Development and validation of an errorable car-following driver model

Yang, H.-H., Peng, H., Gordon, T. J. and Leblanc, D. (2008) Development and validation of an errorable car-following driver model. In: American Control Conference, 2008, 11 - 13 June 2008, Seattle, WA.

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Item Type:Conference or Workshop contribution (Paper)
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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.

Additional Information:Conference Code:73572
Keywords:Automobile drivers, Curve fitting, Delay control systems, Least squares approximations, Random processes, Stochastic programming, Active safety, Car-following, Car-following models, Crash rates, Driver behavior, Driver modeling, Driving database, Experimental data, Longitudinal driving, Quantizer, Random disturbances, Recursive Least Square method, Statistic analysis, Stochastic driver model, Stochastic processing, Time delaying, Stochastic models
Subjects:H Engineering > H331 Road Vehicle Engineering
Divisions:College of Science > School of Engineering
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ID Code:11664
Deposited On:13 Feb 2014 09:54

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