mlsanityData to 14 Oct 2026

CARVE-Q: Quantum-Proposed, Classically Certified Interactive Driving Repair

arXiv 2606.06531 ↗First posted 3 Jun 2026cs.AI quant-phcited by 0

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Abstract (arXiv)

The critical question after a correct driving veto is not only whether a maneuver is unsafe, but whether the blocked interaction admits a lawful, auditable, and responsibility-bounded repair. Prediction and game-theoretic planners can suggest plausible cooperation, yet they do not return a proof that the repair respects hard rules, right-of-way, cost allocation, and ego fallback. We introduce CARVE, Certified Affordable Repair of Vetoed maneuvers via Envelopes, a certificate architecture for prediction-free interactive repair. Given a vetoed maneuver, CARVE constructs a finite repair lattice and emits a structured certificate recording the binding rule, selected joint repair, right-of-way-scaled cooperation envelope, responsibility-weighted cost split, and ego-only fallback. This certificate view reveals the algorithmic bottleneck: multi-owner repair induces a product lattice M = prodj |Aj|. We therefore introduce CARVE-Q, a verifier-shielded quantum-AI search layer that applies quantum minimum finding only to this black-box lattice while leaving all safety authority classical. In the conservative verifier-oracle model, exact classical minimum finding requires Theta(M) queries in the worst case, whereas Durr-Hoyer/Grover minimum finding uses O(√M) oracle queries with high probability. We prove verifier-shielded certificate soundness, priority non-elicitation, black-box query separation, and finite-precision reversible-oracle constructibility. We then demonstrate state-vector minimum finding on CARVE repair oracles up to 65,536 assignments and validate certificate preservation on Lanelet2-grounded INTERACTION replay with 100% right-of-way respect, 100% blame consistency, and zero priority false positives. The result is a trust-bounded quantum-AI pattern for certified autonomy: quantum proposes; CARVE certifies.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 14 of its 38 references that are in the map. First posted on arXiv 3 Jun 2026.

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14 of 38 references.

References · 14

  1. 2020Variational Quantum Algorithmscited by 692
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  3. 2018Barren plateaus in quantum neural network training landscapescited by 630
  4. 2017Safe Reinforcement Learning via Shieldingcited by 449
  5. 2016A Survey of Motion Planning and Control Techniques for Self-driving Urban Vehiclescited by 254
  6. 2019INTERACTION Dataset: An INTERnational, Adversarial and Cooperative moTION Dataset in Interactive Driving Scenarios with Semantic Mapscited by 231
  7. 2018Convolutional Social Pooling for Vehicle Trajectory Predictioncited by 221
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  10. 2021Parametrized quantum policies for reinforcement learningcited by 90
  11. 2020TensorFlow Quantum: A Software Framework for Quantum Machine Learningcited by 72
  12. 2017Lifelong Multi-Agent Path Finding for Online Pickup and Delivery Taskscited by 64
2 more
  1. 2022LaCAM: Search-Based Algorithm for Quick Multi-Agent Pathfindingcited by 51
  2. 2019Liability, Ethics, and Culture-Aware Behavior Specification using Rulebookscited by 43

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