[1] The future economic and environmental costs of gridlock in 2030, Report for INRIX, 2014.
[2] C B Rafter, B Anvari, S Box. Traffic responsive intersection control algorithm using GPS data. 2017 IEEE 20th International Conference on Intelligent Transportation Systems (ITSC), IEEE, 2017:1-6.
[3] E Onieva, U Hernández-Jayo, E Osaba, et al. A multi-objective evolutionary algorithm for the tuning of fuzzy rule bases for uncoordinated intersections in autonomous driving. Information Sciences, 2015, 321:14-30.
[4] Y Rahmati, A Talebpour. Towards a collaborative connected, automated driving environment:A game theory based decision framework for unprotected left turn maneuvers. 2017 IEEE Intelligent Vehicles Symposium (IV), IEEE, 2017:1316-1321.
[5] B Qian, H Zhou, F Lyu, et al. Toward collision-free and efficient coordination for automated vehicles at unsignalized intersection. IEEE Internet of Things Journal, 2019, 6(6):10408-10420.
[6] B Xu, S E Li, Y Bian, et al. Distributed conflict-free cooperation for multiple connected vehicles at unsignalized intersections. Transportation Research Part C:Emerging Technologies, 2018, 93:322-334.
[7] S A Fayazi, A Vahidi. Mixed-integer linear programming for optimal scheduling of autonomous vehicle intersection crossing. IEEE Transactions on Intelligent Vehicles, 2018, l3(3):287-299.
[8] M K Shi, H Jiang, S H Li. An intelligent traffic- flow-based real-time vehicles scheduling algorithm at intersection. 2016 14th International Conference on Control, Automation, Robotics and Vision (ICARCV), IEEE, 2016:1-5.
[9] J Zheng, H X Liu. Estimating traffic volumes for signalized intersections using connected vehicle data. Transportation Research Part C:Emerging Technologies, 2017, 79:347-362.
[10] S Sunkari. The benefits of retiming traffic signals. Institute of Transportation Engineers. ITE Journal, 2004, 74(4):26.
[11] N. U.S. Dept. Transp. Fatality analysis reporting system (FARS):Analytical user's manual 1975-2015, USA, Tech. Rep. DOT HS, 2016:Tech. Rep. DOT HS 812 315.
[12] N. U.S. Dept. Transp. National automotive sampling system (NASS) general estimates system (GES):Analytical user's manual 1988-2015, USA, Tech. Rep. DOT HS, 2016:Tech. Rep. DOT HS 812 320.
[13] J M Scanlon, R Sherony, H C Gabler. Models of driver acceleration behavior prior to real-world intersection crashes. IEEE Transactions on Intelligent Transportation Systems, 2017, 19(3):774-786.
[14] K Dresner, P Stone. A multiagent approach to autonomous intersection management. Journal of Artificial Intelligence Research, 2008, 31:591-656.
[15] C Sun, Z Deng, W Chu, et al. Acclimatizing the operational design domain for autonomous driving systems. IEEE Intelligent Transportation Systems Magazine, 2021.
[16] Y Guo, J Ma, C Xiong, et al. Joint optimization of vehicle trajectories and inter- section controllers with connected automated vehicles:Combined dynamic programming and shooting heuristic approach. Transportation Research Part C:Emerging Technologies, 2019, 98:54-72.
[17] J Zhao, V L Knoop, M Wang. Two-dimensional vehicular movement modelling at intersections based on optimal control. Transportation Research Part B:Methodological, 2020, 138:1-22.
[18] R Tachet, P Santi, S Sobolevsky, et al. Revisiting Street intersections using slot-based systems. PloS One, 2016, 11(3):e0149607.
[19] M Kneissl, A K Madhusudhanan, A Molin, et al. A multi-vehicle control framework with application to automated valet parking. IEEE Transactions on Intelligent Transportation Systems, 2020.
[20] M Kloock, P Scheffe, S Marquardt, et al. Distributed model predictive intersection control of multiple vehicles. 2019 IEEE intelligent Transportation Systems Conference (ITSC), IEEE, 2019:1735-1740.
