This paper describes a study performed for the U.S. Department of Transportation (USDOT) Intelligent Vehicle Initiative (IVI) to predict the potential performance of a Rear-End Collision Avoidance System (RECAS). The study used data from an on-road driving experiment conducted by the National Highway Traffic Safety Administration (NHTSA) that gathered data on naturalistic driver behavior behind a stopping or stopped vehicle. The study used the range, speed, and leader deceleration data to simulate the calculations of a RECAS warning algorithm every 1/30 second, and calculated the proportion of stopping events that would have triggered a warning alarm if a RECAS had been installed in the following vehicle. The results could be valuable to RECAS designers striving to maximize system effectiveness while minimizing "false alarms."
Samenvatting