A New Control Strategy Integrated into the Desired Safety Margin Car-Following Model Considering the Disturbance Level

A vehicular communication system can provide motion information based on various sensors. Thus, adaptive cruise control (ACC) systems based on a reliable communication system can relieve traffic congestion in a platoon. However, acquiring vehicle information entails uncertainties because of the disturbance of traffic environment and vehicular communication systems, thereby influencing vehicle control performance. In this study, the desired safety margin (DSM) model is employed to investigate the influence of uncertainty on car-following performance, such as starting, emergency braking, and car-following processes. Based on the DSM model, the disturbance level on perceived safety margin is introduced to characterize the uncertainty of vehicle information. The stability criterion of the DSM model with the disturbance level is derived via linear stability theory. Analytical results indicate that a negative value of disturbance level will enlarge the stable region. By contrast, a positive value is conducive for maintaining the consensus state and achieving the high acceleration and deceleration of following vehicles in the starting process. Findings show that the disturbance in the vehicular system significantly influences car-following performance. To enhance the smoothness and stability of traffic flow evolution, a new control strategy is proposed in this study. Numerical experiments illustrate the effectiveness of the proposed control strategy in stabilizing traffic flow. This study highlights the need to resolve the stability of sensors and vehicular communication systems, and to develop the vehicular controller to help ACC systems improve vehicle control performance in the car-following process.

[1]  Yunpeng Wang,et al.  A new car-following model with consideration of inter-vehicle communication , 2014 .

[2]  G. Stépán,et al.  Subcritical Hopf bifurcations in a car-following model with reaction-time delay , 2006, Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences.

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

[4]  Prakash Ranjitkar,et al.  CAR-FOLLOWING MODELS: AN EXPERIMENT BASED BENCHMARKING , 2005 .

[5]  D Low,et al.  CHAOS IN A CAR-FOLLOWING MODEL WITH A DESIRED HEADWAY TIME , 1997 .

[6]  Ziyou Gao,et al.  A new car-following model: full velocity and acceleration difference model , 2005 .

[7]  Tie-Qiao Tang,et al.  A car-following model accounting for the driver’s attribution , 2014 .

[8]  Daxin Tian,et al.  A rear-end collision avoidance system of connected vehicles , 2014, 17th International IEEE Conference on Intelligent Transportation Systems (ITSC).

[9]  S. Q. Dai,et al.  Analysis of car-following model considering driver’s physical delay in sensing headway , 2008 .

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

[11]  Hai-Jun Huang,et al.  A new car-following model with consideration of roadside memorial , 2011 .

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

[13]  R. E. Wilson,et al.  Bifurcations and multiple traffic jams in a car-following model with reaction-time delay , 2005 .

[14]  Tang Tie-Qiao,et al.  A New Car-Following Model with Consideration of Driving Resistance , 2011 .

[15]  Guangquan Lu,et al.  Quantitative indicator of homeostatic risk perception in car following , 2012 .

[16]  H. X. Ge,et al.  Effect of looking backward on traffic flow in a cooperative driving car following model , 2006 .

[17]  Yunpeng Wang,et al.  An extended car-following model with consideration of the reliability of inter-vehicle communication , 2014 .

[18]  Yanfei Jin,et al.  Stability analysis in a car-following model with reaction-time delay and delayed feedback control , 2016 .

[19]  K Konishi,et al.  Coupled map car-following model and its delayed-feedback control. , 1999, Physical review. E, Statistical physics, plasmas, fluids, and related interdisciplinary topics.

[20]  Tie-Qiao Tang,et al.  An extended car-following model with consideration of the electric vehicle’s driving range , 2015 .

[21]  Edgar Sokolovskij.  Experimental investigation of the braking process of automobiles , 2005 .

[22]  Tian Chuan.  Car-following model based on the information of multiple ahead & velocity difference , 2010 .

[23]  Nakayama,et al.  Dynamical model of traffic congestion and numerical simulation. , 1995, Physical review. E, Statistical physics, plasmas, fluids, and related interdisciplinary topics.

[24]  Bin Yang,et al.  Extended-State-Observer-Based Double-Loop Integral Sliding-Mode Control of Electronic Throttle Valve , 2015, IEEE Transactions on Intelligent Transportation Systems.

[25]  Min Zhang,et al.  Modeling and simulation for microscopic traffic flow based on multiple headway, velocity and acceleration difference , 2011 .

[26]  Tie-Qiao Tang,et al.  A new car-following model accounting for varying road condition , 2012 .

[27]  Leonid M. Fridman,et al.  Cascade control of PM-DC drives via second-order sliding mode technique , 2008, 2008 International Workshop on Variable Structure Systems.

[28]  Yunpeng Wang,et al.  A Car-Following Model Based on Quantified Homeostatic Risk Perception , 2013 .

[29]  Wei-Zhen Lu,et al.  Nonlinear analysis of a new car-following model accounting for the optimal velocity changes with memory , 2016, Commun. Nonlinear Sci. Numer. Simul..

[30]  Martin Treiber,et al.  Traffic Flow Dynamics , 2013 .

[31]  Hao-Chi Chang,et al.  Sliding mode control on electro-mechanical systems , 1999 .

[32]  Helbing,et al.  Congested traffic states in empirical observations and microscopic simulations , 2000, Physical review. E, Statistical physics, plasmas, fluids, and related interdisciplinary topics.

[33]  Rou-Yong Duan,et al.  Cascade Modeling and Intelligent Control Design for an Electromagnetic Guiding System , 2011, IEEE/ASME Transactions on Mechatronics.

[34]  Ziyou Gao,et al.  A control method for congested traffic induced by bottlenecks in the coupled map car-following model , 2006 .

[35]  A. Schadschneider,et al.  Statistical physics of vehicular traffic and some related systems , 2000, cond-mat/0007053.

[36]  Antonio Virga,et al.  Evaluation of emergency braking deceleration for accident reconstruction , 2007 .

[37]  Zhongke Shi,et al.  An improved car-following model considering headway changes with memory , 2015 .

[38]  Dong Chen,et al.  A new control method integrated into the coupled map car-following model for suppressing traffic jams , 2017 .

[39]  G. Zi-you,et al.  Multiple velocity difference model and its stability analysis , 2006 .

[40]  He Chen,et al.  A New Antiswing Control Method for Underactuated Cranes With Unmodeled Uncertainties: Theoretical Design and Hardware Experiments , 2015, IEEE Transactions on Industrial Electronics.

[41]  Wen-xing Zhu,et al.  A speed feedback control strategy for car-following model , 2014 .

[42]  R. Jiang,et al.  Full velocity difference model for a car-following theory. , 2001, Physical review. E, Statistical, nonlinear, and soft matter physics.

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

[44]  Zuduo Zheng,et al.  Incorporating human-factors in car-following models : a review of recent developments and research needs , 2014 .

[45]  Hai-Jun Huang,et al.  A new car-following model with the consideration of the driver's forecast effect , 2010 .

[46]  Dirk Helbing,et al.  GENERALIZED FORCE MODEL OF TRAFFIC DYNAMICS , 1998 .

[47]  Srinivas Peeta,et al.  Non-lane-discipline-based car-following model considering the effects of two-sided lateral gaps , 2018, 2018 Chinese Automation Congress (CAC).

[48]  Licai Yang,et al.  Clustered car-following strategy for improving car-following stability under Cooperative Vehicles Infrastructure Systems , 2016 .