United States Department of Transportation
i
Safety and Cost Assessment of Connected and Automated Vehicles
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2018-03-29
[PDF-376.17 KB]
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TRIS Online Accession Number:01603608
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Edition:Final Research Report
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Abstract:Many light-duty vehicle crashes occur due to human error and distracted driving. The National Highway Traffic Safety Administration (NHTSA) reports that ten percent of all fatal crashes and seventeen percent of injury crashes in 2011 were a result of distracted driving, while close to ninety percent of all crashes occur in part due to human error (NHTSA, 2013a; Olarte, 2011). Crash avoidance features offer the potential to substantially reduce the frequency and severity of vehicle crashes and deaths that occur due to distracted driving and/or human error by assisting in maintaining control of the vehicle or issuing alerts if a potentially dangerous situation is detected. As the automobile industry transitions to partial vehicle automation, newer crash avoidance technologies are beginning to appear more frequently in non-luxury vehicles such as the Honda Accord and Mazda CX-9. The availability of Forward Collision Warning (FCW), Lane Departure Warning (LDW), and Blind Spot Monitoring (BSM) technologies could reach 95% of the registered vehicle fleet anywhere between the years 2032 and 2048 (HLDI, 2014a). The market penetration rate of these technologies depends on government mandates that could speed up implementation by up to 15 years (HLDI, 2014a). Automated vehicle technologies could have significant economic net benefits due to crash reduction (including direct cost savings and associated roadway congestion), enabling greater mobility for the disabled and elderly, and improved fuel economy due to more efficient driving (Anderson et al., 2014). This paper estimates the costs and benefits of large-scale deployment of BSM, LDW, and FCW crash avoidance systems within the light-duty vehicle fleet. Two estimates are made to provide insight on current trends and technology potential. First, an upper bound of relevant crashes that potentially could be avoided or made less severe by the three technologies is estimated, assuming 100% technology effectiveness. Next, a lower bound in crash reduction is estimated using current changes in observed insurance collision claim frequency and severity (average loss payment per claim) in motor vehicles with these technologies. After these estimates are made, an annualized cost to equip each vehicle with the technologies enables a cost benefit analysis for the lower bound and upper bound estimates.
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