Decentralized Model Predictive Control for Automated and Connected Electric Vehicles at Signal-free Intersections

Abstract

The development of connected and automated vehicles (CAVs) enables improvements in the safety, smoothness, and energy efficiency of the road transportation systems. This paper addresses the problem of optimally controlling batteryelectric CAVs crossing an unsignalized intersection subject to a first-in-first-out crossing policy. The optimal velocity trajectory of each vehicle that minimizes the average energy consumption and travel time, is found by a decentralized model predictive control (DMPC) method via a convex modeling framework so as to ensure computational efficiency and the optimality of the solution. Numerical examples and comparisons with a centralized control counterpart demonstrate the effectiveness of the proposed decentralized coordination scheme and the trade-off between energy consumption and travel time. Further investigation into the size of the sampling interval is also provided in order to show the validity of the method in practice.

Autonomous vehicle scheduling at signal-free intersections based on deep reinforcement learning

“…(4) (5) where is the spacing between the preceding and following AVs, is the distance from the following AV to the stop line at the intersection, is the distance from the preceding AV to the stop line at the intersection, represents the threshold of the time gap, and is the velocity of the following AV.…”

“…Mixed integer linear programming (MILP) is usually employed to formulate an optimization problem aimed at orchestrating the traffic flow through signal-free intersections in reference [4]. Similarly, X. Pan et al adopted a decentralized model predictive control (DMPC) approach to find optimal speed trajectory for each vehicle, focusing on minimizing of energy consumption and travel time [5]. However, these optimization-based techniques face challenges in handling the exponential growth of feasible solutions as the number of vehicles considered increases, making modeling and computation demanding.…”