Option Policies for Obstacle Avoidance in Safety Critical Scenarios Using Hierarchical Deep Reinforcement Learning

Autonomous Vehicle (AV) driving involves complex maneuvers, with Obstacle Avoidance (OA) being one of the most challenging and safety critical tasks. While Reinforcement Learning (RL) has shown promise in achieving human like driving behavior, a single RL agent struggles to manage the multiple sub-maneuvers required for OA, particularly in safety critical scenarios. To address this, we propose an Option Policy inspired Hierarchical Deep Reinforcement Learning (OPDRL) framework that divides OA into sub tasks such as left lane change, straight driving, and right lane change. Each sub task is handled by a specialized RL agent, governed by a central master policy. This approach reduces training time, simplifies validation, and seamlessly incorporates traffic safety rules to ensure robust decision making in safety critical scenarios. The proposed method is validated using scenarios inspired by the National Highway Traffic Safety Administration (NHTSA) precrash scenarios in the CARLA simulator, demonstrating its effectiveness in handling OA maneuvers efficiently.