[21] G Li, Y Yang, T Zhang, X Qu, et al. Risk assessment based collision avoidance decision-making for autonomous vehicles in multi- scenarios. Transportation Research Part C:Emerging Technologies, 2021, 122:102820.
[22] W Zhao, R Liu, D Ngoduy. A bilevel programming model for autonomous intersection control and trajectory planning. Transportmetrica A:Transport Science, 2021, 17(1):34-58.
[23] Y Feng, D He, Y Guan. Composite platoon trajectory planning strategy for intersection throughput maximization. IEEE Transactions on Vehicular Technology, 2019, 68(7):6305-6319.
[24] K Yang, X Tang, Y Qin, et al. Comparative study of trajectory tracking control for automated vehicles via model predictive control and robust h-infinity state feedback control. Chinese Journal of Mechanical Engineering, 2021, 34:74,. https://doi.org/10.1186/s10033-021-00590-3
[25] B Liu, Q Shi, Z Song, et al. Trajectory planning for autonomous intersection management of connected vehicles. Simulation Modelling Practice and Theory, 2019, 90:16-30.
[26] Y Liu, U Ozguner. Human driver model and driver decision making for intersection driving. 2007 IEEE Intelligent Vehicles Symposium, IEEE, 2007:642- 647.
[27] X Lin, J Zhang, J Shang, et al. Decision making through occluded intersections for autonomous driving. 2019 IEEE Intelligent Transportation Systems Conference (ITSC), IEEE, 2019:2449-2455.
[28] B Xu, X J Ban, Y Bian, et al. Cooperative method of traffic signal optimization and speed control of connected vehicles at isolated intersections. IEEE Transactions on Intelligent Transportation Systems, 2018, 20(4):1390-1403.
[29] W Zhao, D Ngoduy, S Shepherd, et al. A platoon based cooperative eco-driving model for mixed automated and human-driven vehicles at a signalised intersection. Transportation Research Part C:Emerging Technologies, 2018, 95:802-821.
[30] J Ma, X Li, F Zhou, et al. Parsimonious shooting heuristic for trajectory design of connected automated traffic part ii:Computational issues and optimization. Transportation Research Part B:Methodological, 2017, 95:421-441.
[31] Y Guan, Y Ren, S E Li, et al. Centralized cooperation for connected and auto- mated vehicles at intersections by proximal policy optimization. IEEE Transactions on Vehicular Technology, 2020, 69(11):12597-12608.
[32] G Li, S Li, S Li, et al. Deep reinforcement learning enabled decision-making for autonomous driving at intersections. Automotive Innovation, 2020, 3(4):374-385.
[33] S M LaValle. Rapidly-exploring random trees:A new tool for path planning. Technical Report, TR98-11, Ames, USA:Department of Computer Science, Iowa State University, 1998.
[34] S Karaman, E Frazzoli. Sampling-based algorithms for optimal motion planning. The International Journal of Robotics Research, 2011, 30(7):846-894.
[35] Y Kuwata, J Teo, S Karaman, et al. Motion planning in complex environments using closed-loop prediction. AIAA Guidance, Navigation and Control Conference and Exhibit, 2008:7166.
[36] D J Webb, J Van Den Berg. Kinodynamic rrt*:Asymptotically optimal motion planning for robots with linear dynamics. 2013 IEEE International Conference on Robotics and Automation, IEEE, 2013:5054-5061.
[37] L Ma, J Xue, K Kawabata, et al. Efficient sampling-based motion planning for on-road autonomous driving. IEEE Transactions on Intelligent Transportation Systems, 2015, 16(4):1961-1976.
[38] C Chen, M Rickert, A Knoll. Combining task and motion planning for intersection assistance systems. 2016 IEEE Intelligent Vehicles Symposium (IV), IEEE, 2016:1242-1247.
[39] S Yoon, D Lee, J Jung, et al. Spline-based rrt* using piecewise continuous collision-checking algorithm for car-like vehicles," Journal of Intelligent & Robotic Systems, 2018, 90(3):537-549.
[40] S S Mehta, C Ton, M J McCourt, et al. Human-assisted rrt for path planning in urban environments. 2015 IEEE International Conference on Systems, Man, and Cybernetics, IEEE, 2015:941-946.
