Deep reinforcement learning for pedestrian collision avoidance and human-machine cooperative driving
Deep reinforcement learning for pedestrian collision avoidance and human-machine cooperative driving
复制标题
行人防撞与人机协同驾驶的深度强化学习
DOI：
10.1016/j.ins.2020.03.105
复制
发表时间：
2020-09-01
影响因子：
8.1
通讯作者：
Ren, Junkai
中科院分区：
文献类型：
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作者：
Li, Junxiang;Yao, Liang;Ren, Junkai
随着智能驾驶技术的发展，人机协同驾驶对于提高驾驶员分心或误操作等异常情况下的驾驶安全性具有重要意义。对于人机协同驾驶来说，行人避撞能力是基础性的，也是重要的。提出了一种基于深度强化学习的具有主动避碰能力的人机协同驾驶方案（L-HMC）。首先，设计了一种改进的深度Q网络（DQN）方法来学习行人避撞的最优驾驶策略。在改进的DQN方法中，设计了两个具有非均匀样本的重放缓冲区，以缩短最优驱动策略的学习过程。在此基础上，提出了一种人机协同驾驶方案，当驾驶员的驾驶行为对行人造成危险时，利用学习到的驾驶策略辅助驾驶员进行行人避撞。利用真实的车辆动力学模型，在PreScan仿真平台上验证了人机协同驾驶方案的有效性。结果表明，基于深度强化学习的方法可以学习有效的驾驶策略，以快速收敛速度避免行人碰撞。同时，提出的人机协同驾驶方案L-HMC可以在典型场景下通过灵活的策略避免潜在的行人碰撞，从而提高驾驶安全性。(C)2020爱思唯尔公司All rights reserved.
With the development of intelligent driving technology, human-machine cooperative driving is significant to improve driving safety in abnormal situations, such as distraction or incorrect operations of drivers. For human-machine cooperative driving, the capacity of pedestrian collision avoidance is fundamental and important. This paper proposes a novel learning-based human-machine cooperative driving scheme (L-HMC) with active collision avoidance capacity using deep reinforcement learning. Firstly, an improved deep Q-network (DQN) method is designed to learn the optimal driving policy for pedestrian collision avoidance. In the improved DQN method, two replay buffers with nonuniform samples are designed to shorten the learning process of the optimal driving policy. Then, a human-machine cooperative driving scheme is proposed to assist human drivers with the learned driving policy for pedestrian collision avoidance when the driving behavior of human drivers is dangerous to the pedestrian. The effectiveness of the human-machine cooperative driving scheme is verified on the simulation platform PreScan using a real vehicle dynamic model. The results demonstrate that the deep reinforcement learning-based method can learn an effective driving policy for pedestrian collision avoidance with a fast convergence rate. Meanwhile, the proposed human-machine cooperative driving scheme L-HMC can avoid potential pedestrian collisions through flexible policies in typical scenarios, therefore improving driving safety. (C) 2020 Elsevier Inc. All rights reserved.