A perceptual forward collision warning model using naturalistic driving data

Rear-end collisions represent a quarter to one-third of the total number of collisions occurring on North American roads. While there are several methods to mitigate rear-end collision effects, one way is to warn drivers about impending events using forward collision warning (FCW) systems. At the core of any FCW algorithm is a trigger distance at which a message is relayed to the driver to avoid rear-end collisions. The main goal of this paper is to propose a warning distance model based on naturalistic driver following behavior. This was achieved by investigating car-following events within a critical time-to-collision range. A total of 5785 candidate car-following events were identified for the model development from 2 months of naturalistic driving study data of 63 drivers. Using regression analysis, the minimum warning distance was linked to several performance measures. It was found that the relative speed, the host vehicle speed, and the host vehicle acceleration can significantly affect the minimum warning distance. To assess the performance of the developed algorithm, it was compared to six of the existing FCW algorithms in terms of warning distances. The results of the developed algorithm were consistent with the other perceptual FCW algorithms. However, the warning distances of the proposed algorithm were less than the distances produced by the kinematic algorithms. The proposed algorithm could be used as a minimum threshold to trigger an alert for an FCW algorithm. Since the proposed algorithm is developed based on actual driving data, it is expected to be more acceptable by drivers. However, the algorithm needs further testing in real-life to validate this expectation.