A multiagent approach to autonomous intersection management
Journal of Artificial Intelligence Research
| Hi-index | 0.01 |
The recent robot car competitions and demonstrations have convincingly shown that fully autonomous vehicles are feasible with current or near-future intelligent vehicle technology. Looking ahead to the time when such autonomous cars will be common, Dresner and Stone proposed a new intersection control protocol called Autonomous Intersection Management (AIM) and showed that by leveraging the capacities of autonomous vehicles we can devise a reservation-based intersection control protocol that is much more efficient than traffic signals and stop signs. Their proposed protocol, however, handles reservation requests one at a time and does not prioritize reservations according to their relative importance and vehicles' waiting times, causing potentially large inequalities in granting reservations. For example, at an intersection between a main street and an alley, vehicles from the alley can take a very long time to get reservations to enter the intersection. In this research, we introduce a prioritization scheme to prevent uneven reservation assignments in unbalanced traffic. Our experimental results show that our prioritizing scheme outperforms previous intersection control protocols in unbalanced traffic.