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

TL;DR

A unified optimization approach is proposed that exhibits the potential to address cooperative decision-making problems related to traffic scenarios with generic road topologies and favorably facilitates the use of standard numerical solvers and the global optimality can be attained through the optimization.

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.

Figures (11)

• Figure 1: An illustrative example of extended waypoint graphs. Different styles of arrows represent edges for different driving purposes, including straight traveling, lane switching, etc. Those edges are treated indiscriminately in the proposed methodology.
• Figure 2: An illustrative example of critical edge pairs. Collision may occur when CAV $i$ moves along edge $e^i$ and CAV $j$ moves along edge $e^j$ at the same time.
• Figure 3: An illustrative example of critical regions within a critical edge pair. Collision may occur when both CAVs enter the corresponding critical region at the same time.
• Figure 4: Projection of motion of CAV $j$ onto edge $e^i$.
• Figure 5: t-s curves of different cases and conditions.

Theorems & Definitions (3)

• Definition 1
• Definition 2
• Definition 3