VIS-MM: a novel map-matching algorithm with semantic fusion from vehicle-borne images

• Published in: International journal of geographical information science : IJGIS Vol. 37; no. 5; pp. 1069 - 1098
• Main Authors: Li, Bozhao, Wang, Mengqi, Cai, Zhongliang, Su, Shiliang, Kang, Mengjun
• Format: Journal Article
• Language: English
• Published: Abingdon Taylor & Francis 04.05.2023; Taylor & Francis LLC
• Subjects: Algorithms; Autonomous navigation; Complexity; Driving; Driving conditions; Entropy; Entropy (statistics); entropy weight method; hidden Markov model; High definition; image recognition technology; Map-matching algorithm; Markov chains; Matching; Roads & highways; Semantics; Traffic; vehicle-borne image semantics; Weighting methods
• ISSN: 1365-8816; 1362-3087; 0269-3798; 1362-3087; 1365-8824
• DOI: 10.1080/13658816.2023.2169445

Conventional map-matching (MM) algorithms take blind eyes to the complexity in realistic traffic conditions and hence present significant limitations in distinguishing the detailed driving paths of vehicles within complex urban road networks. The popularity of vehicle-borne cameras and advances in image recognition technologies provide an opportunity to remedy the gap through integrating vehicle-borne image semantic information with MM algorithms. Following this logic, this article proposes a novel MM algorithm with semantic fusion from vehicle-borne images (VIS-MM) suited to the parallel road scenes. First, a multipath output algorithm is developed using the hidden Markov model to obtain candidate paths. Second, image recognition techniques are employed to extract vehicle-borne image semantics. Finally, the entropy weight method is performed to determine the most promising driving path among the candidate paths. The experimental results show that semantic fusion from vehicle-borne images contributes to a significant improvement of accuracy from 66.18% to 99.88% against the parallel road scenes. The proposed map-matching algorithm can be applied into the fields of unmanned autonomous navigation and crowdsourcing updating of high-definition maps.