[41] A Zyner, S Worrall, E Nebot. Naturalistic driver intention and path prediction using recurrent neural networks. IEEE Transactions on Intelligent Transportation Systems, 2019.
[42] D J Phillips, T A Wheeler, M J Kochenderfer. Generalizable intention prediction of human drivers at intersections. 2017 IEEE Intelligent Vehicles Symposium (IV), IEEE, 2017:1665-1670.
[43] A Zyner, S Worrall, E Nebot. A recurrent neural network solution for predicting driver intention at unsignalized intersections. IEEE Robotics and Automation Letters, 2018, 3(3):1759-1764.
[44] X Huang, S G McGill, B C Williams, et al. Uncertainty-aware driver trajectory prediction at urban intersections. 2019 International Conference on Robotics and Automation (ICRA), IEEE, 2019:9718-9724.
[45] D Roy, T Ishizaka, C K Mohan, et al. Vehicle trajectory prediction at intersections using interaction based generative adversarial networks. 2019 IEEE Intelligent Transportation Systems Conference (ITSC), IEEE, 2019:2318-2323.
[46] M S Shirazi, B T Morris. Trajectory prediction of vehicles turning at intersections using deep neural networks. Machine Vision and Applications, 2019, 30(6):1097-1109.
[47] S Lefe`vre, D Vasquez, C Laugier. A survey on motion prediction and risk assessment for intelligent vehicles. ROBOMECH Journal, 2014, 1(1):1.
[48] J Zhang, G Li, Z Deng, et al. Interaction-aware cut-in behavior prediction and risk assessment for autonomous driving. IFAC- PapersOnLine, 2020, 53(5):656-663.
[49] Y Jeong, K Yi, S Park. SVM based intention inference and motion planning at uncontrolled intersection. IFAC-PapersOnLine, 2019, 52(8):356-361.
[50] Y Jeong, S Kim, K Yi. Surround vehicle motion prediction using LSTM-RNN for motion planning of autonomous vehicles at multi-lane turn intersections. IEEE Open Journal of Intelligent Transportation Systems, 2020, 1:2-14.
[51] Y Wang, Z Liu, Z Zuo, et al. Trajectory planning and safety assessment of autonomous vehicles based on motion prediction and model predictive control. IEEE Transactions on Vehicular Technology, 2019, 68(9):8546-8556.
[52] T Zhang, W Song, M Fu, et al. Vehicle motion prediction at intersections based on the turning intention and prior trajectories model. IEEE/CAA Journal of Automatica Sinica, 2021.
[53] P Falcone, F Borrelli, H E Tseng, et al. A hierarchical model predictive control framework for autonomous ground vehicles. 2008 American Control Conference, IEEE, 2008:3719-3724.
[54] P Polack, F Altché, B d'Andréa-Novel, et al. The kinematic bicycle model:A consistent model for planning feasible trajectories for autonomous vehicles? 2017 IEEE Intelligent Vehicles Symposium (IV), IEEE, 2017:812-818.
[55] F Altché, P Polack, A de La Fortelle. High-speed trajectory planning for autonomous vehicles using a simple dynamic model. 2017 IEEE 20th International Conference on Intelligent Transportation Systems (ITSC), IEEE, 2017:1-7.
[56] Y Rasekhipour. Prioritized obstacle avoidance in motion planning of autonomous vehicles, 2017.
[57] G Schildbach, M Soppert, F Borrelli. A collision avoidance system at intersections using robust model predictive control. 2016 IEEE Intelligent Vehicles Symposium (IV), IEEE, 2016:233-238.
[58] R Hult, M Zanon, S Gros, et al. Optimal coordination of automated vehicles at intersections:Theory and experiments. IEEE Transactions on Control Systems Technology, 2018, 27(6):2510-2525.
[59] J Nilsson, M Bra¨nnström, J Fredriksson, et al. Longitudinal and lateral control for automated yielding maneuvers. IEEE Transactions on Intelligent Transportation Systems, 2016, 17(5):1404-1414.
[60] C Liu, S Lee, S Varnhagen, et al. Path planning for autonomous vehicles using model predictive control. 2017 IEEE Intelligent Vehicles Symposium (IV), IEEE, 2017:174-179.
