# Bayesian Optimization and Deep Learning forsteering wheel angle prediction

arXiv: https://arxiv.org/abs/2110.13629 · HTML: https://mlsanity.com/paper/2110.13629/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 58 references that are in the map.
- First posted on arXiv 22 Oct 2021.

## Builds on (most cited first, 14 of 14)

- [ImageNet Large Scale Visual Recognition Challenge](https://mlsanity.com/paper/1409.0575/) (2014)
- [Convolutional LSTM Network: A Machine Learning Approach for Precipitation Nowcasting](https://mlsanity.com/paper/1506.04214/) (2015)
- [AutoML: A Survey of the State-of-the-Art](https://mlsanity.com/paper/1908.00709/) (2019)
- [A Survey of Autonomous Driving: Common Practices and Emerging Technologies](https://mlsanity.com/paper/1906.05113/) (2019)
- [LSTM: A Search Space Odyssey](https://mlsanity.com/paper/1503.04069/) (2015)
- [BANANAS: Bayesian Optimization with Neural Architectures for Neural Architecture Search](https://mlsanity.com/paper/1910.11858/) (2019)
- [A Survey on Evolutionary Neural Architecture Search](https://mlsanity.com/paper/2008.10937/) (2020)
- [Interpretable Learning for Self-Driving Cars by Visualizing Causal Attention](https://mlsanity.com/paper/1703.10631/) (2017)
- [CurveLane-NAS: Unifying Lane-Sensitive Architecture Search and Adaptive Point Blending](https://mlsanity.com/paper/2007.12147/) (2020)
- [Explaining How a Deep Neural Network Trained with End-to-End Learning Steers a Car](https://mlsanity.com/paper/1704.07911/) (2017)
- [Predictive Entropy Search for Bayesian Optimization with Unknown Constraints](https://mlsanity.com/paper/1502.05312/) (2015)
- [Benchmark and Survey of Automated Machine Learning Frameworks](https://mlsanity.com/paper/1904.12054/) (2019)
- [Deep Neural Architecture Search with Deep Graph Bayesian Optimization](https://mlsanity.com/paper/1905.06159/) (2019)
- [Deep Learning Based Motion Planning For Autonomous Vehicle Using Spatiotemporal LSTM Network](https://mlsanity.com/paper/1903.01712/) (2019)

## Built on by (most cited first, 2 of 2)

- [Memory based neural networks for end-to-end autonomous driving](https://mlsanity.com/paper/2205.12124/) (2022)
- [Dense Optimizer : An Information Entropy-Guided Structural Search Method for Dense-like Neural Network Design](https://mlsanity.com/paper/2410.07499/) (2024)

## Abstract (arXiv)

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.