User Acceptance and Public Policy Implications for Deployment of Automated Driving Systems

Research Team: Ching-Yao Chan (lead), Pei Wang, and Sanaz Motamedi

UC Campus(es): UC Berkeley

Problem Statement: Many challenges are yet to be addressed for automated driving systems and self-driving cars, which include public perception, liability issues, security, and control of the systems. The question of public perception is vital to determine the extent to which people will accept and pay for automated driving technologies.

Project Description: The objective of this project is to understand public perception of Automated Driving Systems (ADS) and to develop acceptance models that can help understand users’ intentions to use fully ADS, including both personally owned fully ADS and shared-use fully ADS. This project consisted of three phases, including (1) indepth interviews with end-users of partially ADS, (2) interviews with experts in the transportation domain regarding policy gaps for deployment of ADS, and (3) focus group and online surveys to understand public perception and acceptance model of fully ADS. Findings from this study show that safety, vehicle control and compatibility, and trust are the three most critical factors that have influence on users’ acceptance of the fully automated driving systems.

Status: Completed

Budget: $75,000

Project Partner(s): Caltrans

Report(s):
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Policy Brief(s):
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