[61] C Liu, W Zhan, M Tomizuka. Speed profile planning in dynamic environments via temporal optimization. 2017 IEEE Intelligent Vehicles Symposium (IV), IEEE, 2017:154-159.
[62] J Moreau, P Melchior, S Victor, et al. Reactive path planning for autonomous vehicle using Bézier curve optimization. 2019 IEEE Intelligent Vehicles Symposium (IV), IEEE, 2019:1048-1053.
[63] J Moreau, P Melchior, S Victor, et al. Reactive path planning in intersection for autonomous vehicle. IFAC-PapersOnLine, 2019, 52(5):109-114.
[64] S Hecker, D Dai, L Van Gool. End-to-end learning of driving models with surround-view cameras and route planners. Proceedings of the European Conference on Computer Vision (ECCV), 2018:435-453.
[65] W Zeng, W Luo, S Suo, et al. End-to-end interpretable neural motion planner. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2019:8660-8669.
[66] M Bouton, A Nakhaei, K Fujimura, et al. Safe reinforcement learning with scene decomposition for navigating complex urban environments. 2019 IEEE Intelligent Vehicles Symposium (IV), IEEE, 2019:1469-1476.
[67] D Kamran, C F Lopez, M Lauer, et al. Risk-aware high-level decisions for automated driving at occluded intersections with reinforcement learning. arXiv preprint arXiv:2004.04450, 2020.
[68] T Tram, A Jansson, R Grönberg, et al. Learning negotiating behavior between cars in intersections using deep q-learning. 2018 21st International Conference on Intelligent Transportation Systems (ITSC), IEEE, 2018:3169-3174.
[69] A Demir, V Sezer. Intersection navigation under dynamic constraints using deep reinforcement learning. 2018 6th International Conference on Control Engineering & Information Technology (CEIT), IEEE, 2018:1-5.
[70] D Isele, R Rahimi, A Cosgun, et al. Navigating occluded intersections with autonomous vehicles using deep reinforcement learning. 2018 IEEE International Conference on Robotics and Automation (ICRA), IEEE, 2018:2034-2039.
[71] Z Qiao, K Muelling, J M Dolan, et al. Automatically generated curriculum-based reinforcement learning for autonomous vehicles in urban environment. 2018 IEEE Intelligent Vehicles Symposium (IV), IEEE, 2018:1233-1238.
[72] D Aberdeen. A (revised) survey of approximate methods for solving partially observable Markov decision processes. Technical Report, National ICT Australia, Tech. Rep., 2003.
[73] M Bouton, A Cosgun, M J Kochenderfer. Belief state planning for autonomously navigating urban inter- sections. 2017 IEEE Intelligent Vehicles Symposium (IV), IEEE, 2017:825-830.
[74] C Hubmann, M Becker, D Althoff, et al. Decision making for autonomous driving considering interaction and uncertain prediction of sur- rounding vehicles. 2017 IEEE Intelligent Vehicles Symposium (IV), IEEE, 2017:1671-1678.
[75] C Hubmann, J Schulz, M Becker, et al. Automated driving in uncertain environments:Planning with interaction and uncertain maneuver prediction. IEEE Transactions on Intelligent Vehicles, 2018, 3(1):5-17.
[76] K Shu, H Yu, X Chen, et al. Autonomous driving at intersections:A behavior-oriented critical-turning-point approach for decision making. IEEE/ASME Transactions on Mechatronics, 2021.
[77] K Shu, H Yu, X Chen, et al. Autonomous driving at intersections:a critical- turning-point approach for left turns. 2020 IEEE 23rd International Conference on Intelligent Transportation Systems (ITSC), IEEE, 2020:1-6.
[78] C Hubmann, N Quetschlich, J Schulz, et al. A pomdp maneuver plan- ner for occlusions in urban scenarios. 2019 IEEE Intelligent Vehicles Symposium (IV), IEEE, 2019:2172-2179.
[79] S Brechtel, T Gindele, R Dillmann. Probabilistic decision-making under uncertainty for autonomous driving using continuous POMDPs. 17th International IEEE Conference on Intelligent Transportation Systems (ITSC), IEEE, 2014:392-399.
