Driving behaviour models enabling the simulation of Advanced Driving Assistance Systems: revisiting the Action Point paradigm

In the field of Intelligent Transportation Systems (ITS), one of the most promising sub-functions is that of Advanced Driver Assistance Systems (ADAS). Development of an effective ADAS, and one that is able to gain drivers’ acceptance, hinges on the development of a human-like car-following model, and this is particularly important in order to ensure the driver is always ‘in the (vehicle control) loop’ and is able to recover control safely in any situation where the ADAS may release control. One of the most commonly used models of car-following is that of the Action Point (AP) (psychophysical) paradigm. However, while this is widely used in both micro-simulation models and behavioural research, the approach is not without its weaknesses. One of these, the potential redundancy of some of the identified APs, is examined in this paper and its basic structure validated using microscopic driving behaviour collected on thirteen subjects in Italy. Another weakness in practical application of the Action Point theory is the identification of appropriate thresholds, accounting for the perception, reaction and adjustment of relative speed (or spacing) from the leading vehicle. This article shows that this identification is problematic if the Action Point paradigm is analysed in a traditional way (car-following spirals), while it is easier if the phenomenon is analysed in terms of car-following ‘waves’, related to Time To Collision (TTC) or the inverse of TTC. Within this new interpretative framework, the observed action points can be observed to follow a characteristically linear pattern. The identification of the most significant variables to be taken into account, and their characterisation by means of a simple linear pattern, allows for the formulation of more efficient real-time applications, thereby contributing to the development and diffusion of emerging on-board technologies in the field of vehicle control and driver’s assistance.

[1]  E. Kometani,et al.  On the stability of traffic flow , 1958 .

[2]  Mike McDonald,et al.  Car-following: a historical review , 1999 .

[3]  M.M. Trivedi,et al.  Design of an instrumented vehicle test bed for developing a human centered driver support system , 2004, IEEE Intelligent Vehicles Symposium, 2004.

[4]  Roberta Di Pace,et al.  Real-time Smoothing of Car-following Data Through Sensor-fusion Techniques , 2011 .

[5]  Mike McDonald,et al.  Motorway driver behaviour: studies on car following , 2002 .

[6]  Rainer Wiedemann,et al.  SIMULATION DES STRASSENVERKEHRSFLUSSES. , 1974 .

[7]  Xiaoliang Ma,et al.  Behavior Measurement, Analysis, and Regime Classification in Car Following , 2007, IEEE Transactions on Intelligent Transportation Systems.

[8]  J Treiterer,et al.  THE HYSTERESIS PHENOMENON IN TRAFFIC FLOW , 1974 .

[9]  D Regan,et al.  Accuracy of estimating time to collision using binocular and monocular information , 1998, Vision Research.

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

[11]  Prakash Ranjitkar,et al.  Multiple Car-Following Data with Real-Time Kinematic Global Positioning System , 2002 .

[12]  Marika Hoedemaeker,et al.  Behavioural adaptation to driving with an adaptive cruise control (ACC) , 1998 .

[13]  Gordon F. Newell,et al.  INSTABILITY IN DENSE HIGHWAY TRAFFIC: A REVIEW. , 1965 .

[14]  Leonard Evans,et al.  Perceptual Thresholds in Car-Following---A Comparison of Recent Measurements with Earlier Results , 1977 .

[15]  Edward Chung,et al.  An Examination of the Microscopic Simulation Models to Identify Traffic Safety Indicators , 2012, Int. J. Intell. Transp. Syst. Res..

[16]  Serge P. Hoogendoorn,et al.  Wiedemann Revisited , 2011 .

[17]  Neville A Stanton,et al.  Driver behaviour with adaptive cruise control , 2005, Ergonomics.

[18]  Hani S. Mahmassani,et al.  Driver Car-Following Behavior: From Discrete Event Process to Continuous Set of Episodes , 2008 .

[19]  S.P. Hoogendoorn,et al.  Driving behavior interaction with ACC: results from a Field Operational Test in the Netherlands , 2008, 2008 IEEE Intelligent Vehicles Symposium.

[20]  E. Montroll,et al.  Traffic Dynamics: Studies in Car Following , 1958 .

[21]  Hwasoo Yeo,et al.  Asymmetric Microscopic Driving Behavior Theory , 2008 .

[22]  Bobbie D. Seppelt,et al.  Making adaptive cruise control (ACC) limits visible , 2007, Int. J. Hum. Comput. Stud..

[23]  H. M. Zhang.  A mathematical theory of traffic hysteresis , 1999 .

[24]  Wuhong Wang,et al.  A safety-based approaching behavioural model with various driving characteristics , 2011 .

[25]  Robert Herman,et al.  Traffic Dynamics: Analysis of Stability in Car Following , 1959 .

[26]  William H. Warren,et al.  Chapter 8 – Self-Motion: Visual Perception and Visual Control , 1995 .

[27]  Roberta Di Pace,et al.  Development and testing of a fully Adaptive Cruise Control system , 2013 .

[28]  R. B. Potts,et al.  Car-Following Theory of Steady-State Traffic Flow , 1959 .

[29]  H Ozaki,et al.  REACTION AND ANTICIPATION IN THE CAR-FOLLOWING BEHAVIOR. , 1993 .

[30]  G.N. Bifulco,et al.  Experiments toward an human-like Adaptive Cruise Control , 2008, 2008 IEEE Intelligent Vehicles Symposium.

[31]  Xiao Zhi Gao,et al.  PSO-Optimized Negative Selection Algorithm for Anomaly Detection , 2009 .

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

[33]  Mike McDonald,et al.  Drivers' Use of Deceleration and Acceleration Information in Car-Following Process , 2004 .

[34]  Michael A. Goodrich,et al.  A model of human brake initiation behavior with implications for ACC design , 1999, Proceedings 199 IEEE/IEEJ/JSAI International Conference on Intelligent Transportation Systems (Cat. No.99TH8383).

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

[36]  Mike McDonald,et al.  Determinants of following headway in congested traffic , 2009 .

[37]  John Richardson,et al.  Alarm timing, trust and driver expectation for forward collision warning systems. , 2006, Applied ergonomics.

[38]  Ernest Peter Todosiev,et al.  The action point model of the driver-vehicle system / , 1963 .

[39]  Jianping Wu,et al.  The validation of a microscopic simulation model: a methodological case study , 2003 .

[40]  M M Minderhoud,et al.  Extended time-to-collision measures for road traffic safety assessment. , 2001, Accident; analysis and prevention.

[41]  M. Treiber,et al.  Estimating Acceleration and Lane-Changing Dynamics from Next Generation Simulation Trajectory Data , 2008, 0804.0108.

[42]  Vincenzo Punzo,et al.  Human-Like Adaptive Cruise Control Systems through a Learning Machine Approach , 2009 .

[43]  Gaetano Fusco,et al.  Artificial Neural Network Models for Car Following: Experimental Analysis and Calibration Issues , 2014, J. Intell. Transp. Syst..