让自动驾驶持续记住交通规则，即使标志消失也能保持正确判断。
Persistent Autoregressive Mapping with Traffic Rules for Autonomous Driving
- 边看路边构建车道线与交通规则的联合模型。
- 在长距离驾驶中保持规则一致性，准确率显著提升。
- 适合需要长期记忆交通规则的自动驾驶系统使用。
安全的自动驾驶需同时实现高精地图的精准构建和对交通规则的持久感知，即使相关标志已不可见。现有方法或仅关注几何要素，或把规则视为临时分类，无法捕捉其在长时间驾驶序列中的持续有效性。本文提出PAMR（Persistent Autoregressive Mapping with Traffic Rules）框架，通过视觉观测实现车道线与交通规则的自回归联合构建。引入两个关键机制：分段处理驾驶场景的Map-Rule Co-Construction，以及跨段维持规则一致性的Map-Rule Cache。为评估连续一致的地图生成，我们构建了改进版的MapDRv2数据集，包含更精确的车道几何标注。大量实验表明，PAMR在联合矢量-规则映射任务中表现优异，且在整个长距离驾驶序列中保持规则的有效性。
原文摘要 · Abstract (English)
Safe autonomous driving requires both accurate HD map construction and persistent awareness of traffic rules, even when their associated signs are no longer visible. However, existing methods either focus solely on geometric elements or treat rules as temporary classifications, failing to capture their persistent effectiveness across extended driving sequences. In this paper, we present PAMR (Persistent Autoregressive Mapping with Traffic Rules), a novel framework that performs autoregressive co-construction of lane vectors and traffic rules from visual observations. Our approach introduces two key mechanisms: Map-Rule Co-Construction for processing driving scenes in temporal segments, and Map-Rule Cache for maintaining rule consistency across these segments. To properly evaluate continuous and consistent map generation, we develop MapDRv2, featuring improved lane geometry annotations. Extensive experiments demonstrate that PAMR achieves superior performance in joint vector-rule mapping tasks, while maintaining persistent rule effectiveness throughout extended driving sequences.
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