[80] B De Schutter, d B De Moor. Optimal traffic light control for a single intersection. European Journal of Control, 1998, 4(3):260-276.
[81] E Bingham. Reinforcement learning in neurofuzzy traffic signal control. European Journal of Operational Research, 2001, 131(2):232-241.
[82] M Wiering. Multi-agent reinforcement learning for traffic light control. Machine Learning:Proceedings of the Seventeenth International Conference (ICML'2000), Stanford, CA, USA:Morgan Kaufmann, 2000:1151-1158.
[83] B Abdulhai, R Pringle, G J Karakoulas. Reinforcement learning for true adaptive traffic signal control. Journal of Transportation Engineering, 2003, 129(3):278-285.
[84] D Houli, L Zhiheng, Z Yi. Multiobjective reinforcement learning for traffic signal control using vehicular ad hoc network. EURASIP Journal on Advances in Signal Processing, 2010:7.
[85] B Asadi, A Vahidi. Predictive cruise control:Utilizing upcoming traffic signal information for improving fuel economy and reducing trip time. IEEE Transactions on Control Systems Technology, 2010, 19(3):707-714.
[86] B Xu, X J Ban, Y Bian, et al. V2I based cooperation between traffic signal and approaching automated vehicles. 2017 IEEE Intelligent Vehicles Symposium (IV), California, USA:IEEE, 2017:1658-1664.
[87] L Li, F-Y Wang. Cooperative driving at blind crossings using intervehicle communication. IEEE Transactions on Vehicular Technology, 2006, 55(6):1712-1724.
[88] Y Meng, L Li, F-Y Wang, et al. Analysis of cooperative driving strategies for nonsignalized intersections. IEEE Transactions on Vehicular Technology, 2017, 67(4):2900-2911.
[89] K Dresner, P Stone. Multiagent traffic management:A reservation-based intersection control mechanism. Autonomous Agents and Multiagent Systems, International Joint Conference on, IEEE Computer Society, 2004:530-537.
[90] A A Malikopoulos, C G Cassandras, Y J Zhang. A decentralized energy-optimal control framework for connected automated vehicles at signal-free intersections. Automatica, 2018, 93:244-256.
[91] Q Jin, G Wu, K Boriboonsomsin, et al. Platoon-based multi-agent intersection management for connected vehicle. 16th International IEEE Conference on Intelligent Transportation Systems (ITSC 2013), IEEE, 2013:1462-1467.
[92] B Filocamo, J A Ruiz, M A Sotelo. Efficient management of road intersections for automated vehicles-the frfp system applied to the various types of intersections and roundabouts. Applied Sciences, 2020, 10(1):316.
[93] Q Lu, K-D Kim. Intelligent intersection management of autonomous traffic using discrete-time occupancies trajectory. Journal of Traffic and Logistics Engineering, 2016, 4(1):1-6.
[94] H Xu, Y Zhang, L Li, et al. Cooperative driving at unsignalized intersections using tree search. IEEE Transactions on Intelligent Transportation Systems, 2019, 21(11):4563-4571.
[95] S I Guler, M Menendez, L Meier. Using connected vehicle technology to improve the efficiency of intersections. Transportation Research Part C:Emerging Technologies, 2014, 46:121-131.
[96] K Yang, S I Guler, M Menendez. Isolated intersection control for various levels of vehicle technology:Conventional, connected, and automated vehicles. Transportation Research Part C:Emerging Technologies, 2016, 72:109-129.
[97] M A Guney, I A Raptis. Scheduling-based optimization for motion coordination of autonomous vehicles at multilane intersections. Journal of Robotics, 2020.
[98] Q Lu, K-D Kim. A mixed integer programming approach for autonomous and connected intersection crossing traffic control. 2018 IEEE 88th Vehicular Technology Conference (VTC-Fall), IEEE, 2018:1-6.
[99] C Liu, C-W Lin, S Shiraishi, et al. Distributed conflict resolution for connected autonomous vehicles. IEEE Transactions on Intelligent Vehicles, 2017, 3(1):18-29.
[100] M Bashiri, C H Fleming. A platoon-based inter- section management system for autonomous vehicles. 2017 IEEE Intelligent Vehicles Symposium (IV), IEEE, 2017:667-672.
