MPC-Based Connected Cruise Control with Multiple Human Predecessors

Model predictive control is applied to regulate the longitudinal motion of a connected automated vehicle in mixed traffic scenarios. A prediction method is proposed to enable model predictive control in low-automation, medium-connectivity situations using instantaneous motion information from multiple predecessor vehicles. This includes detection of unconnected vehicles that may be mixed between connected ones. Simulations using real human driver data for the predecessors show that, if the drivers are well-characterized on average, a hidden unconnected vehicle can be detected over 90 % of the time. Moreover, the resulting preview can recover 46 % of the gap in energy performance between single-predecessor prediction and ideal preview. Results are also compared to a classical controller that utilizes instantaneous information from multiple predecessors.

[1]  Antonio Sciarretta,et al.  Energy-Efficient Driving of Road Vehicles , 2019 .

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

[3]  Ardalan Vahidi,et al.  Microsimulation of Energy and Flow Effects from Optimal Automated Driving in Mixed Traffic , 2019, ArXiv.

[4]  Ardalan Vahidi,et al.  Automated Vehicles in Hazardous Merging Traffic: A Chance-Constrained Approach , 2019, IFAC-PapersOnLine.

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

[6]  Tamás G. Molnár,et al.  Feed-forward Neural Networks with Trainable Delay , 2020, L4DC.

[7]  Asad J. Khattak,et al.  Safety, Energy, and Emissions Impacts of Adaptive Cruise Control and Cooperative Adaptive Cruise Control , 2020, Transportation Research Record: Journal of the Transportation Research Board.

[8]  Mitra Pourabdollah,et al.  Calibration and evaluation of car following models using real-world driving data , 2017, 2017 IEEE 20th International Conference on Intelligent Transportation Systems (ITSC).

[9]  Ardalan Vahidi,et al.  Efficient and Collision-Free Anticipative Cruise Control in Randomly Mixed Strings , 2018, IEEE Transactions on Intelligent Vehicles.

[10]  Yurii Nesterov,et al.  Interior-point polynomial algorithms in convex programming , 1994, Siam studies in applied mathematics.

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

[12]  Ali Ghaffari,et al.  A Modified Car-Following Model Based on a Neural Network Model of the Human Driver Effects , 2012, IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans.

[13]  Maria Laura Delle Monache,et al.  Dissipation of stop-and-go waves via control of autonomous vehicles: Field experiments , 2017, ArXiv.

[14]  Maria Laura Delle Monache,et al.  Are Commercially Implemented Adaptive Cruise Control Systems String Stable? , 2019, IEEE Transactions on Intelligent Transportation Systems.

[15]  Paulo Tabuada,et al.  Control barrier function based quadratic programs with application to adaptive cruise control , 2014, 53rd IEEE Conference on Decision and Control.

[16]  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.

[17]  Chaozhe R. He,et al.  Fuel Efficient Connected Cruise Control for Heavy-Duty Trucks in Real Traffic , 2020, IEEE Transactions on Control Systems Technology.

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

[19]  Jan M. Maciejowski,et al.  Predictive control : with constraints , 2002 .

[20]  Jun-ichi Imura,et al.  Road-Speed Profile for Enhanced Perception of Traffic Conditions in a Partially Connected Vehicle Environment , 2018, IEEE Transactions on Vehicular Technology.

[21]  Yunyi Jia,et al.  Energy and Flow Effects of Optimal Automated Driving in Mixed Traffic: Vehicle-in-the-Loop Experimental Results , 2020, ArXiv.

[22]  Soyoung Ahn,et al.  Distributed model predictive control approach for cooperative car-following with guaranteed local and string stability , 2019, Transportation Research Part B: Methodological.

[23]  Sébastien Le Digabel,et al.  Algorithm xxx : NOMAD : Nonlinear Optimization with the MADS algorithm , 2010 .