An Optimal Coordination Framework for Connected and Automated Vehicles in two Interconnected Intersections

Abstract

In this paper, we provide a decentralized optimal control framework for coordinating connected and automated vehicles (CAVs) in two interconnected intersections. We formulate a control problem and provide a solution that can be implemented in real time. The solution yields the optimal acceleration/deceleration of each CAV under the safety constraint at "conflict zones," where there is a chance of potential collision. Our objective is to minimize travel time for each CAV. If no such solution exists, then each CAV solves an energy-optimal control problem. We evaluate the effectiveness of the efficiency of the proposed framework through simulation.

Distributed Cooperative Driving in Multi-Intersection Road Networks

“…As for the scenarios where the adjacent conflict areas are within the reliable communication range of one control unit [28], the proposed strategy can also deal with the trajectory planning problem. In such case, as shown in Fig.…”

“…More seriously, as pointed out in [26], it may generate the causality cycles in the process of planning trajectory for vehicles, where the planning results of the vehicles around different conflict areas affect each other mutually so that it leads to failures when each vehicle plans its ultimately optimal trajectory [26]. In such case, a few studies directly deal with all vehicles in the road network and formulate a large-scale planning problem, where each conflict between vehicles introduces a binary variable to mathematically describe vehicle sequence at conflict areas [27], [28]. It leads to high computational complexity and makes the centralized planning problem intractable.…”

An energy-optimal framework for assignment and trajectory generation in teams of autonomous agents

“…To address this, one may impose a priority ordering on the agents. This has been done previously through a centralized controller; see Turpin et al (2013a), Chalaki and Malikopoulos (2019). In general, finding an optimal ordering is generally NP-Hard and an optimal ordering is not always guaranteed to exist; see Ma et al (2019).…”

Optimal Coordination of Platoons of Connected and Automated Vehicles at Signal-Free Intersections

“…The approaches presented in [20] and [21] designed a least restrictive supervisor to determine set of control actions for the vehicles to safely cross the intersection. An optimal control framework presented in [22] aimed at coordinating CAVs at two interconnected intersections. The research effort derived the optimal schedule for CAVs and presented a closed-form analytical solution to derive optimal control input for vehicles at intersections.…”