Combining Deep Reinforcement Learning and Safety Based Control for Autonomous Driving

By Xincheng Xiong, Jianqiang Wang, Fang Zhang, Keqiang Li. 2016

70 citations recorded by the source corpus.

With the development of state-of-art deep reinforcement learning, we can efficiently tackle continuous control problems. But the deep reinforcement learning method for continuous control is based on historical data, which would make unpredicted decisions in unfamiliar scenarios. Combining deep reinforcement learning and safety based control can get good performance for self-driving and collision avoidance. In this passage, we use the Deep Deterministic Policy Gradient algorithm to implement autonomous driving without vehicles around. The vehicle can learn the driving policy in a stable and familiar environment, which is efficient and reliable. Then we use the artificial potential field to design collision avoidance algorithm with vehicles around. The path tracking method is also taken into consideration. The combination of deep reinforcement learning and safety based control performs well in most scenarios.

Subfields: Artificial Intelligence, Automotive Engineering, Control and Systems Engineering

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Citations in the corpus

Deep Reinforcement Learning for Autonomous Driving: A Survey (2020, methodology): “The deep reinforcement learning algorithms for con-trol such as DDPG and safety based control are combined in [146], including artiﬁcial potential ﬁeld method that is widely used for robot path planning.”
Deep Reinforcement Learning for Intelligent Transportation Systems: A Survey (2020, methodology): “[113] DDPG Autonomous driving Collision avoidance TORCH game -”
Exploring applications of deep reinforcement learning for real-world autonomous driving systems (2019, methodology): “DDPG and safety based control : The deep reinforcement learning (DDPG) and safety based control are combined in (Xiong et al., 2016), including artiﬁcial potential ﬁeld method that is widely used for robot path planning.”
Synthetic Data for Deep Learning (2019, background): “[661] add safety-based control on top of deep RL, Wang et al.”
Distributed Multiagent Coordinated Learning for Autonomous Driving in Highways Based on Dynamic Coordination Graphs (2020, unknown): “The recent integration of deep learning has greatly promoted the successful applications of RL in solving more complex control problems in autonomous driving, including steering and acceleration manipulation [10], [11],”
A Survey of Deep Learning Applications to Autonomous Vehicle Control (2019, background): “[214] suggested a control structure which combines reinforcement learning based control with safety based control and path tracking.”