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Cooperative Mandatory Lane Change for Connected Vehicles on Signalized Intersection Roads
ISSN: 0148-7191, e-ISSN: 2688-3627
Published April 14, 2020 by SAE International in United States
This content contains downloadable datasetsAnnotation ability available
This paper presents a hierarchical control architecture to coordinate a group of connected vehicles on signalized intersection roads, where vehicles are allowed to change lane to follow a prescribed path. The proposed hierarchical control strategy consists of two control levels: a high level controller at the intersection and a decentralized low level controller in each car. In the hierarchical control architecture, the centralized intersection controller estimates the target velocity for each approaching connected vehicle to avoid red light stop based on the signal phase and timing (SPAT) information. Each connected vehicle as a decentralized controller utilizes model predictive control (MPC) to track the target velocity in a fuel efficient manner. The main objective in this paper is to consider mandatory lane changes. As in the realistic scenarios, vehicles are not required to drive in single lane. More specifically, they more likely change their lanes prior to signals. Hence, the vehicle decentralized controllers must prepare to cooperate with the vehicle that has a mandatory lane change request (host vehicle). The cooperative mandatory lane change is accomplished by inserting a virtual vehicle on the host vehicle’s target lane. The simulation results show the advantage of our proposed approach on both the lane change duration and vehicle fuel economy.
CitationDu, Z., Xu, B., and Pisu, P., "Cooperative Mandatory Lane Change for Connected Vehicles on Signalized Intersection Roads," SAE Technical Paper 2020-01-0889, 2020, https://doi.org/10.4271/2020-01-0889.
Data Sets - Support Documents
|[Unnamed Dataset 1]|
- Schrank, D., Eisele, B., and Lomax, T. , “TTI’s 2012 Urban Mobility Report,” Texas A&M Transportation Institute, The Texas A&M University System, 2012.
- Xin, W. , “A New Architecture of Adaptive Traffic Signal Control in a Data-Rich Environment,” Polytechnic Institute of New York University, 2014.
- Malmir, F., Xu, B., and Filipi, Z. , “A Heuristic Supervisory Controller for a 48V Hybrid Electric Vehicle Considering Fuel Economy and Battery Aging,” SAE Technical Paper 2019-01-0079, 2019. https://doi.org/10.4271/2019-01-0079.
- Neuman, T.R. , “Guidance for Implementation of the AASHTO Strategic Highway Safety Plan: A Guide for Addressing Unsignalized Intersection Collisions,” Vol. 4: Highway Research Board of the Division of Engineering and Industrial Research, National Academy of Sciences, National Research Council, 1965.
- Kamal, M., Samad, A., Mukai, M., Murata, J., and Kawabe, T. , “Model Predictive Control of Vehicles on Urban Roads for Improved Fuel Economy,” Control Systems Technology, IEEE Transactions on 21:831-841, 2013.
- Mandava, S., Boriboonsomsin, K., and Barth, M. , “Arterial Velocity Planning Based on Traffic Signal Information under Light Traffic Conditions,” in Intelligent Transportation Systems, 2009. ITSC’09, 12th International IEEE Conference on, 2009, 1-6.
- Asadi, B. and Vahidi, A. , “Predictive Cruise Control: Utilizing Upcoming Traffic Signal Information for Improving Fuel Economy and Reducing Trip Time,” Control Systems Technology, IEEE Transactions on 19:707-714, 2011.
- He, X., Liu, H.X., and Liu, X. , “Optimal Vehicle Speed Trajectory on a Signalized Arterial with Consideration of Queue,” Transportation Research Part C: Emerging Technologies 61:106-120, 2015.
- Gonder, J., Earleywine, M., and Sparks, W. , “Final Report on the Fuel Saving Effectiveness of Various Driver Feedback Approaches,” National Renewable Energy Laboratory (NREL), Golden, CO, 2011.
- Choudhury, C.F., Ben-Akiva, M.E., Toledo, T., Lee, G., and Rao, A. , “Modeling Cooperative Lane Changing and Forced Merging Behavior,” in 86th Annual Meeting of the Transportation Research Board, Washington, DC, 2007.
