Collision Avoidance in Tightly-Constrained Environments without Coordination: a Hierarchical Control Approach
Xu Shen, Edward L. Zhu, Yvonne R. Stürz, Francesco Borrelli
Abstract
We present a hierarchical control approach for maneuvering an autonomous vehicle (AV) in tightly-constrained environments where other moving AVs and/or human driven vehicles are present. A two-level hierarchy is proposed: a high-level data-driven strategy predictor and a lower-level model-based feedback controller. The strategy predictor maps an encoding of a dynamic environment to a set of high-level strategies via a neural network. Depending on the selected strategy, a set of time-varying hyperplanes in the AV’s position space is generated online and the corresponding halfspace constraints are included in a lower-level model-based receding horizon controller. These strategy-dependent constraints drive the vehicle towards areas where it is likely to remain feasible. Moreover, the predicted strategy also informs switching between a discrete set of policies, which allows for more conservative behavior when prediction confidence is low. We demonstrate the effectiveness of the proposed data-driven hierarchical control framework in a two-car collision avoidance scenario through simulations and experiments on a 1/10 scale autonomous car platform where the strategy-guided approach outperforms a model predictive control baseline in both cases.
BibTeX
@inproceedings{icra2021_collisionavoidan,
title = {Collision Avoidance in Tightly-Constrained Environments without Coordination: a Hierarchical Control Approach},
author = {Xu Shen and Edward L. Zhu and Yvonne R. Stürz and Francesco Borrelli},
booktitle = {ICRA 2021},
year = {2021}
}