Details

Autor(en) / Beteiligte

• Zhang, Yixiao
• Hao, Rui
• Zhang, Tingting
• Chang, Xiaohan
• Xie, Zepeng
• Zhang, Qinyu

Titel

A Trajectory Optimization-Based Intersection Coordination Framework for Cooperative Autonomous Vehicles

Ist Teil von

• IEEE transactions on intelligent transportation systems, 2022-09, Vol.23 (9), p.14674-14688

Ort / Verlag

IEEE

Erscheinungsjahr

2022

Quelle

IEEE Xplore

Beschreibungen/Notizen

• Since vehicles from multiple roads frequently merge at intersections, it formulates a typical traffic bottleneck of modern transportation systems. Proper vehicle coordination and motion plan at road intersections are of importance to guarantee safety as well as improving the traffic throughput, fuel efficiency and so on. In this paper, we try to present a general dedicated intersection coordination framework for autonomous vehicles, where both high- and low-level planners are appropriately designed and integrated. In the high-level planner, two different strategies are formulated to coordinate the autonomous vehicles to generate reference trajectories and feasible "tunnels ", respectively. Especially, a novel space-time-block based resource allocation scheme is presented to describe the feasible tunnels. Furthermore, to avoid collisions with unexpected obstacles such as pedestrians, bicycles or other vehicles with human drivers, a low-level planner is designed to generate practical trajectories based on the solutions from the high-level planner, according to their local on-board observations. Simulations and practical experiments are carried out, to show that our proposed coordination framework can achieve obvious performance advantages in various traffic metrics, including the throughput, fairness in driving maneuvers and driving comfort, etc. We also find that the high-level planner is effective in eliminating possible deadlocks among autonomous vehicles, which is rarely discussed in existing investigations.