Data-driven Strategy-guided Control

Collision Avoidance in Tightly-Constrained Environments without Coordination: a Hierarchical Control Approach (ICRA 2021)

1. 1. We present a hierarchical control approach for maneuvering an autonomous vehicle (AV) in a tightly-constrained environment where other moving AVs and/or human-driven vehicles are present. 

   2. 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 a high-dimensional environment encoding into a set of high-level strategies. Our approach uses data collected on an offline simulator to train a neural network model as the strategy predictor. Depending on the online selected strategy, a set of time-varying hyperplanes in the AV's motion space is generated and included in the lower-level control. The latter is a Strategy-Guided Optimization-Based Collision Avoidance (SG-OBCA) algorithm where the strategy-dependent hyperplane constraints are used to drive a model-based receding horizon controller towards a predicted feasible area. The strategy also informs switching from the SG-OBCA control policy to a safety or emergency control policy. 

   3. We demonstrate the effectiveness of the proposed data-driven hierarchical control framework in 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.

Collision Avoidance in Tightly-Constrained Environments without Coordination: a Hierarchical Control Approach

Xu Shen*, Edward L. Zhu*, Yvonne R. Stürz, Francesco Borrelli 

Model Predictive Control Lab

University of California, Berkeley

ICRA 2021

* equal contribution

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