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· Apply multiple spatiotemporal constraints to lane-free trajectory planning.
· Present a spatiotemporal sparse sampling method to enhance the heuristic search.
· Provide an effective decision-making scheme for mixed flows at intersections.
Improving the capacity of intersections is the key to enhancing road traffic systems. Benefiting from the application of Connected Automated Vehicles (CAVs) in the foreseeing future, it is promising to fully utilize spatiotemporal resources at intersections through cooperative and intelligent trajectory planning for CAVs. Lane-free traffic is currently a highly anticipated solution that can achieve more flexible trajectories without being limited by lane boundaries. However, it is challenging to apply efficient lane-free traffic to be compatible with the traditional intersection control mode for mixed flow composed of CAVs and Human-driving Vehicles (HVs). To address the research gap, this paper proposes a spatiotemporal-restricted A* algorithm to obtain efficient and flexible lane-free trajectories for CAVs. First, we restrict the feasible area of the heuristic search algorithm by considering the feasible area and orientation of vehicles to maintain the trajectory directionality of different turning behaviors. Second, we propose a spatiotemporal sparse sampling method by defining the four-dimensional spatiotemporal grid to accelerate the execution of the heuristic search algorithm. Third, we consider the motions of HVs as dynamic obstacles with rational trajectory fluctuation during the process of trajectory planning for CAVs. The proposed method can retain the advantage of efficiently exploring feasible trajectories through the hybrid A* algorithm, while also utilizing multiple spatiotemporal constraints to accelerate solution efficiency. The experimental results of the simulated and real scenarios with mixed flows show that the proposed model can continuously enhance traffic efficiency and fuel economy as the penetration of CAVs gradually increases.
Ishaque MM, Noland RB. Making roads safe for pedestrians or keeping them out of the way? An historical perspective on pedestrian policies in Britain. J Transport Hist 2006;27(1):115-37.
de Oliveira LFP, Manera LT, Da Luz PDG. Development of a smart traffic light control system with real-time monitoring. IEEE Internet Things J 2020;8(5):3384-93.
Xu B, Ban XJ, Bian Y, Li W, Wang J, Li SE, et al. Cooperative method of traffic signal optimization and speed control of connected vehicles at isolated intersections. IEEE Trans Intell Transport Syst 2018;20(4):1390-403.
Viti F, van Zuylen HJ. A probabilistic model for traffic at actuated control signals. Transport Res C Emerg Technol 2010;18(3):299-310.
Zheng X, Recker W. An adaptive control algorithm for traffic-actuated signals. Transport Res C Emerg Technol 2013;30:93-115.
Yuan H, Zhang H, Liu X, Jiao X. Traffic wave model based on vehicle-infrastructure cooperative and vehicle communication data. Comput Intell 2020;36(4):1755-72.
Guanetti J, Kim Y, Borrelli F. Control of connected and automated vehicles: state of the art and future challenges. Annu Rev Control 2018;45:18-40.
Wu J, Qu X. Intersection control with connected and automated vehicles: a review. J Intell Connect Veh 2022;5(3):260-9.
Yu C, Feng Y, Liu HX, Ma W, Yang X. Integrated optimization of traffic signals and vehicle trajectories at isolated urban intersections. Transp Res Part B Methodol 2018;112:89-112.
Pourmehrab M, Emami P, Martin-Gasulla M, Wilson J, Elefteriadou L, Ranka S. Signalized intersection performance with automated and conventional vehicles: a comparative study. J Transport Eng, Part A: Systems 2020;146(9):04020089.
He S, Ding F, Lu C, Qi Y. Impact of connected and autonomous vehicle dedicated lane on the freeway traffic efficiency. European Transport Research Review 2022;14(1):12.
Yang X, Zou Y, Chen L. Operation analysis of freeway mixed traffic flow based on catch-up coordination platoon. Accid Anal Prev 2022;175:106780.
Levin MW, Boyles SD, Patel R. Paradoxes of reservation-based intersection controls in traffic networks. Transport Res Pol Pract 2016;90:14-25.
Zhang J, Li S, Li L. Coordinating CAV swarms at intersections with a deep learning model. IEEE Trans Intell Transport Syst 2023.
