Development and validation of an errorable car-following driver model

Abstract

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). ©2008 AACC

Driver Modeling for Simulation of Transportation Systems

“…Recently, Yang [12][13] and colleagues developed an errorable car-following driver model. An errorable driver model is one that emulates human driver's functions and can generate both nominal (error-free) as well as devious (with error) behaviors.…”

“…Model developed by Yang and colleagues [12][13] focus on simulating aggregate or average outcomes and is useful for activities such as evaluating and designing active safety technology.…”

A Test Scenario Automatic Generation Strategy for Intelligent Driving Systems

“…To improve N-FOT, some researchers use the data collected from naturalistic traffic to build a stochastic driving behavior model to generate test scenarios, which is called MCS. Based on this approach, Yang et al developed a car-following driver model to evaluate FCW and AEB [6]. Compared with N-FOT, MCS can partly extend the test condition by the driving behavior model, ensure a good consistency with real traffic, and reduce the similar and simple scenarios to increase test efficiency.…”

Development of Driver Model for Lane Keeping Manoeuvre

“…In the literature, a wide range of human driver models implementing various techniques can be found. There are various reasons for the development of human driver modelling, namely for vehicle following [7]- [10], road following [11], collision avoidance [12], lane keeping on a curving road [13]- [15], and path following [16].…”