Citation

Žuraulis V, Surblys V, Šabanovič E. Transport 2019; 34(3): 363-372.

Copyright

(Copyright © 2019, Vilnius Gediminas Technical University and Lithuanian Academy of Sciences, Publisher Vilnius Gediminas Technical University (VGTU) Press)

DOI

10.3846/transport.2019.10372

PMID

unavailable

Abstract

This paper presents the technological measures currently being developed at institutes and vehicle research centres dealing with forefront road identification. In this case, road identification corresponds with the surface irregularities and road surface type, which are evaluated by laser scanning and image analysis. Real-time adaptation, adaptation in advance and system external informing are stated as sequential generations of vehicle suspension and active braking systems where road identification is significantly important. Active and semi-active suspensions with their adaptation technologies for comfort and road holding characteristics are analysed. Also, an active braking system such as Anti-lock Braking System (ABS) and Autonomous Emergency Braking (AEB) have been considered as very sensitive to the road friction state. Artificial intelligence methods of deep learning have been presented as a promising image analysis method for classification of 12 different road surface types. Concluding the achieved benefit of road identification for traffic safety improvement is presented with reference to analysed research reports and assumptions made after the initial evaluation.

Keyword : road identification, road irregularities, laser scanning, semi-active suspension, damper, image analysis, friction state, deep learning, artificial intelligence, vehicle

Language: en

Keywords

artificial intelligence; damper; deep learning; friction state; image analysis; laser scanning; road identification; road irregularities; semi-active suspension; vehicle