Antonio GP, Maria-Dolores C. Multi-agent deep reinforcement learning to manage connected autonomous vehicles at tomorrow's intersections. IEEE Trans Veh Technol 2022;71(7):7033-43.
Shao C, Cheng F, Xiao J, Zhang K. Vehicular intelligent collaborative intersection driving decision algorithm in Internet of Vehicles. Future Generat Comput Syst 2023;145:384-95.
Sekeran M, Rostami-Shahrbabaki M, Syed AA, Margreiter M, Bogenberger K. Lane-free traffic: history and state of the art. International Conference on Intelligent Transportation Systems (ITSC) 2022:1037-42.
Rostami-Shahrbabaki M, Weikl S, Niels T, Bogenberger K. Modeling vehicle flocking in lane-free automated traffic. Transport Res Rec 2023.
McConky K, Rungta V. Don't pass the automated vehicles!: system level impacts of multi-vehicle CAV control strategies. Transport Res C Emerg Technol 2019;100:289-305.
Liu Q, Lin X, Li M, Li L, He F. Coordinated lane-changing scheduling of multilane CAV platoons in heterogeneous scenarios. Transport Res C Emerg Technol 2023;147:103992.
Fu L, Sun D, Rilett LR. Heuristic shortest path algorithms for transportation applications: state of the art. Comput Oper Res 2006;33(11):3324-43.
Bast H, Delling D, Goldberg A, Müller-Hannemann M, Pajor T, Sanders P, et al. Route planning in transportation networks. Algorithm engineering: Selected Results and Surveys 2016:19-80.
Fu D, Cai P, Lin Y, Mao S, Wen L, Li Y. Incremental path planning: reservation system in V2X environment. Phys Stat Mech Appl 2023:128914.
Xu W, Wei J, Dolan JM, Zhao H, Zha H. A real-time motion planner with trajectory optimization for autonomous vehicles. IEEE International Conference on Robotics and Automation (ICRA) 2012:2061-7.
Mu C, Du L, Zhao X. Event triggered rolling horizon based systematical trajectory planning for merging platoons at mainline-ramp intersection. Transport Res C Emerg Technol 2021;125:103006.
Yang C, Chen X, Lin X, Li M. Coordinated trajectory planning for lane-changing in the weaving areas of dedicated lanes for connected and automated vehicles. Transport Res C Emerg Technol 2022;144:103864.
Amini MR, Feng Y, Yang Z, Kolmanovsky I, Sun J. Long-term vehicle speed prediction via historical traffic data analysis for improved energy efficiency of connected electric vehicles. Transport Res Rec 2020;2674(11):17-29.
Yang XT, Huang K, Zhang Z, Zhang ZA, Lin F. Eco-driving system for connected automated vehicles: multi-objective trajectory optimization. IEEE Trans Intell Transport Syst 2020;22(12):7837-49.
Wang T, Zhang J, He J. Dynamic wireless charging lane reversal for connected and automated electric vehicles in highway. Sustain Energy Technol Assessments 2023;57:103206.
Ard T, Guo L, Han J, Jia Y, Vahidi A, Karbowski D. Energy-efficient driving in connected corridors via minimum principle control: vehicle-in-the-loop experimental verification in mixed fleets. IEEE Transactions on Intelligent Vehicles 2023;8(2):1279-91.
Troullinos D, Chalkiadakis G, Papamichail I, Papageorgiou M. Collaborative multiagent decision making for lane-free autonomous driving. International Conference on Autonomous Agents and Multi-Agent Systems (AAMAS) 2021:1335-43.
Berahman M, Rostmai-Shahrbabaki M, Bogenberger K. Driving strategy for vehicles in lane-free traffic environment based on deep deterministic policy gradient and artificial forces. IFAC-PapersOnLine 2022;55(14):14-21.
Naderi M, Papageorgiou M, Karafyllis I, Papamichail I. Automated vehicle driving on large lane-free roundabouts. International Conference on Intelligent Transportation Systems (ITSC) 2022:1528-35.
Yanumula VK, Typaldos P, Troullinos D, Malekzadeh M, Papamichail I, Papageorgiou M. Optimal trajectory planning for connected and automated vehicles in lane-free traffic with vehicle nudging. IEEE Transactions on Intelligent Vehicles 2023;8(3):2385-99.
