Conflict-free Cooperation Method for Connected and Automated Vehicles at Unsignalized Intersections: Graph-based Modeling and Optimality Analysis

Chen, Chaoyi; Xu, Qing; Cai, Mengchi; Wang, Jiawei; Wang, Jianqiang; Xu, Bo; Li, Keqiang

doi:10.48550/arxiv.2107.07179

Cited by 4 publications

(11 citation statements)

References 31 publications

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“…, V k in the descending order and obtain the spanning G N +1 7: Exchange the conflicting CAVs of G N +1 in the same lane if necessary 8: return G N +1 6. We have proved that this can be solved by exchanging the unfeasible sequence [23], as shown in Lemma 1.…”

Section: Minimum Clique Cover Schedulingmentioning

confidence: 96%

“…In [23], we introduced a graph-based minimum clique cover (MCC) method applied to an intersection with complex vehicle conflict relationships, as shown in Fig. 1.…”

Section: Two-stage Cooperation Frameworkmentioning

confidence: 99%

“…Considering the limit range of V2X communication, previous studies usually set a certain range of the control zone to design cooperation algorithms [2], [3], [23], [24]. In our study, we aim to decouple the longitudinal and lateral control problem in CAVs; therefore, the control zone is divided into a lane changing zone and a car-following zone, as shown in Fig.…”

Section: Two-stage Cooperation Frameworkmentioning

confidence: 99%

“…Notions a ij , q ij , and l ij are related to the designed predecessor-leader following (PLF) topology. Interested readers may refer to [23] for further details.…”

Section: E Vehicle Controlmentioning

confidence: 99%

“…Several researchers formulated this problem as a mixed integer program (MIP) problem [18], [19], [20]. Other methods, such as Monte Carlo tree search [21], dynamic programming [22], and minimum clique cover (MCC) [23], were also proposed to schedule CAVs. It is worth mentioning that because scheduling CAVs is a discrete problem rather than a continuous one, a graph-based method is another promising method to solve this problem.…”

Section: Introductionmentioning

confidence: 99%

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Cooperation Method of Connected and Automated Vehicles at Unsignalized Intersections: Lane Changing and Arrival Scheduling

Chen¹,

Cai²,

Wang³

et al. 2021

Preprint

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The cooperation of connected and automated vehicles (CAVs) has shown great potential in improving traffic efficiency during intersection management. Existing research mainly focuses on intersections where lane changing is prohibited, which is impractical for real-life implementation. This paper proposes a two-stage cooperation framework, which decouples the longitudinal and lateral control of CAVs, allowing them to change to their preferred lanes. Based on formation control, an iterative framework is initially proposed to solve the target assignment and path planning problem for multiple CAVs on multiple lanes. A graph-based minimum clique cover method is then applied to obtain the optimal scheduling plan for the CAVs. Extensive numerical simulations for different numbers of vehicles and traffic volumes validate the effectiveness of the proposed algorithm.

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Section: Minimum Clique Cover Schedulingmentioning

confidence: 96%

“…In [23], we introduced a graph-based minimum clique cover (MCC) method applied to an intersection with complex vehicle conflict relationships, as shown in Fig. 1.…”

Section: Two-stage Cooperation Frameworkmentioning

confidence: 99%

Section: Two-stage Cooperation Frameworkmentioning

confidence: 99%

“…Notions a ij , q ij , and l ij are related to the designed predecessor-leader following (PLF) topology. Interested readers may refer to [23] for further details.…”

Section: E Vehicle Controlmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Cooperation Method of Connected and Automated Vehicles at Unsignalized Intersections: Lane Changing and Arrival Scheduling

Chen¹,

Cai²,

Wang³

et al. 2021

Preprint

Self Cite

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Planning and Decision-making for Connected Autonomous Vehicles at Road Intersections: A Review

Shen

Shu

Chen

et al. 2021

Chin. J. Mech. Eng.

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Planning and decision-making technology at intersections is a comprehensive research problem in intelligent transportation systems due to the uncertainties caused by a variety of traffic participants. As wireless communication advances, vehicle infrastructure integrated algorithms designed for intersection planning and decision-making have received increasing attention. In this paper, the recent studies on the planning and decision-making technologies at intersections are primarily overviewed. The general planning and decision-making approaches are presented, which include graph-based approach, prediction base approach, optimization-based approach and machine learning based approach. Since connected autonomous vehicles (CAVs) is the future direction for the automated driving area, we summarized the evolving planning and decision-making methods based on vehicle infrastructure cooperative technologies. Both four-way signalized and unsignalized intersection(s) are investigated under purely automated driving traffic and mixed traffic. The study benefit from current strategies, protocols, and simulation tools to help researchers identify the presented approaches’ challenges and determine the research gaps, and several remaining possible research problems that need to be solved in the future.

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Multi-lane Unsignalized Intersection Cooperation with Flexible Lane Direction based on Multi-vehicle Formation Control

Cai¹,

Xu²,

Chen³

et al. 2021

Preprint

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Unsignalized intersection cooperation of connected and automated vehicles (CAVs) is able to eliminate green time loss of signalized intersections and improve traffic efficiency. Most of the existing research on unsignalized intersection cooperation considers fixed lane direction, where only specific turning behavior of vehicles is allowed on each lane. Given that traffic volume and the proportion of vehicles with different turning expectation may change with time, fixed lane direction may lead to inefficiency at intersections. This paper proposes a multi-lane unsignalized intersection cooperation method that considers flexible lane direction. The two-dimensional distribution of vehicles is calculated and vehicles that are not in conflict are scheduled to pass the intersection simultaneously. The formation reconfiguration method is utilized to achieve collision-free longitudinal and lateral position adjustment of vehicles. Simulations are conducted at different input traffic volumes and turning proportion of incoming vehicles, and the results indicate that our method outperformances the fixed-lane-direction unsignalized cooperation method and the signalized method.

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Conflict-free Cooperation Method for Connected and Automated Vehicles at Unsignalized Intersections: Graph-based Modeling and Optimality Analysis

Cited by 4 publications

References 31 publications

Cooperation Method of Connected and Automated Vehicles at Unsignalized Intersections: Lane Changing and Arrival Scheduling

Cooperation Method of Connected and Automated Vehicles at Unsignalized Intersections: Lane Changing and Arrival Scheduling

Planning and Decision-making for Connected Autonomous Vehicles at Road Intersections: A Review

Multi-lane Unsignalized Intersection Cooperation with Flexible Lane Direction based on Multi-vehicle Formation Control

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