Combining Policy Gradient and Safety-Based Control for Autonomous Driving

With the advancement of data-driven techniques, addressing continuous control challenges has become more efficient. However, the reliance of these methods on historical data introduces the potential for unexpected decisions in novel scenarios. To enhance performance in autonomous driving and collision avoidance, we propose a symbiotic fusion of policy gradient with safety-based control. In this study, we employ the Deep Deterministic Policy Gradient (DDPG) algorithm to enable autonomous driving in the absence of surrounding vehicles. By training the vehicle’s driving policy within a stable and familiar environment, a robust and efficient learning process is achieved. Subsequently, an artificial potential field approach is utilized to formulate a collision avoidance algorithm, accounting for the presence of surrounding vehicles. Furthermore, meticulous consideration is given to path-tracking methods. The amalgamation of these approaches demonstrates substantial performance across diverse scenarios, underscoring its potential for advancing autonomous driving while upholding safety standards.