Intersection Control and Delay Optimization for Autonomous Vehicles Flows Only as Well as Mixed Flows with Ordinary Vehicles

Abdullah Baz,Ahmad Qurashi,Ping Yi

doi:10.3390/vehicles2030029

Abstract

The rapidly improving autonomous vehicle (AV) technology will have a significant impact on traffic safety and efficiency. This study introduces a game-theory-based priority control algorithm for autonomous vehicles to improve intersection safety and efficiency with mixed traffic. By using vehicle-to-infrastructure (V2I) communications, this model allows an AV to exchange information with the roadside units (RSU) to support the decision making of whether an ordinary vehicle (OV) or an AV should pass the intersection first. The safety of vehicles is taken in different stages of decisions to assure collision-free intersection operations. Two different mathematical models have been developed, where model one is for an AV/AV situation and model two is when an AV meets an OV. A simulation model was developed to implement the algorithm and compare the performance of each model with the conventional traffic control at a four-legged signalized intersection and at a roundabout. Three levels of traffic volume and speed combinations were tested in the simulation. The results show significant reductions in delay for both cases; for case (I), AV/AV model, a 65% reduction compared to a roundabout and 84% compared to a four-legged signalized intersection, and for case (II), AV/OV model, the reduction is 30% and 89%, respectively.

Highlights

Autonomous vehicles (AVs) are driverless vehicles that can communicate with other systems and make driving decisions for themselves [1]
Each simulation lasts for fifteen minutes (900 sec) for the AV/AV
A total of twenty-seven tests were performed for each of the two cases, in which each volume~speed combination was performed three times to obtain an average for presentation

Summary

Introduction

Autonomous vehicles (AVs) are driverless vehicles that can communicate with other systems and make driving decisions for themselves [1]. For decades we have used traffic signals to regulate traffic flow going through intersections according to designated signal cycles, phases and intervals This method of intersection control has proven to be inefficient with capacity constraint, mainly because of the need to separate conflicting vehicle trajectories and adjust signal timing to accommodate the required reaction time of human drivers. I.H., Kamalanathsharma, R.K. and Rakha, H. in [8] proposed a new tool to control vehicle trajectories using cooperative adaptive cruise control (CACC) systems to avoid collisions They used an optimization model to minimize the waiting time if there is a conflict by dividing the intersection into three zones, in the first zone to get all AVs to their max speed, second zone for adjusting speed if needed and go back to the maximum to pass the third zone. The last section summarizes the positive results of the study on intersection improvement when it is compared with other methods of intersection control

Game Theory

Conflict

Desired Speed for the Following Vehicles

Simulation, Testing, and Results

Conclusions