Drivers' Adaptation to Adaptive Cruise Control: Examination of Automatic and Manual Braking

Drivers may adapt to the automatic braking control feature available on adaptive cruise control (ACC) in ways unintended by designers. This study examines drivers' adaptation using a conceptual model of adaptive behavior developed and examined quantitatively using logistic regression techniques. Data for this model come from a field operational test on the use of an advanced collision avoidance system, which integrated forward collision warning and ACC functions. A sample of “closing” events was extracted from a subset of these ACC data. The logistic regression model predicted the drivers' likelihood to intervene (i.e., manually brake) whenever ACC began braking or slowing down the vehicle. The results indicate that several factors influence drivers' response, including the environment, selected gap setting, speed, and drivers' age. Safety consequences and the design of future ACC systems based on drivers' adaptation to these factors are discussed.

[1]  Richard Bishop,et al.  Intelligent Vehicle Technology and Trends , 2005 .

[2]  Nick R. Parsons,et al.  Repeated measures proportional odds logistic regression analysis of ordinal score data in the statistical software package R , 2009, Comput. Stat. Data Anal..

[3]  Boris S. Kerner,et al.  Control of Spatiotemporal Congested Traffic Patterns at Highway Bottlenecks , 2005, IEEE Transactions on Intelligent Transportation Systems.

[4]  Petros A. Ioannou,et al.  Evaluation of ACC vehicles in mixed traffic: lane change effects and sensitivity analysis , 2005, IEEE Transactions on Intelligent Transportation Systems.

[5]  Azim Eskandarian,et al.  Research advances in intelligent collision avoidance and adaptive cruise control , 2003, IEEE Trans. Intell. Transp. Syst..

[6]  A. Horst A time-based analysis of road user behaviour in normal and critical encounters , 1990 .

[7]  R. Fuller Towards a general theory of driver behaviour. , 2005, Accident; analysis and prevention.

[8]  G Molenberghs,et al.  An application of maximum likelihood and generalized estimating equations to the analysis of ordinal data from a longitudinal study with cases missing at random. , 1994, Biometrics.

[9]  L Evans,et al.  Antilock brakes and risk of front and rear impact in two-vehicle crashes. , 1996, Accident; analysis and prevention.

[10]  Miguel Ángel Sotelo,et al.  Using Fuzzy Logic in Automated Vehicle Control , 2006 .

[11]  Paul J.Th. Venhovens,et al.  Stop and Go Cruise Control , 2000 .

[12]  Takahiro Wada,et al.  Characterization of Expert Drivers' Last-Second Braking and Its Application to a Collision Avoidance System , 2010, IEEE Transactions on Intelligent Transportation Systems.

[13]  Christina M. Rudin-Brown,et al.  BEHAVIOURAL ADAPTATION TO ADAPTIVE CRUISE CONTROL (ACC): IMPLICATIONS FOR PREVENTIVE STRATEGIES , 2004 .

[14]  Linda Ng Boyle,et al.  Impact of traveler advisory systems on driving speed: some new evidence , 2004 .

[15]  A Touran,et al.  A collision model for safety evaluation of autonomous intelligent cruise control. , 1999, Accident; analysis and prevention.

[16]  M. M. Minderhoud IMPACT OF INTELLIGENT CRUISE CONTROL STRATEGIES AND EQUIPMENT ROUTE ON ROAD CAPACITY , 1998 .

[17]  David B. Kaber,et al.  Situation awareness and workload in driving while using adaptive cruise control and a cell phone , 2005 .

[18]  L. C. Davis Effect of adaptive cruise control systems on mixed traffic flow near an on-ramp , 2005 .

[19]  MengChu Zhou,et al.  Petri Net Modeling of the Cooperation Behavior of a Driver and a Copilot in an Advanced Driving Assistance System , 2011, IEEE Transactions on Intelligent Transportation Systems.

[20]  Feng Gao,et al.  Practical String Stability of Platoon of Adaptive Cruise Control Vehicles , 2011, IEEE Transactions on Intelligent Transportation Systems.

[21]  J Hedlund,et al.  Risky business: safety regulations, risk compensation, and individual behavior , 2000, Injury prevention : journal of the International Society for Child and Adolescent Injury Prevention.

[22]  Jasper J. A. Pauwelussen,et al.  Driver Behavior Analysis During ACC Activation and Deactivation in a Real Traffic Environment , 2010, IEEE Transactions on Intelligent Transportation Systems.

[23]  Neville A. Stanton,et al.  From fly-by-wire to drive-by-wire: Safety implications of automation in vehicles , 1996 .

[24]  T Chira-Chavala,et al.  Potential safety benefits of intelligent cruise control systems. , 1994, Accident; analysis and prevention.

[25]  R. D. Ervin Automotive collision avoidance system field operational test methodology and results, volume 2: appendices , 2005 .

[26]  J A Michon,et al.  Explanatory pitfalls and rule-based driver models. , 1989, Accident; analysis and prevention.

[27]  Neville A Stanton,et al.  Taking the load off: investigations of how adaptive cruise control affects mental workload , 2004, Ergonomics.

[28]  S. Lipsitz,et al.  Analysis of repeated categorical data using generalized estimating equations. , 1994, Statistics in medicine.

[29]  Mark S. Young,et al.  Drive-by-wire: The case of driver workload and reclaiming control with adaptive cruise control , 1997 .

[30]  Søren Højsgaard,et al.  The R Package geepack for Generalized Estimating Equations , 2005 .

[31]  Wassim G. Najm,et al.  Evaluation of an Automotive Rear-End Collision Avoidance System , 2006 .

[32]  Dirk Helbing,et al.  Extending Adaptive Cruise Control to Adaptive Driving Strategies , 2007 .

[33]  Wei Zhang,et al.  Car-following Safety Algorithms Based on Adaptive Cruise Control Strategies , 2007, 2007 5th International Symposium on Intelligent Systems and Informatics.

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

[35]  Mike McDonald,et al.  Towards an understanding of adaptive cruise control , 2001 .