mlsanityData to 7 Oct 2026

CARVE: Certified Affordable Repair of Vetoed Maneuvers via Envelopes for Interactive Driving

arXiv 2606.02641 ↗First posted 31 May 2026cs.RO cs.AIcited by 0

On the map: End-to-end autonomous driving → Adversarial scenarios and safety testing

Abstract (arXiv)

Interactive driving exposes a failure mode that is easy to miss in rule-aware autonomous-driving stacks: a hard-rule margin can be negative for an ego candidate even though a small lawful accommodation by a non-priority agent would restore feasibility. Existing rulebooks, shields, and reachability filters are strong at vetoing unsafe actions, while prediction-based planners model likely responses. Neither returns a runtime proof object that states which bounded multi-agent edit repairs the maneuver, who owns the edit, whether the request is right-of-way affordable, and what ego fallback remains if the request is not observed. We formulate this missing object as *interactive repair certification* and introduce *CARVE*, a prediction-free certificate layer over a finite lattice of ego-owned and agent-owned tactical operators. Agent-owned requests are admissible only inside (Bj(s) = β(πj)αjmax(s)), a cooperation envelope that separates kinematic reachability from normative priority. The resulting certificate records the binding rule, repair category, repair set, responsibility-weighted cost split, and fallback. On 589 Lanelet2-geometry-grounded INTERACTION replay episodes, CARVE-Greedy accepts 98.64% of initially vetoed maneuvers and recovers 370/378 human-resolved false vetoes, while preserving 589/589 right-of-way respect, zero priority-agent false positives, and 400/400 negative-stress vetoes. We prove certificate soundness, structural right-of-way respect, exact finite-lattice minimality, fallback contingency, and blame-consistency conditions. CARVE does not predict or require another driver's compliance; it certifies whether a proposed interaction is bounded, attributable, and normatively admissible under declared assumptions.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Placed in Adversarial scenarios and safety testing, one of 9 directions in End-to-end autonomous driving. Builds on 17 of its 35 references that are in the map. First posted on arXiv 31 May 2026.

Builds on

17 of 35 references.

References · 17

  1. 2019nuScenes: A multimodal dataset for autonomous drivingcited by 4,485
  2. 2019Scalability in Perception for Autonomous Driving: Waymo Open Datasetcited by 2,436
  3. 2019Argoverse: 3D Tracking and Forecasting with Rich Mapscited by 728
  4. 2021Large Scale Interactive Motion Forecasting for Autonomous Driving : The Waymo Open Motion Datasetcited by 532
  5. 2020VectorNet: Encoding HD Maps and Agent Dynamics from Vectorized Representationcited by 528
  6. 2019MultiPath: Multiple Probabilistic Anchor Trajectory Hypotheses for Behavior Predictioncited by 417
  7. 2018The highD Dataset: A Drone Dataset of Naturalistic Vehicle Trajectories on German Highways for Validation of Highly Automated Driving Systemscited by 276
  8. 2016Control Barrier Function Based Quadratic Programs for Safety Critical Systemscited by 261
  9. 2018Convolutional Social Pooling for Vehicle Trajectory Predictioncited by 221
  10. 2019The inD Dataset: A Drone Dataset of Naturalistic Road User Trajectories at German Intersectionscited by 137
  11. 2017On a Formal Model of Safe and Scalable Self-driving Carscited by 129
  12. 2018Hierarchical Game-Theoretic Planning for Autonomous Vehiclescited by 48
5 more
  1. 2019Liability, Ethics, and Culture-Aware Behavior Specification using Rulebookscited by 41
  2. 2019Efficient Iterative Linear-Quadratic Approximations for Nonlinear Multi-Player General-Sum Differential Gamescited by 37
  3. 2024Driving with Regulation: Trustworthy and Interpretable Decision-Making for Autonomous Driving with Retrieval-Augmented Reasoningcited by 11
  4. 2024Traffic-Rule-Compliant Trajectory Repair via Satisfiability Modulo Theories and Reachability Analysiscited by 3
  5. 2025Autonomous Vehicle Path Planning by Searching With Differentiable Simulationcited by 1

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  1. 2026CARVE-Q: Quantum-Proposed, Classically Certified Interactive Driving Repair
  2. 2026INTERACT: Interactive Planning for Autonomous Driving via Anchor-Conditioned Prediction and Trust-Region Refinement
  3. 2025ADReFT: Adaptive Decision Repair for Safe Autonomous Driving via Reinforcement Fine-Tuning
  4. 2025AgentSpec: Customizable Runtime Enforcement for Safe and Reliable LLM Agents
  5. 2026Agent Behavioral Contracts: Formal Specification and Runtime Enforcement for Reliable Autonomous AI Agents
  6. 2026EvoDrive: Pareto Evolution for Safety-Critical Autonomous Driving via Self-Improving LLM Agents
  7. 2026MDrive: Benchmarking Closed-Loop Cooperative Driving for End-to-End Multi-agent Systems
  8. 2026Falsifiable Release Gates for Self-Improving Systems: Standing Invariants at Scale