Equalized Coverage in Motion Control Performance Prediction for Self-Adaptive Road Vehicles

TL;DR

This paper presents a conformalized quantile regression model for predicting vehicle control capabilities under actuator failures, employing equalized coverage to ensure reliable performance across different operational regimes.

Contribution

It introduces a lightweight, statistically guaranteed prediction model that accounts for regime-specific variations in vehicle control performance during degradation.

Findings

The model accurately predicts control feasibility under various actuator conditions.

Equalized coverage improves reliability of predictions across different regimes.

The approach provides a heuristic for safe action space determination during vehicle operation.

Abstract

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.