End-to-End Learning Driver Policy using Moments Deep Neural Network

Qian, Deheng; Ren, Dongchun; Meng, Ying‐Ying; Zhu, Y.; Ding, Shuguang; Fu, Sheng; Wang, Zhichao; Xia, Huaxia

doi:10.1109/robio.2018.8664869

Cited by 2 publications

(1 citation statement)

References 11 publications

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“…The data were recorded around Rancho Palos Verdes and San Pedro, California, using a 2014 Honda Civic. A first version of the dataset has been used by Qian, Ren, Meng, Zhu, Ding, Fu, Wang, and Xia (2018) on their work. Fig.…”

Section: Methodsmentioning

confidence: 99%

Bayesian Optimization and Deep Learning forsteering wheel angle prediction

Riboni¹,

Ghioldi²,

Candelieri³

et al. 2021

Preprint

View full text Add to dashboard Cite

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 word 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.

show abstract

Section: Methodsmentioning

confidence: 99%

Bayesian Optimization and Deep Learning forsteering wheel angle prediction

Riboni¹,

Ghioldi²,

Candelieri³

et al. 2021

Preprint

View full text Add to dashboard Cite

show abstract

Bayesian optimization and deep learning for steering wheel angle prediction

Riboni

Ghioldi

Candelieri

et al. 2022

Sci Rep

View full text Add to dashboard Cite

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.

show abstract

End-to-End Learning Driver Policy using Moments Deep Neural Network

Cited by 2 publications

References 11 publications

Bayesian Optimization and Deep Learning forsteering wheel angle prediction

Bayesian Optimization and Deep Learning forsteering wheel angle prediction

Bayesian optimization and deep learning for steering wheel angle prediction

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