- Gipps, P.G. , “A Model for the Structure of Lane-Changing Decisions,” Transportation Research Part B: Methodological 20:403-414, 1986.
- Hidas, P. , “Modelling Lane Changing and Merging in Microscopic Traffic Simulation,” Transportation Research Part C: Emerging Technologies 10:351-371, 2002.
- Hidas, P. , “Modelling Vehicle Interactions in Microscopic Simulation of Merging and Weaving,” Transportation Research Part C: Emerging Technologies 13:37-62, 2005.
- Kesting, A., Treiber, M., and Helbing, D. , “General Lane-Changing Model Mobil for Car-Following Models,” Transportation Research Record: Journal of the Transportation Research Board, 2007.
- Kamal M., Taguchi, S., and Yoshimura, T. , “Efficient Vehicle Driving on Multi-Lane Roads Using Model Predictive Control Under a Connected Vehicle Environment,” in Intelligent Vehicles Symposium (IV), 2015 IEEE, 2015, 736-741.
- Wang, M., Hoogendoorn, S.P., Daamen, W., van Arem, B., and Happee, R. , “Game Theoretic Approach for Predictive Lane-Changing and Car-Following Control,” Transportation Research Part C: Emerging Technologies, 58, 73-92, 2015.
- Du, Z., HomChaudhuri, B., and Pisu, P. , “Coordination Strategy for Vehicles Passing Multiple Signalized Intersections: A Connected Vehicle Penetration Rate Study,” in 2017 American Control Conference (ACC), 2017, 4952-4957.
- Du, Z., Qiu, L., and Pisu, P. , “Hierarchical Energy Management Control of Connected Hybrid Electric Vehicles on Urban Roads with Efficiencies Feedback,” in ASME 2016 Dynamic Systems and Control Conference, 2016, V001T16A002-V001T16A002.
- Du, Z., HomChaudhuri, B., and Pisu, P. , “Hierarchical Distributed Coordination Strategy of Connected and Automated Vehicles at Multiple Intersections,” Journal of Intelligent Transportation Systems 22:144-158, 2018.
- Du, Z., Chaudhuri, B.H., and Pisu, P. , “Distributed Coordination of Connected and Automated Vehicles at Multiple Interconnected Intersections,” World Academy of Science, Engineering and Technology, International Journal of Computer, Electrical, Automation, Control and Information Engineering 10:1015-1021, 2016.
- Du, Z. and Pisu, P. , “A Fuel Efficient Control Strategy for Connected Vehicles in Multiple-Lane Urban Roads,” in Decision and Control (CDC), 2016 IEEE 55th Conference on, 2016, 715-720.
- Rosolia, U., Braghin, F., Alleyne, A.G., De Bruyne, S., and Sabbioni, E. , “A Decentralized Algorithm for Control of Autonomous Agents Coupled by Feasibility Constraints,” in 2017 American Control Conference (ACC), 2017, 3367-3372.
- Yasuda, G.I. , “Implementation of Real-Time Distributed Control for Discrete Event Robotic Systems Using Petri Nets,” Artificial Life and Robotics 16:537-541, 2012.
- Borges de Sousa, J., Johansson, K.H., Silva, J., and Speranzon, A. , “A Verified Hierarchical Control Architecture for Co-Ordinated Multi-Vehicle Operations,” International Journal of Adaptive Control and Signal Processing, 21, 159-188, 2007.
- HomChaudhuri, B., Lin, R., and Pisu, P. , “Hierarchical Control Strategies for Energy Management of Connected Hybrid Electric Vehicles in Urban Roads,” Transportation Research Part C: Emerging Technologies 62:70-86, 2016.
- HomChaudhuri, B., Vahidi, A., and Pisu, P. , “A Fuel Economic Model Predictive Control Strategy for a Group of Connected Vehicles in Urban Roads,” in American Control Conference (ACC), 2015, 2015, 2741-2746.