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We present \\textsc{RECTOR} (Rule-Enforced Constrained Trajectory Orchestrator), a post-generation reranking layer that scores candidates against a tiered rulebook (Safety~$\\succ$~Legal~$\\succ$~Road~$\\succ$~Comfort) via differentiable proxies and a scene-conditioned applicability mechanism, then selects with a deterministic $\\varepsilon$-lexicographic rule that preserves cross-tier priority by construction -- without retraining the predictor.\n  On the Waymo Open Motion Dataset \\texttt{validation\\_interactive} split (43{,}219 augmented instances, $K{=}6$), under Protocol~B (28-rule proxy catalog, oracle applicability) rule-aware selection cuts Safety+Legal violations from 28.58\\% to 20.42\\% and Total from 40.32\\% to 32.41\\% versus confidence-only on the same candidates. A un","title":"RECTOR: Priority-Aware Rule-Based Reranking for Compliance-Aware Autonomous Driving Trajectory Selection","url":"https://arxiv.org/abs/2605.25095","vendor":"arxiv_cs_ai"},"summary":"arXiv:2605.25095v1 Announce Type: new \nAbstract: Autonomous driving stacks must pick one trajectory from a multi-modal candidate set; choosing by model confidence ignores safety, traffic-law, and comfort constraints. We present \\textsc{RECTOR} (Rule-Enforced Constrained Trajectory Orchestrator), a post-generation reranking layer that scores candidates against a tiered rulebook (Safety~$\\succ$~Legal~$\\succ$~Road~$\\succ$~Comfort) via differentiable proxies and a scene-conditioned applicability mechanism, then selects with a deterministic $\\varepsilon$-lexicographic rule that preserves cross-tier priority by construction -- without retraining the predictor.\n  On the Waymo Open Motion Dataset \\texttt{validation\\_interactive} split (43{,}219 augmented instances, $K{=}6$), under Protocol~B (28-rule proxy catalog, oracle applicability) rule-aware selection cuts Safety+Legal violations from 28.58\\% to 20.42\\% and Total from 40.32\\% to 32.41\\% versus confidence-only on the same candidates. 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