On the Importance of Contextual Information for Building Reliable Automated Driver Identification Systems

Publikation: Beitrag in Buch/Bericht/Sammelwerk/KonferenzbandAufsatz in KonferenzbandForschungPeer-Review

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  • Volkswagen AG
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OriginalspracheEnglisch
Titel des Sammelwerks2020 IEEE 23rd International Conference on Intelligent Transportation Systems, ITSC 2020
Herausgeber (Verlag)Institute of Electrical and Electronics Engineers Inc.
ISBN (elektronisch)9781728141497
ISBN (Print)978-1-7281-4150-3
PublikationsstatusVeröffentlicht - 2020
Veranstaltung23rd IEEE International Conference on Intelligent Transportation Systems, ITSC 2020 - Rhodes, Griechenland
Dauer: 20 Sept. 202023 Sept. 2020

Abstract

Recent studies on machine learning based driver identification have shown that leveraging deep neural networks to learn latent features from vehicle sensor data boosts the performance of the models to high levels of accuracies. However, models produced by deep neural networks are difficult to explain and the interpretability of their results is limited. Consequently, the reliability of the learned models heavily depends on the amount, quality and diversity of the training dataset as well as on the validation scenarios. In this work, we evaluate state-of-the-art deep learning networks for driver identification using a very rich dataset of more than 395, 000 kilometres of different driving scenarios and environmental conditions (e.g. route, vehicle, traffic, weather) collected over two years. It turns out that the neural networks achieve high accuracy levels when training and testing on the same type of driving conditions. However, accuracy drops and importance of individual signals varies when testing on different driving conditions, although all best practices like stratification and cross validation have been applied. Our findings suggest that relying solely on the vehicle sensor data without taking the contextual information about driving conditions into account is not a practical solution.

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On the Importance of Contextual Information for Building Reliable Automated Driver Identification Systems. / Zeng, Li; Al-Rifai, Mohammad; Chelaru, Sergiu et al.
2020 IEEE 23rd International Conference on Intelligent Transportation Systems, ITSC 2020. Institute of Electrical and Electronics Engineers Inc., 2020. 9294439.

Publikation: Beitrag in Buch/Bericht/Sammelwerk/KonferenzbandAufsatz in KonferenzbandForschungPeer-Review

Zeng, L, Al-Rifai, M, Chelaru, S, Nolting, M & Nejdl, W 2020, On the Importance of Contextual Information for Building Reliable Automated Driver Identification Systems. in 2020 IEEE 23rd International Conference on Intelligent Transportation Systems, ITSC 2020., 9294439, Institute of Electrical and Electronics Engineers Inc., 23rd IEEE International Conference on Intelligent Transportation Systems, ITSC 2020, Rhodes, Griechenland, 20 Sept. 2020. https://doi.org/10.1109/ITSC45102.2020.9294439
Zeng, L., Al-Rifai, M., Chelaru, S., Nolting, M., & Nejdl, W. (2020). On the Importance of Contextual Information for Building Reliable Automated Driver Identification Systems. In 2020 IEEE 23rd International Conference on Intelligent Transportation Systems, ITSC 2020 Artikel 9294439 Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ITSC45102.2020.9294439
Zeng L, Al-Rifai M, Chelaru S, Nolting M, Nejdl W. On the Importance of Contextual Information for Building Reliable Automated Driver Identification Systems. in 2020 IEEE 23rd International Conference on Intelligent Transportation Systems, ITSC 2020. Institute of Electrical and Electronics Engineers Inc. 2020. 9294439 doi: 10.1109/ITSC45102.2020.9294439
Zeng, Li ; Al-Rifai, Mohammad ; Chelaru, Sergiu et al. / On the Importance of Contextual Information for Building Reliable Automated Driver Identification Systems. 2020 IEEE 23rd International Conference on Intelligent Transportation Systems, ITSC 2020. Institute of Electrical and Electronics Engineers Inc., 2020.
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abstract = "Recent studies on machine learning based driver identification have shown that leveraging deep neural networks to learn latent features from vehicle sensor data boosts the performance of the models to high levels of accuracies. However, models produced by deep neural networks are difficult to explain and the interpretability of their results is limited. Consequently, the reliability of the learned models heavily depends on the amount, quality and diversity of the training dataset as well as on the validation scenarios. In this work, we evaluate state-of-the-art deep learning networks for driver identification using a very rich dataset of more than 395, 000 kilometres of different driving scenarios and environmental conditions (e.g. route, vehicle, traffic, weather) collected over two years. It turns out that the neural networks achieve high accuracy levels when training and testing on the same type of driving conditions. However, accuracy drops and importance of individual signals varies when testing on different driving conditions, although all best practices like stratification and cross validation have been applied. Our findings suggest that relying solely on the vehicle sensor data without taking the contextual information about driving conditions into account is not a practical solution.",
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note = "Funding Information: ACKNOWLEDGMENT The authors affiliated with Volkswagen AG have received funding from Volkswagen AG to conduct this research.; 23rd IEEE International Conference on Intelligent Transportation Systems, ITSC 2020 ; Conference date: 20-09-2020 Through 23-09-2020",
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AB - Recent studies on machine learning based driver identification have shown that leveraging deep neural networks to learn latent features from vehicle sensor data boosts the performance of the models to high levels of accuracies. However, models produced by deep neural networks are difficult to explain and the interpretability of their results is limited. Consequently, the reliability of the learned models heavily depends on the amount, quality and diversity of the training dataset as well as on the validation scenarios. In this work, we evaluate state-of-the-art deep learning networks for driver identification using a very rich dataset of more than 395, 000 kilometres of different driving scenarios and environmental conditions (e.g. route, vehicle, traffic, weather) collected over two years. It turns out that the neural networks achieve high accuracy levels when training and testing on the same type of driving conditions. However, accuracy drops and importance of individual signals varies when testing on different driving conditions, although all best practices like stratification and cross validation have been applied. Our findings suggest that relying solely on the vehicle sensor data without taking the contextual information about driving conditions into account is not a practical solution.

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