Enhancing the Performance of Vehicle Passenger Detection under Adverse Weather Conditions Using Augmented Reality-Based Machine Learning Approach

In response to extreme traffic congestion in metropolitan areas that causes unnecessarily long travel times, high fuel consumption, and excessive greenhouse gas emissions, transportation agencies have implemented various strategies to mitigate traffic congestion. Managed lanes—one of the measures applied worldwide—provide benefits to road users and operating agencies by integrating advanced technologies such as electronic and dynamic tolling systems. However, those agencies already implementing or considering implementing the managed lane strategy are seeking a solution to effectively and properly charge toll rates based on vehicle occupancy and penalize violating vehicles. Vehicle passenger detection systems (VPDSs) have been developed and evaluated worldwide, but limitations still inhibit their full implementation. This study confirms that the performance of the deep learning algorithm, a core VPDS technology, declines under certain adverse weather conditions because of lack of training data sets. The performance of the “you only look once” (YOLOv3) model trained with a normal weather data set decreased by as much as 8.5% when it was tested for adverse weather conditions. In this study, augmented reality (AR) models are developed to enhance the accuracy of vehicle passenger detection (VPDA) by the VPDS by training the algorithm with AR images representing virtual adverse weather conditions. Models trained with AR image sets of various weather categories (fog, rain, and snow) attained VPDA enhanced by up to 7.9%. The final model significantly improves VPDA under adverse weather conditions. The proposed models could be considered for implementation with road weather information systems under adverse weather conditions.

• Record URL:
  https://doi.org/10.1177/03611981211031229
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/issn/03611981
• Supplemental Notes:
  • Jaeyun Lee https://orcid.org/0000-0001-9292-8583 © National Academy of Sciences: Transportation Research Board 2021.
• Authors:
  • Lee, Jaeyun
  • 0000-0001-9292-8583
  • Kang, Sangcheol
  • 0000-0002-5691-5926
  • Lim, Jaedeok
  • 0000-0002-2332-0344
  • Kim, Seong Geon
  • 0000-0003-1876-3199
  • Kim, Changmo
  • 0000-0001-9652-8675
• Publication Date: 2021-12

Language

• English

Media Info

• Media Type: Digital/other
• Features: Figures; Photos; References; Tables;
• Pagination: pp 741-758
• Serial:
  • Transportation Research Record: Journal of the Transportation Research Board
  • Volume: 2675
  • Issue Number: 12
  • Publisher: Sage Publications, Incorporated
  • ISSN: 0361-1981
  • EISSN: 2169-4052
  • Serial URL: http://journals.sagepub.com/home/trr

Subject/Index Terms

• TRT Terms: Artificial intelligence; Detection and identification technologies; High occupancy toll lanes; Machine learning; Managed lanes; Passenger vehicles; Passengers; Weather conditions
• Identifier Terms: YOLO
• Subject Areas: Highways; Operations and Traffic Management; Passenger Transportation;

Filing Info

• Accession Number: 01764127
• Record Type: Publication
• Report/Paper Numbers: TRBAM-21-01099
• Files: TRIS, TRB, ATRI
• Created Date: Feb 4 2021 11:00AM