Lane-Level Map-Aiding Approach Based on Non-Lane-Level Digital Map Data in Road Transport Security

Philipp Luz, Li Zhang, Jinyue Wang and Volker Schwieger
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Philipp Luz: Institute of Engineering Geodesy (IIGS), University of Stuttgart, D-70174 Stuttgart, Germany
Li Zhang: Institute of Engineering Geodesy (IIGS), University of Stuttgart, D-70174 Stuttgart, Germany
Jinyue Wang: Institute of Engineering Geodesy (IIGS), University of Stuttgart, D-70174 Stuttgart, Germany
Volker Schwieger: Institute of Engineering Geodesy (IIGS), University of Stuttgart, D-70174 Stuttgart, Germany

Sustainability, 2021, vol. 13, issue 17, 1-18

Abstract: To prevent terror attacks in which trucks are used as weapons as happened in Nice or Berlin in 2016, the European Project Autonomous Emergency Maneuvering and Movement Monitoring for Road Transport Security (TransSec) was launched in 2018. One crucial point of this project is the development of a map-aiding approach for the localization of vehicles on digital maps, so that the information in digital map data can be used to detect prohibited driving maneuvers, such as off-road or wrong-way drivers. For example, a lane-level map-aiding approach is required for wrong-way driver detection. Navigation Data Standard (NDS) is one of the worldwide map standards developed by several automobile manufacturers. So far, there is no lane-level NDS map covers a large area, therefore, it was decided to use the latest available NDS map without lane level accuracy. In this paper, a lane-level map-aiding approach based on a non-lane-level NDS map is presented. Due to the inaccuracy of vehicle position and digital map the map-aiding does not always provide the correct results, so probabilities of off-road and wrong-way diver detection are estimated to support risk estimation. The performance of the developed map-aiding approach is comprehensively evaluated with both real and simulated trajectories.

Keywords: road transport security; digital map; map-matching; map-aiding; probability estimation (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2021
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