RECTOR: Priority-Aware Rule-Based Reranking for Compliance-Aware Autonomous Driving Trajectory Selection
The paper presents RECTOR, a rule-based reranking system designed for autonomous driving trajectory selection. It prioritizes safety, legal compliance, road conditions, and comfort in its decision-making process. The results demonstrate a significant reduction in safety and legal violations compared to traditional confidence-based selection methods.
- ▪RECTOR uses a tiered rulebook to score trajectory candidates based on safety, legality, road conditions, and comfort.
- ▪The system achieved a reduction in safety and legal violations from 28.58% to 20.42% compared to confidence-only methods.
- ▪Under adversarial conditions, confidence-only selection failed in 100% of scenarios, while rule-aware selectors maintained a 96% success rate.
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| Original publisher | arXiv cs.AI |
| Canonical URL | https://arxiv.org/abs/2605.25095 |
| Publication time | Tue, 26 May 2026 00:00:00 -0400 |
| Retrieval time | 2026-05-26T04:07:43.013Z |
| Last seen | 2026-05-26T04:07:43.013Z |
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| Commercial reuse | May the content be reused commercially? | Not permitted |
Basis: Derived from the published RSS/Atom feed. Contact: [email protected]. Reviewed: 2026-07-24.
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Computer Science > Artificial Intelligence arXiv:2605.25095 (cs) [Submitted on 24 May 2026] Title:RECTOR: Priority-Aware Rule-Based Reranking for Compliance-Aware Autonomous Driving Trajectory Selection Authors:Hadi Hajieghrary, Benedikt Walter, Chaitanya Shinde, Paul Schmitt, Miguel Hurtado View a PDF of the paper titled RECTOR: Priority-Aware Rule-Based Reranking for Compliance-Aware Autonomous Driving Trajectory Selection, by Hadi Hajieghrary and Benedikt Walter and Chaitanya Shinde and Paul Schmitt and Miguel Hurtado View PDF HTML (experimental) Abstract:Autonomous driving stacks must pick one trajectory from a multi-modal candidate set; choosing by model confidence ignores safety, traffic-law, and comfort constraints.
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