Modeling of Driver Cut-in Behavior Towards a Platoon

A vehicle platoon is a group of vehicles driving together with a harmonized speed and a short inter-vehicle gap by using vehicle automation and vehicle-to-vehicle communication. Platoons have to share road with human-driven vehicles (HDVs) and can only be applied in heterogeneous traffic flow for a long period. Driver cut-in behavior (DCB) towards a platoon can be frequently expected in such driving context. In this paper, to understand and simulate such behavior, we propose a platoon-oriented cut-in behavior (POCB) model by fusing a lateral and a longitudinal control model into the queuing network (QN) cognitive architecture. Platoon-oriented cut-in experiments are conducted to collect driver data under cut-in from back and front scenarios, which both include six sub-scenarios with different platoon gaps or initial velocities. We demonstrate the effectiveness of the proposed model in simulating the DCB towards platoons by comparing experimental and simulation results under various driving scenarios across different subjects.

[1]  R. Su,et al.  A Distributed Platoon Control Framework for Connected Automated Vehicles in an Urban Traffic Network , 2022, IEEE Transactions on Control of Network Systems.

[2]  Bohui Wang,et al.  Direction Convolutional LSTM Network: Prediction Network for Drivers’ Lane-Changing Behaviours , 2022, 2022 IEEE 17th International Conference on Control & Automation (ICCA).

[3]  Nengchao Lyu,et al.  Vehicle Trajectory Prediction and Cut-In Collision Warning Model in a Connected Vehicle Environment , 2022, IEEE Transactions on Intelligent Transportation Systems.

[4]  Cristofer Englund,et al.  A Simulation Study on Effects of Platooning Gaps on Drivers of Conventional Vehicles in Highway Merging Situations , 2020, IEEE Transactions on Intelligent Transportation Systems.

[5]  Xiaopeng Li,et al.  Lane-change-aware connected automated vehicle trajectory optimization at a signalized intersection with multi-lane roads , 2021 .

[6]  Mike Lukuc,et al.  Fusing Radar and Vision Data for Cut-In Vehicle Identification in Platooning Applications , 2020 .

[7]  F. Karray,et al.  Distributed Nonlinear Model Predictive Control and Metric Learning for Heterogeneous Vehicle Platooning with Cut-in/Cut-out Maneuvers , 2020, 2020 59th IEEE Conference on Decision and Control (CDC).

[8]  Junqiang Xi,et al.  A Learning-Based Personalized Driver Model Using Bounded Generalized Gaussian Mixture Models , 2019, IEEE Transactions on Vehicular Technology.

[9]  Bin Jia,et al.  A data-driven lane-changing model based on deep learning , 2019, Transportation Research Part C: Emerging Technologies.

[10]  Yunfeng Ai,et al.  Driver Lane Change Intention Inference for Intelligent Vehicles: Framework, Survey, and Challenges , 2019, IEEE Transactions on Vehicular Technology.

[11]  Xuesong Wang,et al.  Analysis of cut-in behavior based on naturalistic driving data. , 2019, Accident; analysis and prevention.

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

[13]  Giovanni Fiengo,et al.  Cooperative Shock Waves Mitigation in Mixed Traffic Flow Environment , 2019, IEEE Transactions on Intelligent Transportation Systems.

[14]  Soyoung Ahn,et al.  Control of Connected and Autonomous Vehicles with Cut-in Movement using Spring Mass Damper System , 2018, Transportation Research Record: Journal of the Transportation Research Board.

[15]  Fei-Yue Wang,et al.  Capturing Car-Following Behaviors by Deep Learning , 2018, IEEE Transactions on Intelligent Transportation Systems.

[16]  Hossein Nourkhiz Mahjoub,et al.  A Learning-Based Stochastic MPC Design for Cooperative Adaptive Cruise Control to Handle Interfering Vehicles , 2018, IEEE Transactions on Intelligent Vehicles.