[101] R Timmerman, M A Boon. Platoon forming algorithms for intelligent street intersections. Transportmetrica A:Transport Science, 2021, 17(3):278-307.
[102] K Pandit, D Ghosal, H M Zhang, et al. Adaptive traffic signal control with vehicular ad hoc networks. IEEE Transactions on Vehicular Technology, 2013, 62(4):1459-1471.
[103] A Dey, A Pal. Fuzzy graph coloring technique to classify the accidental zone of a traffic control. Annals of Pure and Applied Mathematics, 2013, 3(2):169-178.
[104] Y-T Lin, H Hsu, S-C Lin, et al. Graph-based modeling, scheduling, and verification for intersection management of intelligent vehicles. ACM Transactions on Embedded Computing Systems (TECS), 2019, 18(5s):1-21.
[105] Z Deng, Y Shi, Q Han, et al. A conflict duration graph-based coordination method for connected and automated vehicles at signal-free intersections. Applied Sciences, 2020, 10(18):6223.
[106] H Ahn, D Del Vecchio. Safety verification and control for collision avoidance at road intersections. IEEE Transactions on Automatic Control, 2017, 63(3):630-642.
[107] H Ahn, A Colombo. Abstraction-based safety verification and control of cooperative vehicles at road intersections. IEEE Transactions on Automatic Control, 2019.
[108] D Miculescu, S Karaman. Polling-systems-based autonomous vehicle coordination in traffic intersections with no traffic signals. IEEE Transactions on Automatic Control, 2019, 65(2):680-694.
[109] C Chen, Q Xu, M Cai, et al. A graph-based conflict-free cooperation method for intelligent electric vehicles at unsignalized intersections. arXiv preprint arXiv:2103.14290, 2021.
[110] C Chen, Q Xu, M Cai, et al. Conflict-free cooperation method for connected and automated vehicles at unsignalized intersections:Graph-based modeling and optimality analysis. arXiv preprint arXiv:2107.07179, 2021.
[111] V Milanés, J Pérez, E Onieva, et al. Controller for urban intersections based on wireless communications and fuzzy logic. IEEE Transactions on Intelligent Transportation Systems, 2010, 11(1):243-248.
[112] E Onieva, V Milanés, J Villagra, et al. Genetic optimization of a vehicle fuzzy decision system for intersections. Expert Systems with Applications, 2012, 39(18):13148-13157.
[113] Y J Zhang, A A Malikopoulos, C G Cassandras. Optimal control and coordination of connected and automated vehicles at urban traffic intersections. 2016 American Control Conference (ACC), IEEE, 2016:6227-6232.
[114] Y Zhang, A A Malikopoulos, C G Cassandras. Decentralized optimal control for connected automated vehicles at intersections including left and right turns. 2017 IEEE 56th Annual Conference on Decision and Control (CDC), IEEE, 2017:4428-4433.
[115] Y Zhang, C G Cassandras, A A Malikopoulos. Optimal control of connected automated vehicles at urban traffic intersections:A feasibility enforcement analysis. 2017 American Control Conference (ACC), IEEE, 2017:3548-3553.
[116] Y Zheng, S E Li, K Li, et al. Stability margin improvement of vehicular platoon considering undirected topology and asymmetric control. IEEE Transactions on Control Systems Technology, 2015, 24(4):1253-1265.
[117] H Yang, H Rakha, M V Ala. Eco-cooperative adaptive cruise control at signalized intersections considering queue effects. IEEE Transactions on Intelligent Transportation Systems, 2016, 18(6):1575-1585.
[118] M V Ala, H Yang, H Rakha. Modeling evaluation of eco-cooperative adaptive cruise control in vicinity of signalized intersections. Transportation Research Record, 2016, 2559(1):108-119.
[119] H Rakha, R K Kamalanathsharma. Eco-driving at signalized intersections using v2i communication. 2011 14th International IEEE Conference on Intelligent Transportation Systems (ITSC), IEEE, 2011:341-346.
[120] C Sun, J Guanetti, F Borrelli, et al. Robust eco-driving control of autonomous vehicles connected to traffic lights. arXiv preprint arXiv:1802.05815, 2018.
