A delayed response policy for autonomous intersection management
The DARPA Urban Challenge in 2007 showed that fully autonomous vehicles, driven by computers without human intervention on public roads, are technologically feasible with current intelligent vehicle technology . Some researchers predict that within 5-20 years there will be autonomous vehicles for sale on the automobile market. Therefore, the time is right to rethink our current transportation infrastructure, which is primarily designed for human drivers, not autonomous vehicles. The Autonomous Intersection Management (AIM) project at UT Austin aims to propose a large-scale, real-time framework to be a substitute for current traffic light and stop signs. Automobiles in modern urban settings spend a lot of time idling at intersections. In 2007, US drivers wasted 4.16 billion hours of their time and 2.81 billion gallons of gas in congestion, costing a total of 87.2 billion dollars nationwide . A big portion of this waste takes place at intersections. The AIM project is able to utilize the capacity of intersections to minimize time waste and fuel consumption. The fundamental idea of Autonomous Intersection management (AIM)  is a reservation system in which cells in space-time will be reserved by the au- tonomous vehicles based on their trajectories. An intersection manager takes care of the reservation as well as communication with the vehicles. This mechanism tries to maximize the usage of the intersection area. It ensures a collision free intersection as well. The main question of this project is what intersection control mechanism is appropriate for reducing an autonomous vehicle's waiting time and improving the throughput of the intersection. Previous work proposed the first-come-first-served (FCFS) policy in which the reservation requests are served as soon as they are received. The results of simulation show that FCFS outperforms the current traffic systems, traffic light and stop sign, by orders of magnitude. We, however, observe that FCFS performs suboptimal in certain traffic patterns that are pretty common in urban settings. In this project, first we study the limitations of FCFS, then develop a more efficient policy to alleviate these limitations. The idea that we examined is a systematic policy of granting reservations that have the objective of minimizing the cost of delaying vehicles. In an attempt to build the system in reality, we used miniature robots called Eco-be. Due to their cost and size, Eco-bes are good candidates for testing a multi-agent system with a large number of agents. In spite of the fact that the physical challenges of Eco-bes do not perfectly match those of full size autonomous vehicles, they are still useful for demonstration and education purposes as well as for the study of collisions for which experiments with full size vehicles are costly and dangerous.