Lane-free Autonomous Intersection Management: A Batch-processing Framework Integrating Reservation-based and Planning-based Methods
Bai Li, Youmin Zhang, Tankut Acarman, Yakun Ouyang, Cagdas Yaman, Yaonan Wang
Abstract
Autonomous intersection management (AIM) refers to planning the trajectories for multiple connected and automated vehicles (CAVs) when they traverse an unsignalized intersection cooperatively. As an extension of the conventional AIM, lane-free AIM allows the CAVs to adjust their velocities and paths flexibly within the intersection. Nominally, one needs to formulate a centralized optimal control problem (OCP) to describe the concerned lane-free AIM scheme, but solving such an intractably scaled problem is challenging. This work proposes a batch-processing framework, which divides the traffic flow into batches. The cooperative trajectories within one batch are planned by numerically solving a small-scale OCP; all the batches are managed via a reservation-based method following the first-come-first-serve policy. The proposed batch-processing framework aims to run as fast as a reservation-based method at the macro level while taking care of the cooperative driving quality at the micro level. The proposed method is validated via simulation and preliminary experiments.
BibTeX
@inproceedings{icra2021_lanefreeautonomo,
title = {Lane-free Autonomous Intersection Management: A Batch-processing Framework Integrating Reservation-based and Planning-based Methods},
author = {Bai Li and Youmin Zhang and Tankut Acarman and Yakun Ouyang and Cagdas Yaman and Yaonan Wang},
booktitle = {ICRA 2021},
year = {2021}
}