Toward edge-computing-enabled collision-free scheduling management for autonomous vehicles at unsignalized intersections

Abstract

With the support of Vehicle-to-Everything (V2X) technology and computing power networks, the existing intersection traffic order is expected to benefit from efficiency improvements and energy savings by new schemes such as de-signalization. How to effectively manage autonomous vehicles for traffic control with high throughput at unsignalized intersections while ensuring safety has been a research hotspot. This paper proposes a collision-free autonomous vehicle scheduling framework based on edge-cloud computing power networks for unsignalized intersections where the lanes entering the intersections are undirectional, and designs an efficient communication system and protocol. First, by analyzing the collision point occupation time, this paper formulates an absolute value programming problem. Second, this problem is solved with low complexity by the Edge Intelligence Optimal Entry Time (EI-OET) algorithm based on edge-cloud computing power support. Then, the communication system and protocol are designed for the proposed scheduling scheme to realize efficient and low-latency vehicular communications. Finally, simulation experiments compare the proposed scheduling framework with directional and traditional traffic light scheduling mechanisms, and the experimental results demonstrate its high efficiency, low latency, and low complexity.