Bayesian Optimization and Deep Learning forsteering wheel angle prediction

TL;DR

This paper employs Bayesian Optimization to efficiently tune hyperparameters of a deep learning model, significantly improving steering angle prediction accuracy in automated driving systems.

Contribution

It introduces a BO-based hyperparameter tuning approach for ST-LSTM networks, achieving superior performance over classical models in ADS steering prediction.

Findings

BO identified the most accurate model within limited trials

BOST-LSTM outperformed classical end-to-end models

Efficient hyperparameter tuning reduces computational costs

Abstract

Automated driving systems (ADS) have undergone a significant improvement in the last years. ADS and more precisely self-driving cars technologies will change the way we perceive and know the world of transportation systems in terms of user experience, mode choices and business models. The emerging field of Deep Learning (DL) has been successfully applied for the development of innovative ADS solutions. However, the attempt to single out the best deep neural network architecture and tuning its hyperparameters are all expensive processes, both in terms of time and computational resources. In this work, Bayesian Optimization (BO) is used to optimize the hyperparameters of a Spatiotemporal-Long Short Term Memory (ST-LSTM) network with the aim to obtain an accurate model for the prediction of the steering angle in a ADS. BO was able to identify, within a limited number of trials, a model -- namely BOST-LSTM -- which resulted, on a public dataset, the most accurate when compared to classical end-to-end driving models.