Preparing a nation for autonomous vehicles: opportunities, barriers and policy recommendations

Fagnant, Daniel J.; Kockelman, Kara M.

doi:10.1016/j.tra.2015.04.003

Cited by 1,892 publications

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“…Fagnant and Kockelman (2015) calculate the societal benefits of Level 4 and Level 5 HAVs across a variety of benefit categories, including safety. 4 Drawing on the findings of the National Motor Vehicle Crash Causation Survey, which found that human error accounts for 93 percent of today's crashes (NHTSA, 2008), Fagnant and Kockelman assume in their calculations that HAVs reduce crash and injury rates by 50 percent at the 10-percent market penetration rate and by 90 percent at the 90-percent market penetration rate.…”

Section: Definitions and Prior Workmentioning

confidence: 99%

“…1 Such vehicles have the potential to eliminate many of the mistakes that human drivers routinely make (Anderson et al, 2016;Fagnant and Kockelman, 2015). 2 To begin with, HAVs are never drunk, distracted, or tired; these factors are involved in 29 percent, 10 percent, and 2.5 percent of all fatal crashes, respectively (NHTSA, 2016c;NHTSA, 2016d;NHTSA, 2011).…”

Section: Introductionmentioning

confidence: 99%

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The Enemy of Good: Estimating the Cost of Waiting for Nearly Perfect Automated Vehicles

Kalra¹,

Groves²

2017

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This document and trademark(s) contained herein are protected by law. This representation of RAND intellectual property is provided for noncommercial use only. Unauthorized posting of this publication online is prohibited. Permission is given to duplicate this document for personal use only, as long as it is unaltered and complete. Permission is required from RAND to reproduce, or reuse in another form, any of its research documents for commercial use. For information on reprint and linking permissions, please visit www.rand.org/pubs/permissions.The RAND Corporation is a research organization that develops solutions to public policy challenges to help make communities throughout the world safer and more secure, healthier and more prosperous. RAND is nonprofit, nonpartisan, and committed to the public interest.RAND's publications do not necessarily reflect the opinions of its research clients and sponsors.Support RAND Make a tax-deductible charitable contribution at www.rand.org/giving/contribute www.rand.org For more information on this publication, visit www.rand.org/t/RR2150Library of Congress Cataloging-in-Publication Data is available for this publication.ISBN: 978-0-8330-9937-2 Published by the RAND Corporation, Santa Monica, Calif. © Copyright 2017 RAND CorporationR® is a registered trademark. Cover image: AP Photo/Gene J. Puskar iii PrefaceThe RAND Corporation has a long history of research on intelligent systems. Since the 1950s, with work on chess-playing computers and the Logic Theory Machine, RAND has produced objective, evidence-based research to help inform how society can harness the benefits and manage the risks of intelligent, transformative technologies. RAND's work on autonomous and automated vehicles builds on this firm foundation. RAND Science, Technology, and PolicyThis research was conducted in the RAND Science, Technology, and Policy program, which focuses primarily on the role of scientific development and technological innovation in human behavior, global and regional decisionmaking as it relates to science and technology, and the concurrent effects that science and technology have on policy analysis and policy choices.This program is part of RAND Justice, Infrastructure, and Environment, a division of the RAND Corporation dedicated to improving policy-and decisionmaking in a wide range of policy domains, including civil and criminal justice, infrastructure development and financing, environmental policy, transportation planning and techiv The Enemy of Good: Estimating the Cost of Waiting for Nearly Perfect Automated Vehicles nology, immigration and border protection, public and occupational safety, energy policy, science and innovation policy, space, and telecommunications.During the development of this report and at the time of publication, co-author Nidhi Kalra's spouse served as co-founder and president of Nuro, a machine-learning and robotics start-up company engaged in autonomous vehicle development. He previously served as a principal engineer for Google's driverless car proje...

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Section: Definitions and Prior Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

The Enemy of Good: Estimating the Cost of Waiting for Nearly Perfect Automated Vehicles

Kalra¹,

Groves²

2017

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“…These levels expect no human intervention or driver's attention for safety, hence require automated fail-safe mechanisms. The different levels of automation from manually driven, semi-autonomous to fully autonomous connected vehicles are described and explained in the book: "Preparing a Nation for Autonomous Vehicles" [4]. For this case study, the baseline will be set on level 5, fully autonomous connected cars.…”

Section: Introductionmentioning

confidence: 99%

Proactive Threat Detection for Connected Cars Using Recursive Bayesian Estimation

et al. 2018

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Abstract-Upcoming disruptive technologies around autonomous driving of connected cars have not yet been matched with appropriate security by design principles and lack approaches to incorporate proactive preventative measures in the wake of increased cyber-threats against such systems. In this paper, we introduce proactive anomaly detection to a use-case of hijacked connected cars to improve cyber-resilience. Firstly, we manifest the opportunity of behavioural profiling for connected cars from recent literature covering related underpinning technologies. Then, we design and utilise a new dataset file for connected cars influenced by the Automatic Dependent Surveillance -Broadcast (ADS-B) surveillance technology used in the aerospace industry to facilitate data collection and sharing. Finally, we simulate the analysis of travel routes in real-time to predict anomalies using predictive modelling. Simulations show the applicability of a Bayesian estimation technique, namely Kalman Filter. With the analysis of future state predictions based on the previous behaviour, cyber-threats can be addressed with a vastly increased time-window for a reaction when encountering anomalies. We discuss that detecting real-time deviations for malicious intent with predictive profiling and behavioural algorithms can be superior in effectiveness than the retrospective comparison of known-good/known-bad behaviour. When quicker action can be taken while connected cars encounter cyber-attacks, more effective engagement or interception of command and control will be achieved.

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“…Highly automated cars are estimated to significantly reduce trauma and deaths from road crashes (Fagnant & Kockelman 2013) . The widely cited statistic that "human error is the cause for an estimated 94% of accidents" (Singh 2015) is often used in this context to predict a reduction of said percentage through vehicle automation.…”

Section: Introductionmentioning

confidence: 99%

“…They promise greater mobility, independence, and well being (Fagnant & Kockelman 2013) , as well as environmental benefits by increasing efficiency and reducing congestion. Opinions around when can we have this technology vary widely, not least because of a lack of differentiating between levels of automation (cf.…”

Section: Introductionmentioning

confidence: 99%

Risky Gadgets to the Rescue - Reframing In-Car Technology Use as Task Engagement

Steinberger¹

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Preparing a nation for autonomous vehicles: opportunities, barriers and policy recommendations

Cited by 1,892 publications

References 15 publications

The Enemy of Good: Estimating the Cost of Waiting for Nearly Perfect Automated Vehicles

The Enemy of Good: Estimating the Cost of Waiting for Nearly Perfect Automated Vehicles

Proactive Threat Detection for Connected Cars Using Recursive Bayesian Estimation

Risky Gadgets to the Rescue - Reframing In-Car Technology Use as Task Engagement

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