Combining Deep Reinforcement Learning and Safety Based Control for Autonomous Driving

TL;DR

This paper integrates deep reinforcement learning with safety-based control to enhance autonomous driving, enabling stable learning and effective collision avoidance in unfamiliar scenarios.

Contribution

It proposes a novel combination of Deep Deterministic Policy Gradient and artificial potential fields for improved autonomous driving safety and efficiency.

Findings

Stable and reliable policy learning in familiar environments.

Effective collision avoidance with vehicles around.

Good performance across various scenarios.

Abstract

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.