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IPOD Abstract for presentation (Poster or Podium)

AI in Transportation

1666924 - Emerging Applications of Artificial Intelligence & Computer Vision in Transportation Safety

Tuesday, June 18, 2024
8:50 AM - 9:10 AM ET
Room: 217

    Primary Author(s)

  • Behnam Jamali

    Computer Vision & AI Specialist
    Transoft Solutions
    Montreal, QC, Canada

    • Co-Author(s)

  • Behnam Jamali

    Computer Vision & AI Specialist
    Transoft Solutions
    Montreal, QC, Canada

    • Presenter(s)

  • Behnam Jamali

    Computer Vision & AI Specialist
    Transoft Solutions
    Montreal, QC, Canada

    • Co-Author(s)

  • Karen Giese, P.E.

    Vice President, US Business Development
    Transoft Solutions
    Richmond, BC, Canada

    • Presenter(s)

  • Karen Giese, P.E.

    Vice President, US Business Development
    Transoft Solutions
    Richmond, BC, Canada

    • Submitter(s)

  • BJ

    Behnam Jamali

    Transoft Solutions
    Montreal, Quebec, Canada

Abstract: In recent years, the integration of Artificial Intelligence (AI) and Computer Vision into the field of transportation has indicated significant advancements, especially in the domain of safety. As AI-driven models become increasingly adept at image and video recognition, their incorporation in transportation systems offers a considerable reduction in traffic-related accidents and fatalities. Furthermore, through continuous learning and improvements, these algorithms adapt and evolve, ensuring that they remain relevant in changing traffic conditions and circumstances. Computer Vision systems play a crucial role in road user detection and tracking, traffic flow optimization, and infrastructure maintenance by processing large amounts of visual data. Additionally, the integration of AI with Computer Vision offers significant potential in preventing risks, understanding crash causality, and formulating proactive solutions. While these advancements promise a safer transportation ecosystem, challenges such as ethical considerations, data privacy, and technological reliability are major concerns. This presentation will provide a comprehensive overview of the potential of AI and Computer Vision in creating a safer transportation future. It specifically demonstrates the feasibility of implementing this technology and its future advantages and challenges.   

Learning Objectives:

Attendees can expect to learn the following from this session:
  • Understand the implementation of computer vision and artificial intelligence in video-based classification of road users, aligning with the vehicle classification standard set by the Federal Highway Administration (FHWA)
  • Understand objective measurements of two safety indicators, Post-Encroachment Time (PET) and Time-to-Collision (TTC), using traffic cameras and computer vision technology
  • List three primary categories of safety analysis deployments that uses computer vision and AI technology