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.
Dynamic trajectory planning with dynamic constraints: A’state-time space’approach,
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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.