Comprehensive Performance Assessment of Various NN-based Side-Slip Angle Estimators (ANN-SSE)

Abstract

This paper addresses the problem of sensors lack that can directly measure the side-slip angle of a vehicle which is crucial in controlling autonomous vehicles. Therefore, estimating the side-slip angle is considered to be the most sufficient and cheapest solution. The main aim is to compare the performance of different Neural Network NN-based estimators through comprehensive performance assessment criteria. This can be done if a proper architecture is designed to allow the adaptation of the estimator to suit any tested data. Four NN approaches are investigated in this work; Feed-Forward Neural Networks (FFNN), Recurrent Neural Networks (RNN), Long Short-Term Memory (LSTM) units, and Gated Recurrent Units (GRU). The performance of the approaches is compared to measure the system accuracy represented by Root Mean Square Error and the error variance. Additionally, the computational effort of the architecture is evaluated in terms of the training time and the estimation time. Moreover, the ability of the architecture adaptation to any uncertainty in the validation data is addressed by tuning the network hyper-parameters. IPG CarMaker aided the evaluation process, by providing different roads and cars, that nearly replicates real-life conditions. The tested data show promising results in terms of accuracy, computation effort, and the robustness of the architecture. Regarding the performance assessment, it is shown that the FFNN achieves higher accuracy compared to the NN with recurrence property. Meanwhile, the GRUs surpassed the FFNN in terms of mean training time.