 ›› Issue (7): 976-986.doi: 10.19562/j.chinasae.qcgc.2022.07.004
所属专题: 智能网联汽车技术专题-感知&HMI&测评2022年
收稿日期:
2022-01-06
修回日期:
2022-02-25
出版日期:
2022-07-25
发布日期:
2022-07-20
通讯作者:
任秉韬
E-mail:renbt1706@buaa.edu.cn
基金资助:
Jiangkun Li1,Weiwen Deng1,Bingtao Ren1(),Wenqi Wang1,Juan Ding2
Received:
2022-01-06
Revised:
2022-02-25
Online:
2022-07-25
Published:
2022-07-20
Contact:
Bingtao Ren
E-mail:renbt1706@buaa.edu.cn
摘要:
为解决小概率高风险边缘测试场景的问题,本文提出一种基于场景动力学和强化学习的边缘场景生成方法,实现边缘场景的自动生成,能模拟真实世界中车辆间的对抗与博弈行为的特征。首先将随时间动态变化的场景模型由一组微分方程描述为场景动力学系统;然后利用神经网络作为通用函数逼近器来构造场景黑盒控制器,并基于强化学习实现边缘场景控制器的优化求解;最后以超车切入场景为例,在Matlab/Simulink软件进行仿真验证,结果表明,边缘场景强化生成模型在场景交互博弈、覆盖率和可重复测试等方面具有良好的性能。
李江坤,邓伟文,任秉韬,王文奇,丁娟. 基于场景动力学和强化学习的自动驾驶边缘测试场景生成方法[J]. 汽车工程, 2022, 44(7): 976-986.
Jiangkun Li,Weiwen Deng,Bingtao Ren,Wenqi Wang,Juan Ding. Automatic Driving Edge Scene Generation Method Based on Scene Dynamics and Reinforcement Learning[J]. Automotive Engineering, 2022, 44(7): 976-986.
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