Spatiotemporal-restricted A∗ algorithm as a support for lane-free traffic at intersections with mixed flows
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 trajec...
| Main Authors: | , , , , , |
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| Other Authors: | |
| Format: | Journal Article |
| Language: | English |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://hdl.handle.net/10356/179852 |
| _version_ | 1824455802784579584 |
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| author | Chi, Haifei Cai, Pinlong Fu, Daocheng Zhai, Junda Zeng, Yadan Shi, Botian |
| author2 | School of Mechanical and Aerospace Engineering |
| author_facet | School of Mechanical and Aerospace Engineering Chi, Haifei Cai, Pinlong Fu, Daocheng Zhai, Junda Zeng, Yadan Shi, Botian |
| author_sort | Chi, Haifei |
| collection | NTU |
| description | 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. |
| first_indexed | 2025-02-19T03:44:00Z |
| format | Journal Article |
| id | ntu-10356/179852 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2025-02-19T03:44:00Z |
| publishDate | 2024 |
| record_format | dspace |
| spelling | ntu-10356/1798522024-08-31T16:48:22Z Spatiotemporal-restricted A∗ algorithm as a support for lane-free traffic at intersections with mixed flows Chi, Haifei Cai, Pinlong Fu, Daocheng Zhai, Junda Zeng, Yadan Shi, Botian School of Mechanical and Aerospace Engineering Robotics Research Centre Engineering Connected automated vehicles Trajectory planning 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. Published version This work was supported by the Science and Technology Commission of Shanghai Municipality (Nos. 22YF1461400 and 22DZ1100102) and the National Key R&D Program of China (No. 2022ZD0160104). 2024-08-27T08:06:27Z 2024-08-27T08:06:27Z 2024 Journal Article Chi, H., Cai, P., Fu, D., Zhai, J., Zeng, Y. & Shi, B. (2024). Spatiotemporal-restricted A∗ algorithm as a support for lane-free traffic at intersections with mixed flows. Green Energy and Intelligent Transportation, 3(2), 100159-. https://dx.doi.org/10.1016/j.geits.2024.100159 2773-1537 https://hdl.handle.net/10356/179852 10.1016/j.geits.2024.100159 2-s2.0-85189516944 2 3 100159 en Green Energy and Intelligent Transportation © 2024 The Authors. Published by Elsevier Ltd on behalf of Beijing Institute of Technology Press Co., Ltd. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/). application/pdf |
| spellingShingle | Engineering Connected automated vehicles Trajectory planning Chi, Haifei Cai, Pinlong Fu, Daocheng Zhai, Junda Zeng, Yadan Shi, Botian Spatiotemporal-restricted A∗ algorithm as a support for lane-free traffic at intersections with mixed flows |
| title | Spatiotemporal-restricted A∗ algorithm as a support for lane-free traffic at intersections with mixed flows |
| title_full | Spatiotemporal-restricted A∗ algorithm as a support for lane-free traffic at intersections with mixed flows |
| title_fullStr | Spatiotemporal-restricted A∗ algorithm as a support for lane-free traffic at intersections with mixed flows |
| title_full_unstemmed | Spatiotemporal-restricted A∗ algorithm as a support for lane-free traffic at intersections with mixed flows |
| title_short | Spatiotemporal-restricted A∗ algorithm as a support for lane-free traffic at intersections with mixed flows |
| title_sort | spatiotemporal restricted a∗ algorithm as a support for lane free traffic at intersections with mixed flows |
| topic | Engineering Connected automated vehicles Trajectory planning |
| url | https://hdl.handle.net/10356/179852 |
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