Periodic and Event-Triggering for Joint Capacity Maximization and Safe Intersection Crossing

TL;DR

This paper introduces a novel framework for intersection management of connected autonomous vehicles that maximizes capacity and safety by using periodic and event-triggered control strategies, accounting for location uncertainty.

Contribution

It proposes AVOID-PERIOD and AVOID-EVENT frameworks that improve intersection throughput and safety while reducing computational and communication overhead.

Findings

AVOID-EVENT reduces re-optimizations by 92.2%.

The frameworks account for location uncertainty.

They enhance intersection capacity and safety.

Abstract

Intersection crossing represents a bottleneck for transportation systems and Connected Autonomous Vehicles (CAVs) may be the groundbreaking solution to the problem. This work proposes a novel framework, i.e, AVOID-PERIOD, where an Intersection Manager (IM) controls CAVs approaching an intersection in order to maximize intersection capacity while minimizing the CAVs' gas consumption. Contrary to most of the works in the literature, the CAVs' location uncertainty is accounted for and periodic communication and re-optimization allows for the creation of safe trajectories for the CAVs. To improve scalability for high-traffic intersections, an event-triggering approach is also developed (AVOID-EVENT) that minimizes computational and communication complexity. AVOID-EVENT reduces the number of re-optimizations required by 92.2%, while retaining most of the benefits introduced by AVOID-PERIOD.