Safe, Out-of-Distribution-Adaptive MPC with Conformalized Neural Network Ensembles

TL;DR

SODA-MPC is a novel control framework combining neural network ensembles and conformal prediction to adaptively switch between learned prediction and reachability-based safety control in autonomous driving, ensuring safety in out-of-distribution scenarios.

Contribution

The paper introduces SODA-MPC, integrating OOD detection with conformal prediction and a fallback reachability controller for safe, adaptive autonomous vehicle control.

Findings

Improved safety and task completion over existing MPC methods.

Effective OOD detection with probabilistic guarantees.

Successful validation on real pedestrian data and large-scale traffic datasets.

Abstract

We present SODA-MPC, a Safe, Out-of-Distribution-Adaptive Model Predictive Control algorithm, which uses an ensemble of learned models for prediction, with a runtime monitor to flag unreliable out-of-distribution (OOD) predictions. When an OOD situation is detected, SODA-MPC triggers a safe fallback control strategy based on reachability, yielding a control framework that achieves the high performance of learning-based models while preserving the safety of reachability-based control. We demonstrate the method in the context of an autonomous vehicle, driving among dynamic pedestrians, where SODA-MPC uses a neural network ensemble for pedestrian prediction. We calibrate the OOD signal using conformal prediction to derive an OOD detector with probabilistic guarantees on the false-positive rate, given a user-specified confidence level. During in-distribution operation, the MPC controller avoids collisions with a pedestrian based on the trajectory predicted by the mean of the ensemble. When OOD conditions are detected, the MPC switches to a reachability-based controller to avoid collisions with the reachable set of the pedestrian assuming a maximum pedestrian speed, to guarantee safety under the worst-case actions of the pedestrian. We verify SODA-MPC in extensive autonomous driving simulations in a pedestrian-crossing scenario. Our model ensemble is trained and calibrated with real pedestrian data, showing that our OOD detector obtains the desired accuracy rate within a theoretically-predicted range. We empirically show improved safety and improved task completion compared with two state-of-the-art MPC methods that also use conformal prediction, but without OOD adaptation. Further, we demonstrate the effectiveness of our method with the large-scale multi-agent predictor Trajectron++, using large-scale traffic data from the nuScenes dataset for training and calibration.