Cooperative Autonomous Vehicle Speed Optimization near Signalized Intersections

TL;DR

This paper presents a game-theoretic cooperative framework for autonomous vehicles to optimize speed near signalized intersections, reducing idling times and stops through vehicle cooperation.

Contribution

It introduces a novel cooperative speed optimization framework with modules for individual optimization, conflict recognition, and cooperative decision making, utilizing a time token allocation algorithm.

Findings

Significant reduction in vehicle idling times in simulations

Decreased number of stops at intersections

Effective cooperation among autonomous vehicles

Abstract

Road congestion in urban environments, especially near signalized intersections, has been a major cause of significant fuel and time waste. Various solutions have been proposed to solve the problem of increasing idling times and number of stops of vehicles at signalized intersections, ranging from infrastructure to vehicle-based techniques. However, all the solutions introduced to solve the problem have approached the problem from a single vehicle point of view. This research introduces a game-theoretic cooperative speed optimization framework to minimize vehicles' idling times and number of stops at signalized intersections. This framework consists of three modules to cover individual autonomous vehicle speed optimization; conflict recognition; and cooperative speed decision making. A time token allocation algorithm is introduced through the proposed framework to allow the vehicles to cooperate and agree on certain speed actions such that the average idling times and number of stops are minimized. Simulation to test and validate the proposed framework is conducted and results are reported.