[17]  Partha S. Roop,et al.  A Formal Approach for Modeling and Simulation of Human Car-Following Behavior , 2018, IEEE Transactions on Intelligent Transportation Systems.

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

[19]  Karl Henrik Johansson,et al.  Heavy-Duty Vehicle Platoon Formation for Fuel Efficiency , 2016, IEEE Transactions on Intelligent Transportation Systems.

[20]  Vicente Milanés Montero,et al.  Handling Cut-In Vehicles in Strings of Cooperative Adaptive Cruise Control Vehicles , 2016, J. Intell. Transp. Syst..

[21]  Dongpu Cao,et al.  Switching-Based Stochastic Model Predictive Control Approach for Modeling Driver Steering Skill , 2015, IEEE Transactions on Intelligent Transportation Systems.

[22]  Le Yi Wang,et al.  Communication Information Structures and Contents for Enhanced Safety of Highway Vehicle Platoons , 2014, IEEE Transactions on Vehicular Technology.

[23]  Changxu Wu,et al.  Mathematical Modeling of Driver Speed Control With Individual Differences , 2013, IEEE Transactions on Systems, Man, and Cybernetics: Systems.

[24]  Yili Liu,et al.  Queuing Network Modeling of Driver Lateral Control With or Without a Cognitive Distraction Task , 2012, IEEE Transactions on Intelligent Transportation Systems.

[25]  Li Liu,et al.  Dynamic Modeling of Driver Control Strategy of Lane-Change Behavior and Trajectory Planning for Collision Prediction , 2012, IEEE Transactions on Intelligent Transportation Systems.

[26]  Han-Shue Tan,et al.  Experimental Development of a New Target and Control Driver Steering Model Based on DLC Test Data , 2012, IEEE Transactions on Intelligent Transportation Systems.

[27]  David J. Cole,et al.  Application of time-variant predictive control to modelling driver steering skill , 2011 .

[28]  Yili Liu,et al.  Queuing Network Modeling of a Real-Time Psychophysiological Index of Mental Workload—P300 in Event-Related Potential (ERP) , 2008, IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans.

[29]  Yili Liu,et al.  Queueing Network-Model Human Processor (QN-MHP): A computational architecture for multitask performance in human-machine systems , 2006, TCHI.

[30]  Rajesh Rajamani,et al.  Vehicle dynamics and control , 2005 .

[31]  Huei Peng,et al.  An adaptive lateral preview driver model , 2005 .

[32]  Rajesh Rajamani,et al.  Model predictive control of transitional maneuvers for adaptive cruise control vehicles , 2004, IEEE Transactions on Vehicular Technology.

[33]  J. F. Soechting,et al.  Use of tactile afferent information in sequential finger movements , 2004, Experimental Brain Research.

[34]  Charles C. MacAdam,et al.  Understanding and Modeling the Human Driver , 2003 .

[35]  Randolph W. Hall,et al.  Vehicle Sorting for Platoon Formation: Impacts on Highway Entry and Throughput , 2005 .

[36]  P. Gilbert,et al.  An outline of brain function. , 2001, Brain research. Cognitive brain research.

[37]  R. Henson,et al.  Frontal lobes and human memory: insights from functional neuroimaging. , 2001, Brain : a journal of neurology.

[38]  Hans Fritz,et al.  Fuel Consumption Reduction in a Platoon: Experimental Results with two Electronically Coupled Trucks at Close Spacing , 2000 .

[39]  Mike McDonald,et al.  Car-following: a historical review , 1999 .

[40]  K. Ahmed Modeling drivers' acceleration and lane changing behavior , 1999 .

[41]  J. Jonides,et al.  Neuroimaging analyses of human working memory. , 1998, Proceedings of the National Academy of Sciences of the United States of America.

[42]  Leslie G. Ungerleider,et al.  What fMRI has taught us about human vision , 1997, Current Opinion in Neurobiology.

[43]  H. Mayberg Brain Activation , 1994, Neurology.