A Universal Cooperative Decision-Making Framework for Connected Autonomous Vehicles With Generic Road Topologies

Cooperative decision-making of Connected Autonomous Vehicles (CAVs) presents a longstanding challenge due to its inherent nonlinearity, non-convexity, and discrete characteristics, compounded by the diverse road topologies encountered in real-world traffic scenarios. The majority of current methodol...

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Vydané v:	IEEE transactions on vehicular technology Ročník 74; číslo 4; s. 5414 - 5429
Hlavní autori:	Huang, Zhenmin, Liu, Wenru, Shen, Shaojie, Ma, Jun
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	New York IEEE 01.04.2025 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:	Autonomous driving Autonomous vehicles connected autonomous vehicles (CAVs) Convexity cooperative decision-making Decision making Games Graph theory Graphical representations Integer programming Linear programming Merging Mixed integer Mixed integer linear programming mixed-integer linear programming (MILP) multi-agent systems non-convex optimization Optimization Roads Roads & highways Topology Trajectory Trajectory planning
ISSN:	0018-9545, 1939-9359
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Abstract	Cooperative decision-making of Connected Autonomous Vehicles (CAVs) presents a longstanding challenge due to its inherent nonlinearity, non-convexity, and discrete characteristics, compounded by the diverse road topologies encountered in real-world traffic scenarios. The majority of current methodologies are only applicable to a single and specific scenario, predicated on scenario-specific assumptions. Consequently, their application in real-world environments is restricted by the innumerable nature of traffic scenarios. In this study, we propose a unified optimization approach that exhibits the potential to address cooperative decision-making problems related to traffic scenarios with generic road topologies. This development is grounded in the premise that the topologies of various traffic scenarios can be universally represented as Directed Acyclic Graphs (DAGs). Particularly, the reference paths and time profiles for all involved CAVs are determined in a fully cooperative manner, taking into account factors such as velocities, accelerations, conflict resolutions, and overall traffic efficiency. The cooperative decision-making of CAVs is approximated as a mixed-integer linear programming (MILP) problem building on the DAGs of road topologies. This favorably facilitates the use of standard numerical solvers and the global optimality can be attained through the optimization. Case studies corresponding to different multi-lane traffic scenarios featuring diverse topologies are scheduled as the test itineraries, and the efficacy of our proposed methodology is corroborated.
AbstractList	Cooperative decision-making of Connected Autonomous Vehicles (CAVs) presents a longstanding challenge due to its inherent nonlinearity, non-convexity, and discrete characteristics, compounded by the diverse road topologies encountered in real-world traffic scenarios. The majority of current methodologies are only applicable to a single and specific scenario, predicated on scenario-specific assumptions. Consequently, their application in real-world environments is restricted by the innumerable nature of traffic scenarios. In this study, we propose a unified optimization approach that exhibits the potential to address cooperative decision-making problems related to traffic scenarios with generic road topologies. This development is grounded in the premise that the topologies of various traffic scenarios can be universally represented as Directed Acyclic Graphs (DAGs). Particularly, the reference paths and time profiles for all involved CAVs are determined in a fully cooperative manner, taking into account factors such as velocities, accelerations, conflict resolutions, and overall traffic efficiency. The cooperative decision-making of CAVs is approximated as a mixed-integer linear programming (MILP) problem building on the DAGs of road topologies. This favorably facilitates the use of standard numerical solvers and the global optimality can be attained through the optimization. Case studies corresponding to different multi-lane traffic scenarios featuring diverse topologies are scheduled as the test itineraries, and the efficacy of our proposed methodology is corroborated.
Author	Liu, Wenru Shen, Shaojie Huang, Zhenmin Ma, Jun
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SubjectTerms	Autonomous driving Autonomous vehicles connected autonomous vehicles (CAVs) Convexity cooperative decision-making Decision making Games Graph theory Graphical representations Integer programming Linear programming Merging Mixed integer Mixed integer linear programming mixed-integer linear programming (MILP) multi-agent systems non-convex optimization Optimization Roads Roads & highways Topology Trajectory Trajectory planning
Title	A Universal Cooperative Decision-Making Framework for Connected Autonomous Vehicles With Generic Road Topologies
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