Collision Avoidance in Tightly-Constrained Environments without Coordination: a Hierarchical Control Approach
This addresses safety and efficiency for autonomous vehicles in complex, uncoordinated traffic scenarios, representing an incremental improvement over existing methods.
The paper tackles collision avoidance for autonomous vehicles in tightly-constrained environments by proposing a hierarchical control approach with a data-driven strategy predictor and model-based controller, demonstrating effectiveness in simulations and experiments where it outperforms a baseline model predictive control method.
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.