Cooperative Car-Following Control: Distributed Algorithm and Impact on Moving Jam Features

We design controllers and derive implementable algorithms for autonomous and cooperative car-following control (CFC) systems under a receding horizon control framework. An autonomous CFC system controls vehicle acceleration to optimize its own situation, whereas a cooperative CFC (C-CFC) system coordinates accelerations of cooperative vehicles to optimize the joint situation. To realize simultaneous control of many vehicles in a traffic system, decentralized and distributed algorithms are implemented in a microscopic traffic simulator for CFC and C-CFC controllers, respectively. The impacts of the proposed controllers on dynamic traffic flow features, particularly on formation and propagation of moving jams, are investigated through a simulation on a two-lane freeway with CFC/C-CFC vehicles randomly distributed. The simulation shows that the proposed decentralized CFC and distributed C-CFC algorithms are implementable in microscopic simulations, and the assessment reveals that CFC and C-CFC systems change moving jam characteristics substantially.

[1]  Martin Treiber,et al.  Traffic Flow Dynamics: Data, Models and Simulation , 2012 .

[2]  John A. Michon,et al.  Generic intelligent drive support , 1993 .

[3]  Romain Billot,et al.  Linear and Weakly Nonlinear Stability Analyses of Cooperative Car-Following Models , 2014, IEEE Transactions on Intelligent Transportation Systems.

[4]  G.A. Klunder,et al.  Traffic flow effects of Integrated full-Range Speed Assistance (IRSA) , 2007, 2007 IEEE Intelligent Vehicles Symposium.

[5]  Rajesh Rajamani,et al.  An Experimental Comparative Study of Autonomous and Co-operative Vehicle-follower Control Systems , 2001 .

[6]  Adnan Shaout,et al.  Cruise control technology review , 1997 .

[7]  William B. Dunbar,et al.  Distributed receding horizon control for multi-vehicle formation stabilization , 2006, Autom..

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

[9]  M. Treiber,et al.  Evidence of convective instability in congested traffic flow: A systematic empirical and theoretical investigation , 2011 .

[10]  M J Lighthill,et al.  On kinematic waves II. A theory of traffic flow on long crowded roads , 1955, Proceedings of the Royal Society of London. Series A. Mathematical and Physical Sciences.

[11]  Meng Wang,et al.  Rolling horizon control framework for driver assistance systems. Part I: Mathematical formulation and non-cooperative systems , 2014 .

[12]  Benjamin Coifman,et al.  Extended bottlenecks, the fundamental relationship, and capacity drop on freeways , 2011 .

[13]  Carlos F. Daganzo,et al.  Lane-changing in traffic streams , 2006 .

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

[15]  Meng Wang,et al.  Modelling Supported Driving as an Optimal Control Cycle: Framework and Model Characteristics☆ , 2013 .

[16]  Meng Wang,et al.  Generic Model Predictive Control Framework for Advanced Driver Assistance Systems , 2014 .

[17]  Riccardo Scattolini,et al.  Architectures for distributed and hierarchical Model Predictive Control - A review , 2009 .

[18]  Rahmi Akcelik,et al.  Efficiency and drag in the power-based model of fuel consumption , 1989 .

[19]  Meng Wang,et al.  Game theoretic approach for predictive lane-changing and car-following control , 2015 .

[20]  Kuo-Yun Liang,et al.  The Development of a Cooperative Heavy-Duty Vehicle for the GCDC 2011: Team Scoop , 2012, IEEE Transactions on Intelligent Transportation Systems.

[21]  R. E. Wilson,et al.  Mechanisms for spatio-temporal pattern formation in highway traffic models , 2008, Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences.

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

[23]  Rajesh Rajamani,et al.  Should adaptive cruise-control systems be designed to maintain a constant time gap between vehicles? , 2001, IEEE Transactions on Vehicular Technology.

[24]  Meng Wang,et al.  Modeling Driver, Driver Support, and Cooperative Systems with Dynamic Optimal Control , 2012 .

[25]  Petros A. Ioannou,et al.  Autonomous intelligent cruise control , 1993 .

[26]  Nathan van de Wouw,et al.  Lp String Stability of Cascaded Systems: Application to Vehicle Platooning , 2014, IEEE Transactions on Control Systems Technology.

[27]  Soyoung Ahn,et al.  Microscopic traffic hysteresis in traffic oscillations : a behavioral perspective , 2012 .

[28]  K. Chu.  Decentralized Control of High-Speed Vehicular Strings , 1974 .

[29]  M. Cassidy,et al.  Some traffic features at freeway bottlenecks , 1999 .

[30]  Dirk Helbing,et al.  Understanding widely scattered traffic flows, the capacity drop, and platoons as effects of variance-driven time gaps. , 2006, Physical review. E, Statistical, nonlinear, and soft matter physics.

[31]  Mark D. Miller,et al.  Modeling Effects of Driver Control Assistance Systems on Traffic , 2001 .

[32]  Swaroop Darbha,et al.  Intelligent Cruise Control Systems And Traffic Flow Stability , 1998 .

[33]  S Kato,et al.  An architecture for cooperative driving of automated vehicles , 2000, ITSC2000. 2000 IEEE Intelligent Transportation Systems. Proceedings (Cat. No.00TH8493).

[34]  R. Muller,et al.  Intelligent cruise control with fuzzy logic , 1992, Proceedings of the Intelligent Vehicles `92 Symposium.

[35]  Victor L. Knoop,et al.  Integrated Lane Change Model with Relaxation and Synchronization , 2012 .

[36]  Gábor Orosz,et al.  Dynamics of connected vehicle systems with delayed acceleration feedback , 2014 .

[37]  P. Varaiya,et al.  Sketch of an IVHS systems architecture , 1991, Vehicle Navigation and Information Systems Conference, 1991.

[38]  Frank Allgöwer,et al.  Cooperative control of dynamically decoupled systems via distributed model predictive control , 2012 .

[39]  Raja Sengupta,et al.  Breaking the Highway Capacity Barrier: Adaptive Cruise Control-Based Concept , 1999 .

[40]  Bart van Arem,et al.  The Impact of Cooperative Adaptive Cruise Control on Traffic-Flow Characteristics , 2006, IEEE Transactions on Intelligent Transportation Systems.

[41]  Bo Cheng,et al.  Fast Online Computation of a Model Predictive Controller and Its Application to Fuel Economy–Oriented Adaptive Cruise Control , 2015, IEEE Transactions on Intelligent Transportation Systems.

[42]  Ludovic Leclercq,et al.  Capacity drops at merges: An endogenous model , 2011 .

[43]  Akihiro Nakayama,et al.  Dynamical model of a cooperative driving system for freeway traffic. , 2003, Physical review. E, Statistical, nonlinear, and soft matter physics.

[44]  Jun-ichi Imura,et al.  Smart Driving of a Vehicle Using Model Predictive Control for Improving Traffic Flow , 2014, IEEE Transactions on Intelligent Transportation Systems.

[45]  Chris M.J. Tampère,et al.  Human-kinetic multiclass traffic flow theory and modelling. With application to Advanced Driver Assistance Systems in congestion , 2004 .

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

[47]  Meng Wang,et al.  Rolling horizon control framework for driver assistance systems. Part II: Cooperative sensing and cooperative control , 2014 .