Feature and Extrapolation Aware Uncertainty Quantification for AI-based State Estimation in Automated Driving

Abstract

State-estimation is an integral method for automated driving as the need for more measurement data for vehicle control increases, despite them not always being directly measurable. In the field of state estimation, AI-based algorithms are increasingly attracting interest. However, an uncertainty measure is pivotal to use AI-based state estimation for safetycritical applications. This paper presents the implementation of a vehicle state estimator based on a recurrent neural network and a novel method for uncertainty quantification. The uncertainty quantification method comprises the sequential evaluation of four parts: feature importance algorithms to remove input features lacking informative value, novelty detection filtering data beyond the range of the training data, and prediction of an uncertainty measure and confidence interval with Monte Carlo dropout. The performance of the proposed approach is demonstrated using AI-based state estimation of the vehicle sideslip angle based on the simulation data from a nonlinear two-track model. The results achieved imply that the novel method can provide a reliable confidence interval and successfully identify cases where the estimation and uncertainty quantification are not trustworthy.