Li B, Zhang Y. Fault-tolerant cooperative motion planning of connected and automated vehicles at a signal-free and lane-free intersection. IFAC-PapersOnLine 2018;51(24):60-7.
Li B, Zhang Y, Acarman T, Ouyang Y, Yaman C, Wang Y. Lane-free autonomous intersection management: a batch-processing framework integrating reservation based and planning-based methods. IEEE International Conference on Robotics and Automation (ICRA) 2021:7915-21.
Pourmehrab M, Elefteriadou L, Ranka S, Martin-Gasulla M. Optimizing signalized intersections performance under conventional and automated vehicles traffic. IEEE Trans Intell Transport Syst 2019;21(7):2864-73.
Long K, Ma C, Jiang Z, Wang Y, Yang X. Integrated optimization of traffic signals and vehicle trajectories at intersection with the consideration of safety during signal change. IEEE Access 2020;8:170732-41.
Dresner K, Stone P. Multiagent traffic management: a reservation-based intersection control mechanism. International Joint Conference on Autonomous Agents and Multiagent Systems (AAMAS) 2004;3:530-7.
Zhong Z, Nejad M, Lee EE. Autonomous and semiautonomous intersection management: a survey. IEEE Intelligent Transportation Systems Magazine 2020;13(2):53-70.
Dresner KM, Stone P. Sharing the road: autonomous vehicles meet human drivers. Int Joint Conf Artif Intell 2007;7:1263-8.
Hausknecht M, Au TC, Stone P. Autonomous intersection management: multi-intersection optimization. IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) 2011:4581-6.
Liu B, Shi Q, Song Z, El Kamel A. Trajectory planning for autonomous intersection management of connected vehicles. Simulat Model Pract Theor 2019;90:16-30.
Wang S, Mahlberg A, Levin MW. Optimal control of automated vehicles for autonomous intersection management with design specifications. Transport Res Rec 2023;2677(2):1643-58.
Kamal MAS, Imura JI, Hayakawa T, Ohata A, Aihara K. A vehicle-intersection coordination scheme for smooth flows of traffic without using traffic lights. IEEE Trans Intell Transport Syst 2014;16:1136-47.
Dolgov D, Thrun S, Montemerlo M, Diebel J. Path planning for autonomous vehicles in unknown semi-structured environments. Int J Robot Res 2010;29(5):485-501.
McNaughton M, Urmson C, Dolan JM, Lee JW. Motion planning for autonomous driving with a conformal spatiotemporal lattice. IEEE International Conference on Robotics and Automation (ICRA) 2011:4889-95.
Min H, Xiong X, Wang P, Yu Y. Autonomous driving path planning algorithm based on improved A* algorithm in unstructured environment. Proc Inst Mech Eng - Part D J Automob Eng 2021;235(2–3):513-26.
Meng T, Yang T, Huang J, Jin W, Zhang W, Jia Y, et al. Improved hybrid A-star algorithm for path planning in autonomous parking system based on multi-stage dynamic optimization. Int J Automot Technol 2023;24(2):459-68.
Li B, Zhang Y, Shao Z. Spatio-temporal decomposition: a knowledge-based initialization strategy for parallel parking motion optimization. Knowl Base Syst 2016;107:179-96.
Zhang Y, Cassandras CG, Malikopoulos AA. Optimal control of connected automated vehicles at urban traffic intersections: a feasibility enforcement analysis. American Control Conference (ACC) 2017:3548-53.
Reeds J, Shepp L. Optimal paths for a car that goes both forwards and backwards. Pac J Math 1990;145(2):367-93.
Ahn K, Rakha H, Trani A, Van Aerde M. Estimating vehicle fuel consumption and emissions based on instantaneous speed and acceleration levels. J Transport Eng 2002;128(2):182-90.
Zegeye SK, De Schutter B, Hellendoorn J, Breunesse EA, Hegyi A. Integrated macroscopic traffic flow, emission, and fuel consumption model for control purposes. Transport Res C Emerg Technol 2013;31:158-71.
Zhang J, Tang TQ, Wang T. Some features of car-following behavior in the vicinity of signalized intersection and how to model them. IET Intell Transp Syst 2019;13(11):1686-93.
Dubins LE. On curves of minimal length with a constraint on average curvature, and with prescribed initial and terminal positions and tangents. Am J Math 1957;79(3):497-516.
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