基于DRL的四轮独立驱动电动车辆的侧向车速估计  

Lateral velocity estimation for four-wheel-independent-drive electric vehicles based on deep reinforcement learning

作　　者：郑阳俊 贺帅 帅志斌[1] 李建秋[2] 盖江涛[1] 李勇 张颖 李国辉 ZHENG Yangjun;HE Shuai;SHUAI Zhibin;LI Jianqiu;GAI Jiangtao;LI Yong;ZHANG Ying;LI Guohui(China North Vehicle Research Institute,Beijing 100072,China;State Key Laboratory of Automotive Safety and Energy(Tsinghua University),Beijing 100084,China)

机构地区：[1]中国北方车辆研究所,北京100072 [2]汽车安全与节能国家重点实验室(清华大学),北京100084

出　　处：《汽车安全与节能学报》2022年第2期309-316,共8页Journal of Automotive Safety and Energy

基　　金：汽车安全与节能国家重点实验室开放基金课题(KF2018);国家自然科学基金项目(51975543)。

摘　　要：为精确估计车辆行驶状态,提出了一种四轮独立驱动电动车辆侧向车速估计方法。基于深度强化学习(DRL)范式,设计了侧向车速估计方法的架构;基于深度确定性策略梯度(DDPG)算法,设计了DRL智能体;采用循环神经网络,搭建了DDPG算法中的Actor网络和Critic网络。基于设计的奖励函数和训练场景,借助Matlab/Simulink软件,完成了算法的实现和训练;并通过在车辆双车道变换等实际行驶工况的仿真,进行了验证。结果表明:在经过了630次的学习训练之后,与扩展Kalman滤波方法相比,本文方法的估计精度提升40%。因而,本文方法能够在常用行驶工况中对车辆侧向车速进行估计。A lateral-velocity estimation method was proposed for an electric vehicle with four-wheel independent-drive to estimate the vehicle motion states precisely.An architecture was designed for the lateral velocity estimation method based on the deep reinforcement learning(DRL)paradigm;A DRL agent was designed with deep deterministic policy gradient(DDPG)algorithm;The actor network and the critic network of the DDPG algorithm were constructed with the recurrent neural network(RNN).The algorithm was realized and trained in Matlab/Simulink with the designed award function and training scenarios;The algorithm effectiveness was verified by the simulation of practical driving maneuvers such as double-lane changing.The results show that after 630 episodes of training and learning,the proposed method improves the estimation accuracy by 40%,compared with that of the extended Kalman filter(EKF)method.Therefore,the proposed method can be used to estimate vehicle lateral velocity in general driving scenarios.

关 键 词：车辆动力学控制 四轮独立驱动电动车辆 侧向车速估计 深度强化学习(DRL) 深度确定性策略梯度(DDPG) 

分 类 号：U469.72[机械工程—车辆工程]