Document Type

Report

Publication Date

Winter 2024

Abstract

This technical report investigates the burgeoning field of Autonomous Vehicles (AVs), a transformative technology set to redefine transportation by enhancing safety, efficiency, and reducing human error. Despite their potential, AVs introduce complex challenges and safety concerns, particularly in urban environments. This study focuses on California from 2014 to 2022, offering a comprehensive examination of AV collision patterns, safety dynamics, and performance compared to Conventional Vehicles (CVs). Therefore, the objectives of this study are to (1) analyze trends in AV crashes to identify recurring patterns or anomalies that may indicate underlying issues or areas for improvement, (2) utilize the latest data sets to predict injury outcomes in AV crashes, aiming to understand the factors that contribute to the severity of injuries and (3) Identify critical determinants of AV crash injuries and conduct a detailed analysis to understand how these factors influence the nature and extent of injuries sustained. Employing a blend of descriptive and spatial analyses, the report delves into the specifics of AV collisions, revealing predominant crash types and significant spatial clusters in major urban centers. The research utilizes various machine learning algorithms to predict crash outcomes, pinpointing critical determinants like vehicle damage and manufacturer. It also assesses the impact of environmental and vehicular factors, such as lighting and weather conditions, on AV collisions. Through detailed scenario analysis, the report explores the diverse challenges AVs face, highlighting the importance of robust design and advanced safety features. The findings aim to inform stakeholders, including manufacturers, policymakers, and urban planners, about the key areas for improvement and collaboration. As AVs continue to evolve, this report underscores the necessity of ongoing research, technological advancements, and strategic planning to ensure their safe and effective integration into the transportation ecosystem.

Comments

Tran-SET Project 22ITSUTA50

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