SAD-RL, a hierarchical RL framework trained on synthetic and real-road highway scenarios, achieves over 69 percent goal-reaching rates across all test scenario types in simulation.
Scenario- and model-based systems engineering for highly automated driving,
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Scenario-Based Hierarchical Reinforcement Learning for Automated Driving Decision Making
SAD-RL, a hierarchical RL framework trained on synthetic and real-road highway scenarios, achieves over 69 percent goal-reaching rates across all test scenario types in simulation.