Expanding vision-based ADAS for non-structured environments

Advanced driver assistance systems (ADAS) become an integral part of almost all modern automotive systems. ADAS have been evolving over a decade and the expansion of vision-based ADAS is quite rapid mainly due to the recent advancements in camera technologies. Most of the vision-based ADAS applications have been developed focusing on structured environment parameters and being tested adequately for those environments whereas they cannot be applied with their current framework as such for non-structured environments due to various limitations. This study presents a comprehensive overview of challenges in expanding the vision-based ADAS for non-structured environments. The authors have proposed a segmentation detection method for pedestrians and cyclists in a non-structured road environment to improve the accuracy of the popular deep learning networks. This method uses upper body detection and a pairing technique that improves the average precision significantly without consuming much computational resources. This approach would help to transform the structured environment ADAS to non-structured environments with minimal modifications. With the proposed approach, they are able to increase the accuracy of certain object classes up to 49% for various popular deep learning networks.

• Record URL:
  http://dx.doi.org/10.1049/iet-its.2019.0530
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/issn/1751956X
• Supplemental Notes:
  • Abstract reprinted with permission of the Institution of Engineering and Technology.
• Authors:
  • Antony, Joseph
  • Manikandan, Suchetha
• Publication Date: 2020-6

Language

• English

Media Info

• Media Type: Web
• Features: References;
• Pagination: pp 620-627
• Serial:
  • IET Intelligent Transport Systems
  • Volume: 14
  • Issue Number: 6
  • Publisher: Institution of Engineering and Technology (IET)
  • ISSN: 1751-956X
  • EISSN: 1751-9578
  • Serial URL: https://ietresearch.onlinelibrary.wiley.com/journal/17519578
• Publication flags:

  Open Access (libre)

Subject/Index Terms

• TRT Terms: Artificial intelligence; Cyclists; Detection and identification systems; Driver support systems; Highway safety; Machine learning; Machine vision; Pedestrians; Traffic crashes
• Subject Areas: Highways; Safety and Human Factors; Vehicles and Equipment;

Filing Info

• Accession Number: 01773072
• Record Type: Publication
• Files: TRIS
• Created Date: May 26 2021 11:19AM