  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.
