Boxes-Based Representation and Data Sharing of Road Surface Friction for CAVs

Vehicles can easily lose control unexpectedly when encountering unforeseen hazardous road friction conditions. With automation and connectivity increasingly available to assist drivers, vehicle performance can significantly benefit from a road friction preview map, particularly to identify where and how friction ahead of a vehicle may be suddenly decreasing. Although many techniques enable the vehicle to measure the local friction as driving upon a surface, these encounters limit the ability of a vehicle to slow down before a low-friction surface is already encountered. Using the connectivity of connected and autonomous vehicles (CAVs), a global road friction map can be created by aggregating information from vehicles. A challenge in the creation of these global friction maps is the very large quantity of data involved, and that the measurements populating the map are generated by vehicle trajectories that do not uniformly cover the grid. This paper presents a road friction map generation strategy that aggregates the measured road-tire friction coefficients along the individual trajectories of CAVs into a road surface grid. In addition, through clustering the friction grids further, an insight of this work is that the friction map can be represented compactly by rectangular boxes defined by a pair of corner coordinates in space, a friction value, and a confidence interval within the box. To demonstrate the method, a simulation is presented that integrates traffic simulations, vehicle dynamics and on-vehicle friction estimators, and a highway road surface, where friction is changing in space, particularly over a bridge segment. The experimental results indicate that the road friction distribution can be measured effectively by collecting and aggregating the friction data from CAVs.

• Record URL:
  https://doi.org/10.1007/s42421-023-00071-0
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/issn/2948135X
• Supplemental Notes:
  • © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
• Authors:
  • Gao, Liming
  • Mitrovich, Juliette
  • Beal, Craig
  • Bai, Wushuang
  • Maddipatla, Satya Prasad
  • Chen, Cindy
  • Jerath, Kshitij
  • Haeri, Hossein
  • Sinanaj, Lorina
  • Brennan, Sean
• Publication Date: 2023-8

Language

• English

Media Info

• Media Type: Web
• Features: References;
• Pagination: 9
• Serial:
  • Data Science for Transportation
  • Volume: 5
  • Issue Number: 2
  • Publisher: Springer Nature Singapore Pte Ltd.
  • ISSN: 2948-135X
  • EISSN: 2948-1368
  • Serial URL: https://www.springer.com/journal/42421

Subject/Index Terms

• TRT Terms: Autonomous vehicles; Connected vehicles; Highway safety; Rolling friction; Sliding friction; Vehicle trajectories
• Subject Areas: Highways; Safety and Human Factors; Vehicles and Equipment;

Filing Info

• Accession Number: 01889170
• Record Type: Publication
• Files: TRIS
• Created Date: Jul 27 2023 4:55PM