Developing AI-driven Safe Navigation Tool

Traffic crashes are a leading cause of death in the United States. The conventional safety evaluation methods incorporate safety modeling to determine the risk scoring of the roadways and provide these risk maps in non-reproducible format. For roadway users, these risk maps are not usable in their daily roadway trips.  On the other hand, popular navigation applications such as Google Maps and Apple Maps provide distance-based or travel time-based alternative routes with no real-time risk scoring. There is a need for a real-time navigation system that can provide data-driven decision on the safest path or route. Obtaining data from several historical and real-time data sources, the user interface of the tool or application can provide the safest navigation decision by examining several scorings such as safety, distance, travel time, and overall scoring. Conducting safety prediction by using multiple big data sources, AI-driven algorithms perform better than conventional statistical models. This study aims to conduct a unique contribution by developing a robust, AI-driven, safe navigation tool, which can provide an informed decision of the safest route instead of providing several uninformed decisions offered by the current navigation tools.

Language

  • English

Project

  • Status: Active
  • Funding: $349970
  • Contract Numbers:

    69A3551747115

  • Sponsor Organizations:

    Office of the Assistant Secretary for Research and Technology

    University Transportation Centers Program
    Department of Transportation
    Washington, DC  United States  20590
  • Managing Organizations:

    Safety through Disruption University Transportation Center (Safe-D)

    Virginia Tech Transportation Institute
    Blacksburg, VA  United States  24060
  • Project Managers:

    Glenn, Eric

  • Performing Organizations:

    Texas A&M Transportation Institute (TTI)

    400 Harvey Mitchell Parkway South
    Suite 300
    College Station, TX  United States  77845-4375
  • Principal Investigators:

    Das, Subasish

  • Start Date: 20210815
  • Expected Completion Date: 20230731
  • Actual Completion Date: 0
  • USDOT Program: University Transportation Centers

Subject/Index Terms

Filing Info

  • Accession Number: 01779769
  • Record Type: Research project
  • Source Agency: Safety through Disruption University Transportation Center (Safe-D)
  • Contract Numbers: 69A3551747115
  • Files: UTC, RIP
  • Created Date: Aug 25 2021 10:48AM