ICDP constrains interaction distribution shift via contrastive density-ratio estimation, suppressing high-value but interaction-unsupported trajectories on nuPlan closed-loop and a real truck
The work introduces ICDP, an offline reinforcement learning framework that exactly decomposes joint-support degradation over ego and surrounding-agent futures into an ego-support component and a residual interaction-support component, recovers the latter through contrastive density-ratio estimation, and constrains policy improvement without explicit joint-density modeling, surrounding-agent prediction, or reactive simulator or world-model rollouts; on nuPlan and InterPlan closed-loop evaluations and real-world truck experiments, ICDP suppresses high-value yet interaction-unsupported trajectory selections and improves performance in interaction-critical driving scenarios.