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      Examining accident reports involving autonomous vehicles in California

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          Abstract

          Autonomous Vehicle technology is quickly expanding its market and has found in Silicon Valley, California, a strong foothold for preliminary testing on public roads. In an effort to promote safety and transparency to consumers, the California Department of Motor Vehicles has mandated that reports of accidents involving autonomous vehicles be drafted and made available to the public. The present work shows an in-depth analysis of the accident reports filed by different manufacturers that are testing autonomous vehicles in California (testing data from September 2014 to March 2017). The data provides important information on autonomous vehicles accidents’ dynamics, related to the most frequent types of collisions and impacts, accident frequencies, and other contributing factors. The study also explores important implications related to future testing and validation of semi-autonomous vehicles, tracing the investigation back to current literature as well as to the current regulatory panorama.

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          Most cited references3

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          Autonomous Vehicles: Disengagements, Accidents and Reaction Times

          Autonomous vehicles are being viewed with scepticism in their ability to improve safety and the driving experience. A critical issue with automated driving at this stage of its development is that it is not yet reliable and safe. When automated driving fails, or is limited, the autonomous mode disengages and the drivers are expected to resume manual driving. For this transition to occur safely, it is imperative that drivers react in an appropriate and timely manner. Recent data released from the California trials provide compelling insights into the current factors influencing disengagements of autonomous mode. Here we show that the number of accidents observed has a significantly high correlation with the autonomous miles travelled. The reaction times to take control of the vehicle in the event of a disengagement was found to have a stable distribution across different companies at 0.83 seconds on average. However, there were differences observed in reaction times based on the type of disengagements, type of roadway and autonomous miles travelled. Lack of trust caused by the exposure to automated disengagements was found to increase the likelihood to take control of the vehicle manually. Further, with increased vehicle miles travelled the reaction times were found to increase, which suggests an increased level of trust with more vehicle miles travelled. We believe that this research would provide insurers, planners, traffic management officials and engineers fundamental insights into trust and reaction times that would help them design and engineer their systems.
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            Driving to Safety: How Many Miles of Driving Would It Take to Demonstrate Autonomous Vehicle Reliability?

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              "Driver distraction: A review of the literature

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                Author and article information

                Contributors
                Role: ConceptualizationRole: Data curationRole: Formal analysisRole: Funding acquisitionRole: InvestigationRole: MethodologyRole: Project administrationRole: ResourcesRole: SoftwareRole: SupervisionRole: ValidationRole: VisualizationRole: Writing – original draftRole: Writing – review & editing
                Role: InvestigationRole: VisualizationRole: Writing – original draft
                Role: Data curationRole: Formal analysisRole: InvestigationRole: Software
                Role: Data curationRole: InvestigationRole: Software
                Role: Data curationRole: SoftwareRole: Visualization
                Role: Editor
                Journal
                PLoS One
                PLoS ONE
                plos
                plosone
                PLoS ONE
                Public Library of Science (San Francisco, CA USA )
                1932-6203
                20 September 2017
                2017
                : 12
                : 9
                : e0184952
                Affiliations
                [1 ] Department of Aviation and Technology, San Jose State University, San Jose, California, United States of America
                [2 ] RiSA 2S Research Center, San Jose State University, San Jose, California, United States of America
                Chongqing University, CHINA
                Author notes

                Competing Interests: The authors have declared that no competing interests exist.

                Author information
                http://orcid.org/0000-0003-0239-0770
                Article
                PONE-D-17-25715
                10.1371/journal.pone.0184952
                5607180
                28931022
                d1c2dd83-a9b8-4ae8-85e9-9b42dce396e6
                © 2017 Favarò et al

                This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

                History
                : 7 July 2017
                : 5 September 2017
                Page count
                Figures: 13, Tables: 4, Pages: 20
                Funding
                Funded by: San Jose State University, College of Engineering, Research Professor
                Award Recipient :
                The present work was funded by the San Jose State University College of Engineering 2017 Grant for Research Professors to FMF. The work of the RiSA2S research center is also supported by the Mineta Transportation Institute (MTI) of San Jose, CA. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
                Categories
                Research Article
                Engineering and Technology
                Civil Engineering
                Transportation Infrastructure
                Roads
                Engineering and Technology
                Transportation
                Transportation Infrastructure
                Roads
                Engineering and Technology
                Control Engineering
                Automation
                Engineering and Technology
                Technology Development
                Prototypes
                Medicine and Health Sciences
                Public and Occupational Health
                Traumatic Injury Risk Factors
                Road Traffic Collisions
                Engineering and Technology
                Transportation
                Engineering and Technology
                Navigation
                Steering
                Computer and Information Sciences
                Software Engineering
                Software Design
                Engineering and Technology
                Software Engineering
                Software Design
                Engineering and Technology
                Mechanical Engineering
                Wheels
                Custom metadata
                All the data used in the paper are publicly available from the websites of the Department of Motor Vehicles of California, the National Highway Safety Administration, and the Federal Highway Administration. References to the specific pages within the websites are found in the paper.

                Uncategorized
                Uncategorized

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