Bayesian optimization and deep learning for steering wheel angle prediction
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 e...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2022-05-01
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| Series: | Scientific Reports |
| Online Access: | https://doi.org/10.1038/s41598-022-12509-6 |
| _version_ | 1811254239731646464 |
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| author | Alessandro Riboni Nicolò Ghioldi Antonio Candelieri Matteo Borrotti |
| author_facet | Alessandro Riboni Nicolò Ghioldi Antonio Candelieri Matteo Borrotti |
| author_sort | Alessandro Riboni |
| collection | DOAJ |
| description | 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 BO_ST-LSTM—which resulted, on a public dataset, the most accurate when compared to classical end-to-end driving models. |
| first_indexed | 2024-04-12T17:03:29Z |
| format | Article |
| id | doaj.art-62413137abe944aba5c5ce26d3b94206 |
| institution | Directory Open Access Journal |
| issn | 2045-2322 |
| language | English |
| last_indexed | 2024-04-12T17:03:29Z |
| publishDate | 2022-05-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj.art-62413137abe944aba5c5ce26d3b942062022-12-22T03:24:00ZengNature PortfolioScientific Reports2045-23222022-05-0112111210.1038/s41598-022-12509-6Bayesian optimization and deep learning for steering wheel angle predictionAlessandro Riboni0Nicolò Ghioldi1Antonio Candelieri2Matteo Borrotti3Department of Economics, Management and Statistics, University of Milano-BicoccaDepartment of Economics, Management and Statistics, University of Milano-BicoccaDepartment of Economics, Management and Statistics, University of Milano-BicoccaDepartment of Economics, Management and Statistics, University of Milano-BicoccaAbstract 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 BO_ST-LSTM—which resulted, on a public dataset, the most accurate when compared to classical end-to-end driving models.https://doi.org/10.1038/s41598-022-12509-6 |
| spellingShingle | Alessandro Riboni Nicolò Ghioldi Antonio Candelieri Matteo Borrotti Bayesian optimization and deep learning for steering wheel angle prediction Scientific Reports |
| title | Bayesian optimization and deep learning for steering wheel angle prediction |
| title_full | Bayesian optimization and deep learning for steering wheel angle prediction |
| title_fullStr | Bayesian optimization and deep learning for steering wheel angle prediction |
| title_full_unstemmed | Bayesian optimization and deep learning for steering wheel angle prediction |
| title_short | Bayesian optimization and deep learning for steering wheel angle prediction |
| title_sort | bayesian optimization and deep learning for steering wheel angle prediction |
| url | https://doi.org/10.1038/s41598-022-12509-6 |
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