Semester

Summer

Date of Graduation

2021

Document Type

Thesis

Degree Type

MS

College

Statler College of Engineering and Mineral Resources

Department

Civil and Environmental Engineering

Committee Chair

Kakan Dey

Committee Member

David Martinelli

Committee Member

Dimitra Pyrialakou

Abstract

Autonomous Vehicles (AVs) can dramatically reduce the number of traffic crashes and associated fatalities by eliminating the avoidable human-errors related crash contributing factors. Many companies have been conducting pilot tests on public roads in several states in the United States and other countries to accelerate AV mass deployment. AV pilot operations on California public roads saw 251 AV-involved crashes (as of February 2020). These AV-involved crashes provide a unique opportunity to investigate AV crash risks in a mixed traffic environment. This study collected the AV crash reports from the California Department of Motor Vehicles and applied the Decision Tree (DT), and Association Rule methods to extract the pre-crash rules of AV-involved crashes. Extracted rules revealed that the most frequent types of AV crashes were rear-end crashes and predominantly occurred at intersections when AVs were stopped and engaged in the autonomous mode. AV and Non-AV manufacturers, and transportation agencies can use the findings of this study to minimize AV-related crashes. AV companies could install a distinct signal/display to inform the operational mode of the AVs (i.e., autonomous or non-autonomous) to human drivers around them. Moreover, the Automatic Emergency Braking system in non-AVs could avoid a significant number of rear-end crashes as often rear-end crashes occurred due to the failure of following non-AVs timely slow down behind AVs. Transportation agencies can consider separating the AVs from the non-AVs by assigning “AV only lanes” to eliminate the excessive rear-end crashes due to the mistakes of human drivers in non-AVs at the intersections.

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