Estimating empirically the response time of commercially available ACC controllers under urban and freeway conditions

Research on Advanced Driver Assistance Systems (ADAS) and technologies that are expected to be involved in automated driving attracts lately a lot of interest from engineers and modelers. Adaptive Cruise Control (ACC) is one of the first automated functionalities available for privately owned vehicles and the deployment of such systems in public transport networks is constantly increasing. The impact of such controllers is still under investigation and there is a lot of discussion regarding their ability to positively affect congestion and pollution. In simulation studies regarding the impact of ACC on traffic flow, one key parameter is their response time. This parameter, usually takes low values, based on the controller’s theoretical ability to respond instantaneously. In the preliminary results presented by the authors in [1] based on an empirical approach, it seems that this hypothesis is not valid. The present work builds on this conclusion presenting further results on two more commercially available controllers and testing their response in both urban and highway driving conditions under normal driving behavior without critical situations regarding the safety of the vehicle’s passengers. The deployed ACC systems are primarily designed for safety and comfort. Adding on top of that the delays due to the interoperability of various vehicle systems, the final response time, that an observer would see, is very close to the human reaction time and this work shows that in some cases is even higher and by no means instantaneous. The findings here refer to normal driving conditions.

[1]  Ciuffo Biagio,et al.  The r-evolution of driving: from Connected Vehicles to Coordinated Automated Road Transport (C-ART) , 2017 .

[2]  P. Moriarty,et al.  Could automated vehicles reduce transport energy , 2017 .

[3]  Stephen D. Boyles,et al.  Effects of Autonomous Vehicle Behavior on Arterial and Freeway Networks , 2016 .

[4]  Hesham Rakha,et al.  An Enhanced Rakha-Pasumarthy-Adjerid Car-Following Model Accounting for Driver Behavior , 2017 .

[5]  Ye Li,et al.  Evaluating the safety impact of adaptive cruise control in traffic oscillations on freeways. , 2017, Accident; analysis and prevention.

[6]  Biagio Ciuffo,et al.  Capability of Current Car-Following Models to Reproduce Vehicle Free-Flow Acceleration Dynamics , 2018, IEEE Transactions on Intelligent Transportation Systems.

[7]  Biagio Ciuffo,et al.  MFC Free-Flow Model: Introducing Vehicle Dynamics in Microsimulation , 2019, Transportation Research Record: Journal of the Transportation Research Board.

[8]  Daniel Barber,et al.  Connected and Automated Vehicle Simulation to Enhance Vehicle Message Delivery , 2017, AHFE.

[9]  T. P. Cheatham,et al.  The application of correlation functions in the detection of small signals in noise , 1949 .

[10]  Daniele Borio,et al.  Estimating reaction time in Adaptive Cruise Control System , 2018, 2018 IEEE Intelligent Vehicles Symposium (IV).

[11]  Dirk Helbing,et al.  Adaptive cruise control design for active congestion avoidance , 2008 .

[12]  Steven E Shladover,et al.  Modeling cooperative and autonomous adaptive cruise control dynamic responses using experimental data , 2014 .

[13]  Andreas Tapani,et al.  Vehicle Trajectory Effects of Adaptive Cruise Control , 2012, J. Intell. Transp. Syst..

[14]  Marc Green,et al.  "How Long Does It Take to Stop?" Methodological Analysis of Driver Perception-Brake Times , 2000 .

[15]  Martijn van Noort,et al.  Cooperative driving in mixed traffic networks — Optimizing for performance , 2012, 2012 IEEE Intelligent Vehicles Symposium.

[16]  Steven E Shladover,et al.  Impacts of Cooperative Adaptive Cruise Control on Freeway Traffic Flow , 2012 .

[17]  Meng Wang,et al.  Potential impacts of ecological adaptive cruise control systems on traffic and environment , 2014 .

[18]  Todd Litman,et al.  Autonomous Vehicle Implementation Predictions: Implications for Transport Planning , 2015 .

[19]  Xiao-Yun Lu,et al.  COOPERATIVE ADAPTIVE CRUISE CONTROL (CACC) DEFINITIONS AND OPERATING CONCEPTS , 2015 .

[20]  Xiao-Yun Lu,et al.  Micro-Simulation of Truck Platooning with Cooperative Adaptive Cruise Control: Model Development and a Case Study , 2018 .

[21]  Stephen D. Boyles,et al.  A multiclass cell transmission model for shared human and autonomous vehicle roads , 2016 .

[22]  Manukid Parnichkun,et al.  Adaptive cruise control for an intelligent vehicle , 2009, 2008 IEEE International Conference on Robotics and Biomimetics.

[23]  Markos Papageorgiou,et al.  On Microscopic Modelling of Adaptive Cruise Control Systems , 2015 .

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

[25]  Daniel J. Fagnant,et al.  Preparing a Nation for Autonomous Vehicles: Opportunities, Barriers and Policy Recommendations , 2015 .

[26]  Yan Xu,et al.  Modeling reaction time within a traffic simulation model , 2013, 16th International IEEE Conference on Intelligent Transportation Systems (ITSC 2013).

[27]  Khair Jadaan,et al.  Connected Vehicles: An Innovative Transport Technology ☆ , 2017 .

[28]  Vicente Milanés Montero,et al.  Cooperative Adaptive Cruise Control in Real Traffic Situations , 2014, IEEE Transactions on Intelligent Transportation Systems.

[29]  Dirk Helbing,et al.  Jam-Avoiding Adaptive Cruise Control (ACC) and its Impact on Traffic Dynamics , 2005 .