Development of an errorable car-following driver model

Abstract

An errorable car-following driver model is presented in this paper. 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. This model was developed for evaluation and design of active safety systems. The car-following data used for developing and validating the model was obtained from a large-scale naturalistic driving database. The stochastic car-following behavior was first analyzed and modeled as a random process. Three error-inducing behaviors were then introduced. First, human perceptual limitation was studied and implemented. Distraction due to non-driving tasks was then identified based on the statistic analysis of the driving data. Finally, time delay of human drivers was estimated through a recursive least square identification process. By including these three error-inducing behaviors, rear-end collisions with the lead vehicle could occur. The simulated crash rate was found to be similar but somewhat higher than that reported in traffic statistics.

Simplified, data-driven, errorable car-following model to predict the safety effects of distracted driving

“…An errorable car-following model has been proposed by Yang and Peng [19] for the evaluation and design of active safety vehicle systems. The data to validate the model was obtained from the Road-Departure Crash-Warning System Field Operational Test (RDCW) large-scale naturalistic driving database.…”

A new fundamental diagram theory with the individual difference of the driver’s perception ability

“…To overcome this drawback, Ngody [76,81] developed a new multiclass first-order model and found the widely scattered data are resulted by the mixture of traffic flow and some stochastic factors. Yang and Peng [82] proposed an errorable car-following model that is more useful to design and evaluate an active safety vehicle system; Li et al [83] presented a new LWR (Lighthill-Whitham-Richards) model with stochastic free flow speed. The above fundamental diagram theory can successfully explain some complex traffic phenomena, but they cannot be used to explore the effects of the driver's individual difference on traffic flow since they did not explicitly consider the factor.…”

“…In order to study the scattered data, some new models were proposed based on many observed data and the formation mechanism of the widely scattered flow-density data can reasonably be explained from some specific perspectives [72][73][74][75][77][78][79][80], but they cannot be completely the stochastic factor because some stochastic factors were not explicitly considered yet. In order to further enrich the fundamental diagram theory, some new models with the stochastic factors were developed [76,[81][82][83], but they cannot be used to describe the effects of the driver's individual difference on traffic flow since they did not consider this factor. In fact, the driver's perception ability has the individual difference, so we must consider the individual difference when we study traffic flow.…”

Driver`s Steering Behaviour Identification and Modelling in Near Rear-End collision

“…Currently, driver behaviour identification and modelling received much attention from researchers and gives great impact in many, such as traffic flow microscopic simulation [8], Intelligent Transportation system for Advance Driving Assistance system (ADAS) [9] and vehicle dynamic [10]. Thus, the purpose of this work is to identify and model the driver`s steering The neural network approach is proposed to establish the driver's steering manoeuvre behaviour model.…”