Development and validation of an errorable car-following driver model

An errorable car-following driver model was presented in this paper. This model was developed for evaluating and designing of active safety technology. Longitudinal driving was first characterized from a naturalistic driving database. The stochastic part of longitudinal driving behavior was then studied and modeled by a random process. The resulting stochastic car-following model can reproduce the normal driver behavior and occasional deviations without crash. To make this model errorable, three error-inducing behaviors were analyzed. Perceptual limitation was studied and implemented as a quantizer. Next, based on the statistic analysis of the experimental data, the distracted driving was identified and modeled by a stochastic process. Later on, time delay was estimated by recursive least square method and was modeled by a stochastic process as well. These two processes were introduced as random disturbance of the stochastic driver model. With certain combination of those three error-inducing behaviors, accident/incident could happen. Twenty-five crashes happened after eight million miles simulation (272/100M VMT). This simulation crash rate is higher by about twice with 2005 NHTSA data (120/100M VMT).