Equalized Coverage in Motion Control Performance Prediction for Self-Adaptive Road Vehicles
For developers of self-adaptive road vehicles, this work provides a method to ensure statistical guarantees across different operational regimes, improving the reliability of capability monitoring under degraded conditions.
This paper proposes a lightweight prediction model based on conformalized quantile regression with equalized coverage to predict whether an automated vehicle can maintain low lateral deviation under actuator degradations and failures, addressing conditional coverage gaps in motion control performance prediction.
Automated driving systems require monitoring mechanisms to ensure operation as intended, especially when system elements degrade and/or fail. Hence, capability monitoring is crucial in order to evaluate the system's remaining performance and implement capability-based behavior. In this paper, we investigate the dynamics of a highly over-actuated automated vehicle under actuator degradations and failures, affecting the vehicle's motion control capabilities. We propose a lightweight prediction model based on conformalized quantile regression that predicts whether an automated vehicle can be controlled with sufficiently low lateral deviation from a planned trajectory under nominal, degraded, and failed actuator conditions. We recognize that statistical guarantees should hold not only across all data (marginal coverage) but also for different regimes within the data (conditional coverage). We therefore employ equalized coverage methods to address this challenge. During runtime behavior generation our predictor can provide a heuristic for determining the admissible action space. Its application and limitations are discussed in this paper.