An Exploratory Analysis of Crashes Involving Autonomous Vehicles

Recent advancements in autonomous vehicle (AV) technology have the potential to reduce road accidents caused by human error. However, to enhance their safety and performance, it is crucial to understand the patterns of AV collisions. This study examines AV collisions by analyzing their temporal and spatial patterns. Based on reports from the California DMV between 2014 and 2022, the analysis reveals that rear-end collisions are the most common type, while incidents involving pedestrians and overturned vehicles are rare. The majority of collisions involve mid-sized vehicles, and AVs are responsible for a minority of accidents. The study also identifies clusters of incidents in San Francisco, San Jose, Los Angeles, and San Diego, with San Francisco having largest concentration. Specific areas within San Francisco, like Mission District, Japantown, Union Square, and North Beach neighborhoods, show high incident rates. These findings highlight safety concerns, and aid in integration of AVs into transportation infrastructure.

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An Exploratory Analysis of Crashes Involving Autonomous Vehicles

Cited by 3 publications

References 18 publications

Barriers and motivators to the adoption of electric vehicles: A global review

Barriers and motivators to the adoption of electric vehicles: A global review

Understanding key factors in automated vehicle collisions: Automating data extraction and analyzing key insights using explainable AI

An Exploratory Analysis of Temporal and Spatial Patterns of Autonomous Vehicle Collisions

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