CoMoCAVs proposes a Mixture of Experts inspired hierarchical RL framework that couples lane-selection decisions with lane-specific motion-planning policies for autonomous highway driving.
Robust lane change decision for autonomous vehicles in mixed traffic: A safety-aware multi-agent adversarial reinforcement learning approach,
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CoMoCAVs: Cohesive Decision-Guided Motion Planning for Connected and Autonomous Vehicles with Multi-Policy Reinforcement Learning
CoMoCAVs proposes a Mixture of Experts inspired hierarchical RL framework that couples lane-selection decisions with lane-specific motion-planning policies for autonomous highway driving.