Cyclists are overrepresented among motor vehicle crash fatalities. Detailed information regarding common cyclist crash scenarios and relevant crash factors is crucial to the development of cyclist detection warning and crash avoidance systems that could prevent these crashes and fatalities. Motor vehicle‐cyclist crash data from federally maintained national databases were used to identify common and fatal crash scenarios between cyclists and motor vehicles. The most common fatal crash modes involved the motor vehicle‐cyclist movement combinations straight‐in line, straight‐crossing, and straight‐against. The most common crash modes involved the movement combinations straight‐crossing, turning‐crossing, and turning‐in line. Crashes that occurred in non‐daylight conditions and on roads with speed limits of 40 mi/h and greater contributed to the greatest percentage of fatalities. Cyclist detection systems that function at high speeds and in both daylight and non‐daylight conditions offer the greatest potential benefit. Effective cyclist detection systems designed to function in scenarios like the three common fatal crash modes and two additional most common crash modes could help mitigate or prevent up to 47% of crashes, 48% of injuries, and 54% of fatalities, potentially saving up to 363 lives annually.
[1]
Jessica S Jermakian,et al.
Crash avoidance potential of four passenger vehicle technologies.
,
2011,
Accident; analysis and prevention.
[2]
Antje Lang,et al.
Cyclist-car accidents: their consequences for cyclists and typical accident scenarios
,
2015
.
[3]
A. Bauman,et al.
Walking and cycling in the United States, 2001-2009: evidence from the National Household Travel Surveys.
,
2011,
American journal of public health.
[4]
David Zuby,et al.
Collision Avoidance Features: Initial Results
,
2013
.
[5]
M. Harris,et al.
Impact of Transportation Infrastructure on Bicycling Injuries and Crashes : a Review of the Literature
,
2009
.
[6]
A Lie,et al.
Effectiveness of low speed autonomous emergency braking in real-world rear-end crashes.
,
2015,
Accident; analysis and prevention.