CARVE: Certified Affordable Repair of Vetoed Maneuvers via Envelopes for Interactive Driving
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
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5 more
- 2019Liability, Ethics, and Culture-Aware Behavior Specification using Rulebookscited by 41
- 2019Efficient Iterative Linear-Quadratic Approximations for Nonlinear Multi-Player General-Sum Differential Gamescited by 37
- 2024Driving with Regulation: Trustworthy and Interpretable Decision-Making for Autonomous Driving with Retrieval-Augmented Reasoningcited by 11
- 2024Traffic-Rule-Compliant Trajectory Repair via Satisfiability Modulo Theories and Reachability Analysiscited by 3
- 2025Autonomous Vehicle Path Planning by Searching With Differentiable Simulationcited by 1
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- 2026CARVE-Q: Quantum-Proposed, Classically Certified Interactive Driving Repair
- 2026INTERACT: Interactive Planning for Autonomous Driving via Anchor-Conditioned Prediction and Trust-Region Refinement
- 2025ADReFT: Adaptive Decision Repair for Safe Autonomous Driving via Reinforcement Fine-Tuning
- 2025AgentSpec: Customizable Runtime Enforcement for Safe and Reliable LLM Agents
- 2026Agent Behavioral Contracts: Formal Specification and Runtime Enforcement for Reliable Autonomous AI Agents
- 2026EvoDrive: Pareto Evolution for Safety-Critical Autonomous Driving via Self-Improving LLM Agents
- 2026MDrive: Benchmarking Closed-Loop Cooperative Driving for End-to-End Multi-agent Systems
- 2026Falsifiable Release Gates for Self-Improving Systems: Standing Invariants at Scale