INTERACT: Interactive Planning for Autonomous Driving via Anchor-Conditioned Prediction and Trust-Region Refinement

arXiv:2609.31137 · cs.RO · Submitted 2026-09-25 · Read on arXiv

cs.RO

Submitted: 2026-09-25

Updated: 2026-09-25

Terminology

• Benchmark — 18× in this paper · explained in 2 episodes
• Cross-Entropy Method — 5× in this paper
• Ablation — 4× in this paper · explained in 1 episode
• Accuracy — 3× in this paper · explained in 1 episode
• Steering — 3× in this paper · explained in 2 episodes
• Conference on Computer Vision and Pattern Recognition — 2× in this paper
• Transformer — 2× in this paper · explained in 2 episodes
• Ablation Studies — 1× in this paper · explained in 1 episode
• Action Space — 1× in this paper · explained in 1 episode
• Robustness — 1× in this paper · explained in 5 episodes

Sources

• NuPlan: A closed-loop ML-based planning benchmark for autonomous vehicles
• Perfect Prediction or Plenty of Proposals? What Matters Most in Planning for Autonomous Driving
• MBAPPE: MCTS-Built-Around Prediction for Planning Explicitly
• Tree-structured Policy Planning with Learned Behavior Models
• DiffuSearch: How Hybrid Trajectory Planning Benefits from Aligned Objectives in Diffusion and Action Space
• Can Vehicle Motion Planning Generalize to Realistic Long-tail Scenarios?

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• HRDexDB: A 4D Dexterous Grasping Dataset Across Human and Multiple Robot Embodiments
• APT: Action Expert Pretraining Improves Instruction Generalization of Vision-Language-Action Policies
• Fine-tuning is Not Enough: A Parallel Framework for Collaborative Imitation and Reinforcement Learning in End-to-end Autonomous Driving