Completed 2017 Research Project

Using advanced analytics to frame vulnerable road user scenarios with autonomous vehicles

Principal Investigator
Noreen McDonald
University of North Carolina, Chapel Hill
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Co-Investigator
Asad J. Khattak
University of Tennessee, Knoxville
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Full Report

Project Slide Deck

Research Brief

Summary

Connected and automated vehicle (CAV) technologies can dramatically improve safety by reducing human errors, which contribute substantially (an estimated 94 percent) to roadway crashes. CAVs can eventually operate effectively on roadways without experiencing decreased performance due to distraction or fatigue.

However, technological advances will not uniformly decrease crash risks. Some environments, crash types, and user groups will continue to experience elevated risks, particularly vulnerable road users such as pedestrians.

This project addresses these critical safety issues by:

1) Assessing the current and future landscape of pedestrian and vehicle conflicts.

2) Identifying how vehicle technology, planning policies, and data analytics can provide systemic solutions to pedestrian-vehicle conflicts.

3) Using data analytics to identify dangerous pre-crash behaviors.

This trans-disciplinary and multimodal approach is critical because solutions require insights from multiple fields. The information presented includes literature reviews on current patterns of pedestrian-vehicle conflicts, assessment of how planning and physical design strategies can reduce pedestrian-CAV conflicts. Furthermore, risk analysis was conducted based on Fatality Accident Reporting System (FARS) data, and SHRP2 Naturalistic Driving Study data.

An assessment of how automated vehicle technology will impact crash risk and how future countermeasures may change with the adoption of emerging technologies is provided. The team has analyzed safety data from diverse sources and propose a framework to link automation technology to human error/crash typologies.

Overall, the study applies innovative statistical, artificial intelligence, and visualization tools to extract valuable information from studies and data, with the purpose of improving safety across modes, especially for vulnerable road users.

Publications

Project Details

Project Type: Research
Project Status: Completed
Start Date: 3-1-2017
End Date: 9-30-2018
Contract Year: Year 1
Total Funding from CSCRS: $340,000
Co-sponsors: Tennessee Department of Transportation
Collaborating Organizations: University of North Carolina, Chapel Hill; University of Tennessee, Knoxville