Connected cruise control among human-driven vehicles: Experiment-based parameter estimation and optimal control design

In this paper, we consider connected cruise control design in mixed traffic flow where most vehicles are human-driven. We first propose a sweeping least square method to estimate in real time feedback gains and driver reaction time of human-driven vehicles around the connected automated vehicle. Then we propose an optimal connected cruise controller based on the mean dynamics of human driving behavior. We test the performance of both the estimation algorithm and the connected cruise control algorithm using experimental data. We demonstrate that by combining the proposed estimation algorithm and the optimal controller, the connected automated vehicle has significantly improved performance compared to a human-driven vehicle.

[1]  Ilya Kolmanovsky,et al.  Preserving Stability/Performance when Facing an Unknown Time-Delay , 2000 .

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

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

[4]  Ellen van Nunen,et al.  Cooperative Competition for Future Mobility , 2012, IEEE Transactions on Intelligent Transportation Systems.

[5]  Jun Wang,et al.  MOHA: A Multi-Mode Hybrid Automaton Model for Learning Car-Following Behaviors , 2019, IEEE Transactions on Intelligent Transportation Systems.

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

[7]  Chaozhe R. He,et al.  Seeing Beyond the Line of Site – Controlling Connected Automated Vehicles , 2017 .

[8]  Steven E. Shladover,et al.  Effects of Adaptive Cruise Control Systems on Highway Traffic Flow Capacity , 2002 .

[9]  V. Kolmanovskii,et al.  Applied Theory of Functional Differential Equations , 1992 .

[10]  Yury Orlov,et al.  On-line identification of SISO linear time-invariant delay systems from output measurements , 2007, Autom..

[11]  Jianqiang Wang,et al.  Stabilizing Periodic Control of Automated Vehicle Platoon With Minimized Fuel Consumption , 2017, IEEE Transactions on Transportation Electrification.

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

[13]  Gábor Orosz,et al.  Estimation of feedback gains and delays in connected vehicle systems , 2016, 2016 American Control Conference (ACC).

[14]  Jennie Lioris,et al.  Platoons of connected vehicles can double throughput in urban roads , 2015, 1511.00775.

[15]  Dirk Helbing,et al.  Delays, inaccuracies and anticipation in microscopic traffic models , 2006 .

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

[17]  Pamela Irene Labuhn,et al.  Adaptive cruise control , 2007 .

[18]  Sergey V. Drakunov,et al.  Delay identification in time-delay systems using variable structure observers , 2006, Annu. Rev. Control..

[19]  Peter Seiler,et al.  Disturbance propagation in vehicle strings , 2004, IEEE Transactions on Automatic Control.

[20]  Meng Wang,et al.  Infrastructure assisted adaptive driving to stabilise heterogeneous vehicle strings , 2018, Transportation Research Part C: Emerging Technologies.

[21]  Soyoung Ahn,et al.  Receding Horizon Stochastic Optimal Control Strategy for ACC and CACC under Uncertainty , 2017 .

[22]  D. Helbing Traffic and related self-driven many-particle systems , 2000, cond-mat/0012229.

[23]  Vicente Milanés Montero,et al.  Cooperative Maneuvering in Close Environments Among Cybercars and Dual-Mode Cars , 2011, IEEE Transactions on Intelligent Transportation Systems.

[24]  Mélanie Bouroche,et al.  Robust parameter estimation of car-following models considering practical non-identifiability , 2016, 2016 IEEE 19th International Conference on Intelligent Transportation Systems (ITSC).

[25]  G. Orosz,et al.  Analysis of connected vehicle networks using network-based perturbation techniques , 2017 .

[26]  Gábor Orosz,et al.  Stability of Systems with Stochastic Delays and Applications to Genetic Regulatory Networks , 2016, SIAM J. Appl. Dyn. Syst..

[27]  Ziyou Gao,et al.  Coordinated cruise control for high-speed train movements based on a multi-agent model , 2015 .

[28]  Jean-Pierre Richard,et al.  Adaptive identification of linear time‐delay systems , 2003 .

[29]  Chaozhe R. He,et al.  Experimental validation of connected automated vehicle design among human-driven vehicles , 2018, Transportation Research Part C: Emerging Technologies.

[30]  Gábor Orosz,et al.  Scalable stability analysis on large connected vehicle systems subject to stochastic communication delays , 2017 .

[31]  Mario di Bernardo,et al.  Distributed Consensus Strategy for Platooning of Vehicles in the Presence of Time-Varying Heterogeneous Communication Delays , 2015, IEEE Transactions on Intelligent Transportation Systems.

[32]  Gábor Stépán,et al.  Traffic jams: dynamics and control , 2010, Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences.

[33]  Gábor Orosz,et al.  Optimal Control of Connected Vehicle Systems With Communication Delay and Driver Reaction Time , 2017, IEEE Transactions on Intelligent Transportation Systems.

[34]  Gábor Orosz,et al.  To Delay or Not to Delay—Stability of Connected Cruise Control , 2017 .

[35]  Gábor Orosz,et al.  Connected cruise control: modelling, delay effects, and nonlinear behaviour , 2016 .

[36]  Meng Wang,et al.  Eco approaching at an isolated signalized intersection under partially connected and automated vehicles environment , 2017 .

[37]  Bo Cheng,et al.  Fuel-Saving Servo-Loop Control for an Adaptive Cruise Control System of Road Vehicles With Step-Gear Transmission , 2017, IEEE Transactions on Vehicular Technology.

[38]  Elham Semsar-Kazerooni,et al.  The Grand Cooperative Driving Challenge 2016: boosting the introduction of cooperative automated vehicles , 2016, IEEE Wireless Communications.

[39]  Gábor Orosz,et al.  Motif-Based Design for Connected Vehicle Systems in Presence of Heterogeneous Connectivity Structures and Time Delays , 2016, IEEE Transactions on Intelligent Transportation Systems.

[40]  Na Chen,et al.  A Robust Longitudinal Control Strategy of Platoons Under Model Uncertainties and Time Delays , 2018 .

[41]  Gábor Orosz,et al.  Hierarchical Design of Connected Cruise Control in the Presence of Information Delays and Uncertain Vehicle Dynamics , 2018, IEEE Transactions on Control Systems Technology.

[42]  Markos Papageorgiou,et al.  Highway Traffic State Estimation with Mixed Connected and Conventional Vehicles Using Speed Measurements , 2015, ITSC.

[43]  Roncoli Claudio,et al.  Highway traffic state estimation with mixed connected and conventional vehicles: Microscopic simulation-based testing , 2016 .

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

[45]  Gábor Orosz,et al.  Can a finite number of discrete delays approximate stochastic delay? , 2018, Syst. Control. Lett..

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

[47]  Markos Papageorgiou,et al.  A platoon based cooperative eco-driving model for mixed automated and human-driven vehicles at a signalised intersection , 2018, Transportation Research Part C: Emerging Technologies.

[48]  Harald Waschl,et al.  Flexible Spacing Adaptive Cruise Control Using Stochastic Model Predictive Control , 2018, IEEE Transactions on Control Systems Technology.

[49]  Nathan van de Wouw,et al.  Controller Synthesis for String Stability of Vehicle Platoons , 2014, IEEE Transactions on Intelligent Transportation Systems.