The Autonomous Intersection Control Method Based on Reduction in Vehicle Conflict Relationships

Mingjian Liu (), Chao Zheng and Yunhe Zhu
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Mingjian Liu: The Information Science & Engineering, Dalian Ocean University, Dalian 116023, China
Chao Zheng: Dalian Institute of Chemical Physics, Chinese Academy of Sciences, Dalian 116023, China
Yunhe Zhu: The Information Science & Engineering, Dalian Ocean University, Dalian 116023, China

Sustainability, 2023, vol. 15, issue 9, 1-14

Abstract: Current autonomous intersection control strategies are facing issues, such as lack of foresight, frequent occurrence of deadlock, and low control system efficiency. To address these issues, a vehicle–road cooperative autonomous intersection control strategy based on reducing vehicle conflict relationships is proposed in this study. First, a conflict relationship graph that can describe the driving conflict relationship between vehicles is constructed. Second, the complement of the maximum clique in the conflict relationship graph is solved to determine the set of accepted vehicle reservation requests, enabling more vehicle reservation requests to be successfully processed in unit time while ensuring safe driving at the intersection and improving intersection throughput efficiency. Third, based on the maximum clique method, a taboo search method is used to search the neighborhood, thus improving the quality of the final solution with a smaller search cost. Simulation results show that compared to other control strategies, such as the FCFS (First Come First Served) strategy, the traffic signal control strategy (Traffic-Light), and the control strategy based on greedy algorithm search (Batch-Light), the proposed strategy can considerably reduce the average vehicle waiting time by 42%, 19%, and 10%, respectively, as well as increasing the number of vehicles passing through the intersection per unit of time by 35%, 20%, and 12%, respectively. These results demonstrate the effectiveness of the proposed strategy in improving the throughput of the intersection and reducing the average vehicle waiting time.

Keywords: traffic engineering; autonomous intersection; vehicle-road cooperation; maximum clique; trajectory prediction (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2023
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