Crossroads: Time-Sensitive Autonomous Intersection Management Technique

For autonomous vehicles, intelligent autonomous intersection management will be required for safe and efficient operation. In order to achieve safe operation despite uncertainties in ve-hicle trajectory, intersection management techniques must consider a safety buffer around the vehicles. For truly safe operation, an extra buffer space should be added to account for the network and computational delay caused by com-munication with the Intersection Manager (IM). However, modeling the worst-case computation and network delay as additional buffer around the vehicle degrades the through-put of the intersection. To avoid this problem, AIM[1], a popular state-of-The-Art IM, adopts a query-based approach in which the vehicle requests to enter at a certain arrival time dictated by its current velocity and distance to the intersection, and the IM replies yes/no. Although this solu-tion does not degrade the position uncertainty, it ultimately results in poor intersection throughput. We present Cross-roads, a time-sensitive programming method to program the interface of a vehicle and the IM. Without requiring addi-tional buffer to account for the effect of network and compu-tational delay, Crossroads enables efficient intersection man-agement. Test results on a 1=10 scale model of intersection using TRAXXAS RC cars demonstrates that our Crossroads approach obviates the need for large buffers to accommo-date for the network and computation delay, and can re-duce the average wait time for the vehicles at a single-lane intersection by 24%. To compare Crossroads with previ-ous approaches, we perform extensive Matlab simulations, and find that Crossroads achieves on average 1.62X higher throughput than a simple VT-IM with extra safety buffer, and 1.36X better than AIM.