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).

[1]  Eric Rodgman,et al.  The role of driver distraction in traffic crashes , 2001 .

[2]  Y. Sugiyama,et al.  Phenomenological Study of Dynamical Model of Traffic Flow , 1995 .

[3]  R. M. Michaels,et al.  Perceptual Factors in Car-Following , 1963 .

[4]  Robert H. Brown,et al.  Weber Ratio for Visual Discrimination of Velocity , 1960, Science.

[5]  Wei Zhang,et al.  Improved action point model in traffic flow based on driver's cognitive mechanism , 2004, IEEE Intelligent Vehicles Symposium, 2004.

[6]  Peter Thier,et al.  Detection of speed changes during pursuit eye movements , 2006, Experimental Brain Research.

[7]  W. D. Jones,et al.  Keeping cars from crashing , 2001 .

[8]  Gawron,et al.  Continuous limit of the Nagel-Schreckenberg model. , 1996, Physical review. E, Statistical physics, plasmas, fluids, and related interdisciplinary topics.

[9]  J. Tyler.  The characteristics of model-following systems as synthesized by optimal control , 1964 .

[10]  P. G. Gipps,et al.  A behavioural car-following model for computer simulation , 1981 .

[11]  G. F. Newell.  Nonlinear Effects in the Dynamics of Car Following , 1961 .

[12]  Ihor Lubashevsky,et al.  Empirical basis for car-following theory development , 2003 .

[13]  W Leutzbach,et al.  DEVELOPMENT AND APPLICATIONS OF TRAFFIC SIMULATION MODELS AT THE KARLSRUHE INSTITUT FUR VERKEHRWESEN , 1986 .

[14]  David J. Cole,et al.  Neuromuscular dynamics and the vehicle steering task , 2004 .

[15]  Kai Nagel,et al.  Probabilistic Traffic Flow Breakdown in Stochastic Car-Following Models , 2002, cond-mat/0208082.

[16]  D. Gazis,et al.  Nonlinear Follow-the-Leader Models of Traffic Flow , 1961 .

[17]  Michael Schreckenberg,et al.  A cellular automaton model for freeway traffic , 1992 .

[18]  Hiromitsu Kumamoto,et al.  RULE-BASED COGNITIVE ANIMATION SIMULATOR FOR CURRENT LANE AND LANE CHANGE DRIVERS , 1995 .

[19]  Mike Flynn,et al.  AUTOMOTIVE INDUSTRY TRENDS IN ELECTRONICS : YEAR 2000 SURVEY OF SENIOR EXECUTIVES , 2001 .

[20]  Huei Peng.  Evaluation of Driver Assistance Systems—A Human Centered Approach , 2002 .

[21]  L. A. Pipes.  An Operational Analysis of Traffic Dynamics , 1953 .

[22]  R. Bishop,et al.  A survey of intelligent vehicle applications worldwide , 2000, Proceedings of the IEEE Intelligent Vehicles Symposium 2000 (Cat. No.00TH8511).

[23]  Stephen J Heinen,et al.  Perceptual and oculomotor evidence of limitations on processing accelerating motion. , 2003, Journal of vision.

[24]  Julie M. Harris,et al.  Speed discrimination of motion-in-depth using binocular cues , 1995, Vision Research.

[25]  Huei Peng,et al.  Identification and verification of a longitudinal human driving model for collision warning and avoidance systems , 2004 .