Scenario-Based Hierarchical Reinforcement Learning for Automated Driving Decision Making
The SAD-RL framework integrates Hierarchical Reinforcement Learning (HRL) with a structured, scenario-based training environment for automated driving. This approach uses a high-level policy to select maneuver templates, which are executed by low-level control logic. By combining synthetic and real road scenarios and incorporating safety shielding, the method achieves safe behavior efficiently across easy and challenging driving situations.