Persistent Autoregressive Mapping with Traffic Rules for Autonomous Driving

作者：Shiyi Liang, Xinyuan Chang, Chan Wu, Huiyuan Yan, Yifan Bai, Xinran Liu, Hang Zhang, Yujian Yuan, Shuang Zeng, Mu Xu, Xing Wei · 发表于：arXiv (Cornell University) · 年份：2025 · DOI：10.48550/arxiv.2509.22756 · 研究领域：Graph Theory and Algorithms、Medical Image Segmentation Techniques、Computer Graphics and Visualization Techniques

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.