A Flexible Multi-Layer Map Model Designed for Lane-Level Route Planning in Autonomous Vehicles. (April 2019)

• Record Type: Journal Article
• Authors: Jiang, Kun; Yang, Diange; Liu, Chaoran; Zhang, Tao; Xiao, Zhongyang
• Is Part Of: Engineering. Volume 5:Number 2(2019)
• Page Start: 305
• Page End: 318
• Publication Date: 2019-04
• Subjects: Lane-level -- Route planning -- Tsinghua map model -- Travel cost model; Engineering -- Periodicals; Engineering -- China -- Periodicals; 620.005
• DOI: 10.1016/j.eng.2018.11.032
• Languages: English
• ISSNs: 2095-8099

Abstract

Abstract: An increasing number of drivers are relying on digital map navigation systems in vehicles or mobile phones to select optimal driving routes in order to save time and improve safety. In the near future, digital map navigation systems are expected to play more important roles in transportation systems. In order to extend current navigation systems to more applications, two fundamental problems must be resolved: the lane-level map model and lane-level route planning. This study proposes solutions to both problems. The current limitation of the lane-level map model is not its accuracy but its flexibility; this study proposes a novel seven-layer map structure, called as Tsinghua map model, which is able to support autonomous driving in a flexible and efficient way. For lane-level route planning, we propose a hierarchical route-searching algorithm to accelerate the planning process, even in the presence of complicated lane networks. In addition, we model the travel costs allocated for lane-level road networks by analyzing vehicle maneuvers in traversing lanes, changing lanes, and turning at intersections. Tests were performed on both a grid network and a real lane-level road network to demonstrate the validity and efficiency of the proposed algorithm.