Refined high-definition map model for roadside rest area

Yuan Guo, Jian Zhou, Quanhua Dong, Bijun Li, Jinsheng Xiao and Zhijiang Li

Transportation Research Part A: Policy and Practice, 2025, vol. 195, issue C

Abstract: With the rapid advancements in information and communication technology, sensor technology, and artificial intelligence, intelligent vehicles are advancing rapidly, creating new demands for navigation electronic maps. In response to these emerging requirements, high-definition (HD) maps have quickly risen to prominence. However, current HD maps primarily focus on road data and often simplify roadside rest areas (RRAs) into points of interest (POIs), presenting challenges for intelligent vehicles navigating within RRAs. To address these challenges, this paper introduces a refined HD map model specifically tailored for RRA. It categorizes the traffic elements within RRAs into four distinct classes: roads, lanes, markings and signs, and service areas. Each class of elements is thoroughly defined and explained within the proposed map model. In practical RRA scenarios, the proposed map model is visualized and validated using map exchange formats and physical application formats. The introduction of the RRA HD map model not only ensures that vehicles can achieve intelligent driving within RRAs, but also contributes to the establishment of data management platforms for RRAs, providing travellers users with enhanced services.

Keywords: Roadside rest area; High-definition map; Intelligent driving; Map model (search for similar items in EconPapers)
Date: 2025
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DOI: 10.1016/j.tra.2025.104463

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