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Safety of Autonomous Vehicles

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TLDR
In this paper, the levels of automation are reviewed according to the role of the automated system in the autonomous driving process, which will affect the frequency of the disengagements and accidents when driving in autonomous modes.
Abstract
Autonomous vehicle (AV) is regarded as the ultimate solution to future automotive engineering; however, safety still remains the key challenge for the development and commercialization of the AVs. Therefore, a comprehensive understanding of the development status of AVs and reported accidents is becoming urgent. In this article, the levels of automation are reviewed according to the role of the automated system in the autonomous driving process, which will affect the frequency of the disengagements and accidents when driving in autonomous modes. Additionally, the public on-road AV accident reports are statistically analyzed. The results show that over 3.7 million miles have been tested for AVs by various manufacturers from 2014 to 2018. The AVs are frequently taken over by drivers if they deem necessary, and the disengagement frequency varies significantly from 2 × 10−4 to 3 disengagements per mile for different manufacturers. In addition, 128 accidents in 2014–2018 are studied, and about 63% of the total accidents are caused in autonomous mode. A small fraction of the total accidents (∼6%) is directly related to the AVs, while 94% of the accidents are passively initiated by the other parties, including pedestrians, cyclists, motorcycles, and conventional vehicles. These safety risks identified during on-road testing, represented by disengagements and actual accidents, indicate that the passive accidents which are caused by other road users are the majority. The capability of AVs to alert and avoid safety risks caused by the other parties and to make safe decisions to prevent possible fatal accidents would significantly improve the safety of AVs. Practical applications. This literature review summarizes the safety-related issues for AVs by theoretical analysis of the AV systems and statistical investigation of the disengagement and accident reports for on-road testing, and the findings will help inform future research efforts for AV developments.

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References
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Journal ArticleDOI

A model for types and levels of human interaction with automation

TL;DR: A model for types and levels of automation is outlined that can be applied to four broad classes of functions: 1) information acquisition; 2) information analysis; 3) decision and action selection; and 4) action implementation.
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Preparing a nation for autonomous vehicles: opportunities, barriers and policy recommendations

TL;DR: In this article, the authors proposed a nationally recognized licensing framework for AVs, determining appropriate standards for liability, security, and data privacy, which can be used to improve vehicle safety, congestion, and travel behavior.

Preparing a Nation for Autonomous Vehicles: Opportunities, Barriers and Policy Recommendations

TL;DR: In this paper, the authors proposed a nationally recognized licensing framework for AVs, determining appropriate standards for liability, security, and data privacy for personal travel in the United States, which is based on the work of the authors of this paper.
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A Review of Motion Planning Techniques for Automated Vehicles

TL;DR: A review of motion planning techniques implemented in the intelligent vehicles literature, with a description of the technique used by research teams, their contributions in motion planning, and a comparison among these techniques is presented.
Book

Autonomous Vehicle Technology: A Guide for Policymakers

TL;DR: In this paper, the potential advantages of autonomous vehicles, including safety, mobility and fuel consumption, and disadvantages, including travel demand, are discussed, and guidance for policymakers and risks to be considered as policies are formulated.
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