[121] X Qi, P Wang, G Wu, et al. Connected cooperative eco-driving system considering human driver error. IEEE Transactions on Intelligent Transportation Systems, 2018, 19(8):2721-2733.
[122] H Jiang, J Hu, S An, et al. Eco approaching at an isolated signalized intersection under partially connected and automated vehicles environment. Transportation Research Part C:Emerging Technologies, 2017, 79:290-307.
[123] S E Li, S Xu, X Huang, et al. Eco-departure of connected vehicles with v2x communication at signalized intersections. IEEE Transactions on Vehicular Technology, 2015, 64(12):5439-5449.
[124] L Li, F-Y Wang, H Kim. Cooperative driving and lane changing at blind crossings. IEEE Proceedings Intelligent Vehicles Symposium, 2005, IEEE, 2005:435-440.
[125] L Li, F-Y Wang, Y Zhang. Cooperative driving at lane closures. 2007 IEEE Intelligent Vehicles Symposium, IEEE, 2007:1156-1161.
[126] G Lu, Y M Nie, X Liu, et al. Trajectory-based traffic management inside an autonomous vehicle zone. Transportation Research Part B:Methodological, 2019, 120:76-98.
[127] X Hu, J Sun. Trajectory optimization of connected and autonomous vehicles at a multilane freeway merging area. Transportation Research Part C:Emerging Technologies, 2019, 101:111-125.
[128] H Xu, Y Zhang, C G Cassandras, et al. A bi-level cooperative driving strategy allowing lane changes. Transportation Research Part C:Emerging Technologies, 2020, 120:102773.
[129] C Chen, M Cai, J Wang, et al. Cooperation method of connected and automated vehicles at unsignalized intersections:Lane changing and arrival scheduling. arXiv preprint arXiv:2109.14175, 2021.
[130] Z He, L Zheng, L Lu, et al. Erasing lane changes from roads:A design of future road intersections. IEEE Transactions on Intelligent Vehicles, 2018, 3(2):173-184.
[131] M Cai, Q Xu, C Chen, et al. Multi-lane unsignalized intersection cooperation with flexible lane direction based on multi-vehicle formation control. arXiv preprint arXiv:2108.11112, 2021.
[132] N Mitrovic, I Dakic, A Stevanovic. Combined alternate-direction lane assignment and reservation-based intersection control. IEEE Transactions on Intelligent Transportation Systems, 2019, 21(4):1779-1789.
[133] J Contreras-Castillo, S Zeadally, J A Guerrero- Iban?ez. Internet of vehicles:architecture, protocols, and security. IEEE Internet of Things Journal, 2017, 5(5):3701-3709.
[134] J Guanetti, Y Kim, F Borrelli. Control of connected and automated vehicles:State of the art and future challenges. Annual Reviews in Control, 2018, 45:18-40.
[135] J A Volpe. Vehicle-infrastructure integration (VII) initiative benefit-cost analysis version 2.3 (draft). National Transportation Systems Center, FHWA, 2008.
[136] Long-term development planning of automobile industry. Beijing:Tech. Rep., 2017.
[137] Y Zheng, J Wang, K Li. Smoothing traffic flow via control of autonomous vehicles. IEEE Internet of Things Journal, 2020, 7(5):3882-3896.
[138] C Priemer, B Friedrich. A decentralized adaptive traffic signal control using V2I communication data. 2009 12th International IEEE Conference on Intelligent Transportation Systems, St. Louis, MO, USA:IEEE, 2009:1-6.
[139] Y Feng, C Yu, H X Liu. Spatiotemporal intersection control in a connected and automated vehicle environment. Transportation Research Part C:Emerging Technologies, 2018, 89:364-383.
[140] M A Salman, S Ozdemir, F V Celebi. Fuzzy logic based traffic surveillance system using cooperative V2X protocols with low penetration rate. 2017 International Symposium on Networks, Computers and Communications (ISNCC), Marrakech, Morocco:IEEE, 2017:1-6
[141] P G Gipps. A behavioural car-following model for computer simulation. Transportation Research Part B:Methodological, 1981, 15(2):105-111.
