Crash Avoidance Technology Evaluation Using Real-World Crash Data
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2020-03-01
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Edition:Final Report
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Abstract:This study used data on primarily optional safety content from 1.2 million General Motors (GM) vehicles linked to State police-reported crash data by Vehicle Identification Number to estimate field performance of a variety of new safety technologies. After linkage, there were 35,401 vehicles in our analysis dataset. This data included both an indication (presence/absence) of certain types of safety equipment on each vehicle, as well as a variety of crash descriptors at the crash, vehicle, and driver levels. Available covariates were also used to attempt to control for variables that might influence system-relevant crash involvement for the systems examined, including driver age and gender, speed limit, alcohol/drug presence, fatigue, weather, road surface condition, vehicle type, and vehicle model.
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Main Document Checksum:urn:sha-512:cdd25ab6465fe673d603be60e3895d42f96580f2834ca31d2995bf10635ffec2b009809031cd0bc479b4d1d9e13d7d7854437f4449933bbf878fc190a3efb75c
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