Optimizing connected automated vehicle movements through roadway intersections is a challenging problem. Traditional traffic control strategies, such as traffic signals are not optimal, especially for heavy traffic. Alternatively, centralized automated vehicle control strategies are costly and not scalable given that the ability of a central controller to track and schedule the movement of hundreds of vehicles in real-time is highly questionable. In this research, a series of fully distributed heuristic algorithms are proposed where vehicles in the vicinity of an intersection continuously cooperate with each other to develop a schedule that allows them to safely proceed through the intersection while incurring minimum delays. An algorithm is proposed for the case of an isolated intersection then a number of algorithms are proposed for a network of intersections where neighboring intersections communicate directly or indirectly to help the distributed control at each intersection makes a better estimation of traffic in the whole network. An algorithm based on the Godunov scheme outperformed optimized signalized control. The simulated experiments show significant reductions in the average delay. The base algorithm is successfully added to the INTEGRATION micro-simulation model and the results demonstrate improvements in delay, fuel consumption, and emissions when compared to roundabout, signalized, and stop sign controlled intersections. The study also shows the capability of the proposed technique to favor emergency vehicles, producing significant increases in mobility with minimum delays to the other vehicles in the network. / Ph. D. / Intelligent self-driving cars are getting much closer to reality than fiction. Technological advances make it feasible to produce such vehicles at low affordable cost. This type of vehicles is also promising to significantly reduce car accidents saving people lives and health. Moreover, the congested roads in cities and metropolitan areas especially at rush hours can benefit from this technology to avoid or at least to reduce the delays experienced by car passengers during their trips. One major challenge facing the operation of an intelligent self-driving car is how to pass an intersection as fast as possible without any collision with cars approaching from other directions of the intersection. The use of current traffic lights or stop signs is not the best choice to make the best use of the capabilities of future cars. In this dissertation, the aim is to study and propose ways to make sure the future intersections are ready for such self-driving intelligent cars. Assuming that an intersection has no type of traditional controls such as traffic lights or stop signs, this research effort shows how vehicles can pass safely with minimum waiting. The proposed techniques focus on providing lowcost solutions that do not require installation of expensive devices at intersections that makes it difficult to be approved by authorities. The proposed techniques can be applied to intersections of various sizes. The algorithms in this dissertation carefully design a way for vehicles in a network of intersections to communicate and cooperate while passing an intersection. The algorithms are extensively compared to the case of using traffic lights, stop signs, and roundabouts. Results show significant improvement in delay reduction and fuel consumption when the proposed techniques are used.

This study explores the application of multi-agent reinforcement learning (MARL) to enhance the decision-making, safety, and passenger comfort of Autonomous Vehicles (AVs)at uncontrolled intersections. The research aims to assess the potential of MARL in modeling multiple agents interacting within a shared environment, reflecting real-world situations where AVs interact with multiple actors. The findings suggest that AVs trained using aMARL approach with global experiences can better navigate intersection scenarios than AVs trained on local (individual) experiences. This capability is a critical precursor to achieving Level 5 autonomy, where vehicles are expected to manage all aspects of the driving task under all conditions. The research contributes to the ongoing discourse on enhancing autonomous vehicle technology through multi-agent reinforcement learning and informs the development of sophisticated training methodologies for autonomous driving.