Real-Time Terrain Condition Detection for Off-Road Driving Based on Transformer

Off-road driving is dangerous due to the deformation and irregularities of the road surface. The diverse nature of off-road surfaces makes it difficult to identify the safety of the driving surface. In this study, a Transformer-based neural network is proposed to estimate the drivability of various off-road surfaces, aiming to discern whether the terrain is safe to drive or potentially dangerous. The network only utilizes Controller Area Network (CAN)-bus signals from the vehicle, which makes it easy to implement on a readily available vehicle. To train the network, driving data was collected from a diverse range of off-road environments, from areas where novice drivers can drive safely to hazardous areas where expert drivers get stuck. The authors also propose a post-processing algorithm to filter out false estimations and limit frequent changes in estimation, as these can have detrimental effects on real-world systems. The performance of the authors' algorithm was evaluated in real-time on various off-road surfaces showing high level of accuracy.

• Record URL:
  • https://doi.org/10.1109/TITS.2024.3368476
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/oclc/41297384
• Supplemental Notes:
  • Copyright © 2024, IEEE.
• Authors:
  • Shon, Hyukju
  • Choi, Seungwon
  • Huh, Kunsoo
• Publication Date: 2024-9

Language

• English

Media Info

• Media Type: Web
• Features: References;
• Pagination: pp 11726-11738
• Serial:
  • IEEE Transactions on Intelligent Transportation Systems
  • Volume: 25
  • Issue Number: 9
  • Publisher: Institute of Electrical and Electronics Engineers (IEEE)
  • ISSN: 1524-9050
  • Serial URL: http://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=6979

Subject/Index Terms

• TRT Terms: All terrain vehicles; Machine learning; Real time information; Terrain evaluation; Transformers; Vehicle performance
• Subject Areas: Data and Information Technology; Highways; Vehicles and Equipment;

Filing Info

• Accession Number: 01939898
• Record Type: Publication
• Files: TRIS
• Created Date: Dec 16 2024 11:59AM