[142] D Helbing, B Tilch. Generalized force model of traffic dynamics. Physical Review E, 1998, 58(1):133.
[143] M Treiber, A Kesting. Traffic Flow Dynamics:Data, Models and Simulation, Springer-Verlag Berlin Heidelberg, 2013.
[144] Z Du, B HomChaudhuri, P Pisu. Coordination strategy for vehicles passing multiple signalized intersections:A connected vehicle penetration rate study. 2017 American Control Conference (ACC), IEEE, 2017:4952-4957.
[145] S Fang, L Yang, T Wang, et al. Trajectory planning method for mixed vehicles considering traffic stability and fuel consumption at the signalized intersection. Journal of Advanced Transportation, 2020, No. Article ID 1456207.
[146] X Liang, S I Guler, V V Gayah. Joint optimization of signal phasing and timing and vehicle speed guidance in a connected and autonomous vehicle environment. Transportation Research Record, 2019, 2673(4):70-83.
[147] C Chen, J Wang, Q Xu, et al. Mixed platoon control of automated and human-driven vehicles at a signalized intersection:dynamical analysis and optimal control. Transportation Research Part C:Emerging Technologies, 2021, 127:103138.
[148] R E Stern, S Cui, M L Delle Monache, et al. Dissipation of stop-and-go waves via control of autonomous vehicles:Field experiments. Transportation Research Part C:Emerging Technologies, 2018, 89:205-221.
[149] J Wang, Y Zheng, C Chen, et al. Leading cruise control in mixed traffic flow. Conference on Decision and Control (CDC), IEEE, 2020:1-7.
[150] E Vinitsky, K Parvate, A Kreidieh, et al. Lagrangian control through deep-RL:Applications to bottleneck decongestion. 2018 21st International Conference on Intelligent Transportation Systems (ITSC), IEEE, 2018:759-765.
[151] C Wu, A Kreidieh, E Vinitsky, et al. Emergent behaviors in mixed-autonomy traffic. Conference on Robot Learning, 2017:398-407.
[152] Y Feng, K L Head, S Khoshmagham, et al. A real-time adaptive signal control in a connected vehicle environment. Transportation Research Part C:Emerging Technologies, 2015, 55:460-473.
[153] R Niroumand, M Tajalli, L Hajibabai, et al. The effects of the "white phase" on intersection performance with mixed-autonomy traffic stream. 2020 IEEE 23rd International Conference on Intelligent Transportation Systems (ITSC), IEEE, 2020:1-6.
[154] Q Guo, L Li, X J Ban. Urban traffic signal control with connected and automated vehicles:A survey. Transportation Research Part C:Emerging Technologies, 2019, 101:313-334.
[155] Y Zhang, C G Cassandras. An impact study of integrating connected automated vehicles with conventional traffic. Annual Reviews in Control, 2019, 48:347-356.
[156] R Niroumand, M Tajalli, L Hajibabai, et al. Joint optimization of vehicle-group trajectory and signal timing:Introducing the white phase for mixed-autonomy traffic stream. Transportation Research Part C:Emerging Technologies, 2020, 116:102659.
[157] Y Bian, S E Li, W Ren, et al. Cooperation of multiple connected vehicles at unsignalized intersections:Distributed observation, optimization, and control. IEEE Transactions on Industrial Electronics, 2019, 67(12):10744-10754.
[158] C Wuthishuwong, A Traechtler, T Bruns. Safe trajectory planning for autonomous intersection management by using vehicle to infrastructure communication. EURASIP Journal on Wireless Communications and Networking, 2015, 1:1-12.
[159] F Perronnet, A Abbas-Turki, A El Moudni. A sequenced-based protocol to manage autonomous vehicles at isolated intersections. 16th International IEEE Conference on Intelligent Transportation Systems (ITSC 2013), IEEE, 2013:1811-1816.
[160] O A Mahmood, A Khakimov, A Muthanna, et al. Effect of heterogeneous traffic on quality of service in 5G network. International Conference on Distributed Computer and Communication Networks, Springer, 2019:469-478.
[161] M Beshley, N Kryvinska, M Seliuchenko, et al. End-to-end qos "smart queue" management algorit
