title: Cost and benefit estimates of partially-automated vehicle collision avoidance technologies.
abstract: Many light-duty vehicle crashes occur due to human error and distracted driving. Partially-automated crash avoidance features offer the potential to reduce the frequency and severity of vehicle crashes 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. This paper evaluates the benefits and costs of fleet-wide deployment of blind spot monitoring, lane departure warning, and forward collision warning crash avoidance systems within the US light-duty vehicle fleet. The three crash avoidance technologies could collectively prevent or reduce the severity of as many as 1.3 million U.S. crashes a year including 133,000 injury crashes and 10,100 fatal crashes. For this paper we made two estimates of potential benefits in the United States: (1) the upper bound fleet-wide technology diffusion benefits by assuming all relevant crashes are avoided and (2) the lower bound fleet-wide benefits of the three technologies based on observed insurance data. The latter represents a lower bound as technology is improved over time and cost reduced with scale economies and technology improvement. All three technologies could collectively provide a lower bound annual benefit of about $18 billion if equipped on all light-duty vehicles. With 2015 pricing of safety options, the total annual costs to equip all light-duty vehicles with the three technologies would be about $13 billion, resulting in an annual net benefit of about $4 billion or a $20 per vehicle net benefit. By assuming all relevant crashes are avoided, the total upper bound annual net benefit from all three technologies combined is about $202 billion or an $861 per vehicle net benefit, at current technology costs. The technologies we are exploring in this paper represent an early form of vehicle automation and a positive net benefit suggests the fleet-wide adoption of these technologies would be beneficial from an economic and social perspective.
authors.affiliation: Civil and Environmental Engineering, Carnegie Mellon University, Porter Hall A7A, 5000 Forbes Avenue, Pittsburgh, PA 15213, USA. Electronic address: cdharper@andrew.cmu.edu.
authors.forename: Corey D
authors.initials: CD
authors.lastname: Harper
authors.affiliation: Civil and Environmental Engineering, Carnegie Mellon University, Porter Hall 123J, 5000 Forbes Avenue, Pittsburgh, PA 15213, USA. Electronic address: cth@cmu.edu.
authors.forename: Chris T
authors.initials: CT
authors.lastname: Hendrickson
authors.affiliation: Civil and Environmental Engineering, Carnegie Mellon University, Porter Hall 103. 5000 Forbes Avenue, Pittsburgh, PA 15213, USA. Electronic address: csamaras@cmu.edu.
authors.forename: Constantine
authors.initials: C
authors.lastname: Samaras
mesh_terms: D000063:Accidents, Traffic / Q000517:prevention & control*; D001331:Automation / Q000191:economics*; D001336:Automobiles / Q000191:economics*; D003362:Cost-Benefit Analysis; D000071826:Distracted Driving; D006801:Humans; D011482:Protective Devices / Q000191:economics* / Q000706:statistics & numerical data; D012449:Safety / Q000191:economics*; D014481:United States
keywords: Automated vehicles; Collision avoidance technologies; Cost-benefit analysis; Vehicle safety