End-to-End Deep Neural Network Architectures for Speed and Steering Wheel Angle Prediction in Autonomous Driving

Navarro, Pedro J.; Miller, Leanne; Rosique, Francisca; Fernández-Isla, Carlos; Gila-Navarro, Alberto

doi:10.3390/electronics10111266

Cited by 18 publications

(14 citation statements)

References 39 publications

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“…Finally, the most conclusive validation presented in this document was performed, the usability analysis of the data contained in the final dataset. The work by Navarro et al [18] presents a mixed data input end-to-end model which used the front images obtained by the vehicle and angular speeds to predict the speed and steering wheel angle with a mean error of 1.06%. An exhaustive optimization process of the convolutional blocks has demonstrated that it is possible to design lightweight end-to-end architectures with a high performance more suitable for the final implementation in autonomous driving.…”

Section: Experimental Validationmentioning

confidence: 99%

“…As stated in the work by [17], this method of offering already labelled and even segmented data often presents problems in data quality due to the methods or models used. Another disadvantage of those models trained using only synthetic datasets is that in real world scenarios these tend to perform poorly, suffering from domain shift [18], [19].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Autonomous Vehicle Dataset with Real Multi-Driver Scenes and Biometric Data

Rosique¹,

Navarro²,

Miller³

et al. 2023

Preprint

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The development of autonomous vehicles is becoming increasingly popular and gathering real world data is considered a valuable task. Many datasets have been published recently in the autonomous vehicle sector, with synthetic datasets gaining particular interest due to availability and cost. For a real implementation and correct evaluation of vehicles at higher levels of autonomy it is also necessary to consider human interaction, which is precisely something that lacks in existing datasets. In this article the UPCT dataset is presented, a public dataset containing high quality, multimodal data obtained using state of the art sensors and equipment installed onboard the UPCT’s CICar autonomous vehicle. The dataset includes data from a variety of perception sensors including 3D LiDAR, cameras, IMU, GPS, encoders, as well as driver biometric data and driver behaviour questionnaires. In addition to the dataset, the software developed for data synchronisation and processing has been made available. The quality of the dataset was validated using an end-to-end neural network model with multiple inputs to obtain speed and steering wheel angle and obtained very promising results.

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Section: Experimental Validationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Autonomous Vehicle Dataset with Real Multi-Driver Scenes and Biometric Data

Rosique¹,

Navarro²,

Miller³

et al. 2023

Preprint

View full text Add to dashboard Cite

show abstract

“…These networks have obtained excellent results in the detection, classification, and segmentation of images. Furthermore, CNNs can be used to solve regression problems simply by modifying the activation functions of the last layers [ 12 ].…”

Section: Contextmentioning

confidence: 99%

A novel ground truth multispectral image dataset with weight, anthocyanins, and Brix index measures of grape berries tested for its utility in machine learning pipelines

et al. 2022

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Background The combination of computer vision devices such as multispectral cameras coupled with artificial intelligence has provided a major leap forward in image-based analysis of biological processes. Supervised artificial intelligence algorithms require large ground truth image datasets for model training, which allows to validate or refute research hypotheses and to carry out comparisons between models. However, public datasets of images are scarce and ground truth images are surprisingly few considering the numbers required for training algorithms. Results We created a dataset of 1,283 multidimensional arrays, using berries from five different grape varieties. Each array has 37 images of wavelengths between 488.38 and 952.76 nm obtained from single berries. Coupled to each multispectral image, we added a dataset with measurements including, weight, anthocyanin content, and Brix index for each independent grape. Thus, the images have paired measures, creating a ground truth dataset. We tested the dataset with 2 neural network algorithms: multilayer perceptron (MLP) and 3-dimensional convolutional neural network (3D-CNN). A perfect (100% accuracy) classification model was fit with either the MLP or 3D-CNN algorithms. Conclusions This is the first public dataset of grape ground truth multispectral images. Associated with each multispectral image, there are measures of the weight, anthocyanins, and Brix index. The dataset should be useful to develop deep learning algorithms for classification, dimensionality reduction, regression, and prediction analysis.

show abstract

“…As stated in the work by [ 19 ], this method of offering already labelled and even segmented data often presents problems in data quality due to the methods or models used. Another disadvantage of those models trained using only synthetic datasets is that in real-world scenarios, these tend to perform poorly, suffering from domain shift [ 20 , 21 ].…”

Section: Introductionmentioning

confidence: 99%

Autonomous Vehicle Dataset with Real Multi-Driver Scenes and Biometric Data

Rosique

Navarro

Miller

et al. 2023

Sensors

Self Cite

View full text Add to dashboard Cite

The development of autonomous vehicles is becoming increasingly popular and gathering real-world data is considered a valuable task. Many datasets have been published recently in the autonomous vehicle sector, with synthetic datasets gaining particular interest due to availability and cost. For a real implementation and correct evaluation of vehicles at higher levels of autonomy, it is also necessary to consider human interaction, which is precisely something that lacks in existing datasets. In this article the UPCT dataset is presented, a public dataset containing high quality, multimodal data obtained using state-of-the-art sensors and equipment installed onboard the UPCT’s CICar autonomous vehicle. The dataset includes data from a variety of perception sensors including 3D LiDAR, cameras, IMU, GPS, encoders, as well as driver biometric data and driver behaviour questionnaires. In addition to the dataset, the software developed for data synchronisation and processing has been made available. The quality of the dataset was validated using an end-to-end neural network model with multiple inputs to obtain the speed and steering wheel angle and it obtained very promising results.

show abstract

End-to-End Deep Neural Network Architectures for Speed and Steering Wheel Angle Prediction in Autonomous Driving

Cited by 18 publications

References 39 publications

Autonomous Vehicle Dataset with Real Multi-Driver Scenes and Biometric Data

Autonomous Vehicle Dataset with Real Multi-Driver Scenes and Biometric Data

A novel ground truth multispectral image dataset with weight, anthocyanins, and Brix index measures of grape berries tested for its utility in machine learning pipelines

Autonomous Vehicle Dataset with Real Multi-Driver Scenes and Biometric Data

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