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 the data-driven decision on the safest path or route. By leveraging data from a diverse range of historical and real-time sources, this study successfully developed a user interface for a navigation tool or application that offers informed and data-driven decisions regarding the safest navigation options. The interface considers multiple scoring factors, including safety, distance, travel time, and an overall scoring metric. This study made a distinctive and valuable contribution by designing and implementing a robust safe navigation tool driven by artificial intelligence. Unlike existing navigation tools that offer multiple uninformed route options, this tool provides users with an informed decision on the safest route. By leveraging advanced AI algorithms and integrating various data sources, this navigation tool enhances the accuracy and reliability of route selection, thereby improving overall road safety and ensuring users can make informed decisions for their journeys.
- This study used Google Directions API and Open Route Services (ORS) API to explore the route-finding algorithms with the inclusion of safety risk scores of different routes.
- Incorporated advanced AI algorithms and various data sources to develop a robust navigation tool that significantly enhances the accuracy and reliability of route selection, ultimately improving overall road safety and enabling users to make well-informed decisions for their journeys.
- Highlights the potential of AI-driven technologies to revolutionize the field of navigation and enhance road user safety.
EWD & T2 Products
A published book with a section discussing details on AI-driven safe navigation tool development
- Das, S. Artificial Intelligence in Highway Safety. CRC Press, Boca Raton, FL, 2022. ISBN 9780367436704. https://www.routledge.com/Artificial-Intelligence-in-Highway-Safety/Das/p/book/9780367436704
Student Impact Statement(pdf): Three students received funding under this project (Yanmo Weng and Shoujia Li, Master students at Texas A&M and Valerie Vierkant, an undergraduate student from VT). This file contains a statement by Yanmo Weng and Valerie Vierkant as to the impact this project had on their education and workforce development.
Das developed a three-hour-long workshop as part of National Summer Transportation Institute (NSTI), July 9-21, 2023 at Texas State University.
Rahman, M. M., Das, S., & Tesic, J. Deep Learning-based Approach for Urban Driveway Identification from Aerial Imagery. TRB Annual Meeting, Washington DC, 2024.
S. Sohrabi, Y. Wang, S. Das, & S. Paal. Safe route-finding: A review of literature and future directions. Accident Analysis & Prevention, Volume 177, 2022.
Web-based safe-navigation tool: https://aitlab.shinyapps.io/SafeR_V01/
The final datasets for this project are located in the Safe-D Collection on the VTTI Dataverse; DOI: 10.15787/VTT1/AL4C8V.
Research Investigators (PI*)
Start Date: 2021-08-15
End Date: 2023-07-31
Grant Number: 69A3551747115
Total Funding: $349,970
Source Organization: Safe-D National UTC
Project Number: 06-002
Safe-D Theme Areas
Safe-D Application Areas
Planning for Safety
Office of the Assistant Secretary for Research and Technology
University Transportation Centers Program
Department of Transportation
Washington, DC 20590 United States
Texas A&M University
Texas A&M Transportation Institute
College Station, Texas 77843-3135