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This paper characterizes the capability of a rear-end crash avoidance system based on data collected from a field operational test. The system performs forward crash warning and adaptive cruise control functions. The test consists of 66 subjects who drove 10 equipped vehicles on public roads over 157,000 km. System characterization addresses the ability of the forward-looking sensor suite to maintain in-path target tracking and discern between in-path and out-of-path targets; the efficacy of the alert logic in warning the driver to driving conflicts that may lead to rear-end crashes; and the visibility, audibility, and readability of information displayed by the driver-vehicle interface.

This paper describes an algorithm that identifies the state of traffic ahead of a moving vehicle using onboard sensors. This algorithm approximates the level of service as defined in the Highway Capacity Manual, which portrays a range of traffic conditions on a particular type of roadway facility. The traffic state forms an independent variable in an evaluation plan to assess the benefits and capability of an automotive rear-end crash avoidance system in a field operational test. The algorithm utilizes inputs from vehicle sensors, onboard radar, global positioning system, and digital map to classify the traffic ahead into light, medium, and heavy states. Basically, the algorithm segregates the roadway into four different categories based on the road type (freeway or non-freeway), posted speed limit, and traffic flow conditions.