Details
| Original language | English |
|---|---|
| Title of host publication | 2022 IEEE 34th International Conference on Tools with Artificial Intelligence, ICTAI 2022 |
| Editors | Marek Reformat, Du Zhang, Nikolaos G. Bourbakis |
| Pages | 766-773 |
| Number of pages | 8 |
| ISBN (electronic) | 9798350397444 |
| Publication status | Published - 2022 |
| Event | 34th IEEE International Conference on Tools with Artificial Intelligence, ICTAI 2022 - Virtual, Online, China Duration: 31 Oct 2022 → 2 Nov 2022 |
Publication series
| Name | Proceedings - International Conference on Tools with Artificial Intelligence, ICTAI |
|---|---|
| Volume | 2022-October |
| ISSN (Print) | 1082-3409 |
Abstract
Recent studies have shown the potential of leveraging neural networks to achieve high levels of accuracy in re-identifying drivers by learning latent features from vehicular sensor data. However, deploying such networks in real-world applications (like theft detection or fleet management) requires re-training the networks with new data to transfer the learnings from the initial dataset to the target drivers. In this paper, we highlight the importance of the evaluation of such networks in both phases, initial training and transfer learning. Our evaluation shows that the performance of existing solutions drops significantly, when applied to new drivers that have not been seen by the networks in the initial training phase. Moreover, we propose a deep neural network that outperforms state-of-the-art solutions in both phases. For the evaluation of the transfer learning phase, we use a dataset from a real-world ride-sharing service that has not been used in the initial training.
Keywords
- driver identification, neural networks, transfer learning
ASJC Scopus subject areas
- Computer Science(all)
- Software
- Computer Science(all)
- Artificial Intelligence
- Computer Science(all)
- Computer Science Applications
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2022 IEEE 34th International Conference on Tools with Artificial Intelligence, ICTAI 2022. ed. / Marek Reformat; Du Zhang; Nikolaos G. Bourbakis. 2022. p. 766-773 (Proceedings - International Conference on Tools with Artificial Intelligence, ICTAI; Vol. 2022-October).
Research output: Chapter in book/report/conference proceeding › Conference contribution › Research › peer review
}
TY - GEN
T1 - Towards building reliable deep learning based driver identification systems
AU - Zeng, Li
AU - Al-Rifai, Mohammad
AU - Nolting, Michael
AU - Nejdl, Wolfgang
N1 - Funding Information: The first author has received funding from Volkswagen AG to conduct this research.
PY - 2022
Y1 - 2022
N2 - Recent studies have shown the potential of leveraging neural networks to achieve high levels of accuracy in re-identifying drivers by learning latent features from vehicular sensor data. However, deploying such networks in real-world applications (like theft detection or fleet management) requires re-training the networks with new data to transfer the learnings from the initial dataset to the target drivers. In this paper, we highlight the importance of the evaluation of such networks in both phases, initial training and transfer learning. Our evaluation shows that the performance of existing solutions drops significantly, when applied to new drivers that have not been seen by the networks in the initial training phase. Moreover, we propose a deep neural network that outperforms state-of-the-art solutions in both phases. For the evaluation of the transfer learning phase, we use a dataset from a real-world ride-sharing service that has not been used in the initial training.
AB - Recent studies have shown the potential of leveraging neural networks to achieve high levels of accuracy in re-identifying drivers by learning latent features from vehicular sensor data. However, deploying such networks in real-world applications (like theft detection or fleet management) requires re-training the networks with new data to transfer the learnings from the initial dataset to the target drivers. In this paper, we highlight the importance of the evaluation of such networks in both phases, initial training and transfer learning. Our evaluation shows that the performance of existing solutions drops significantly, when applied to new drivers that have not been seen by the networks in the initial training phase. Moreover, we propose a deep neural network that outperforms state-of-the-art solutions in both phases. For the evaluation of the transfer learning phase, we use a dataset from a real-world ride-sharing service that has not been used in the initial training.
KW - driver identification
KW - neural networks
KW - transfer learning
UR - http://www.scopus.com/inward/record.url?scp=85156114525&partnerID=8YFLogxK
U2 - 10.1109/ICTAI56018.2022.00118
DO - 10.1109/ICTAI56018.2022.00118
M3 - Conference contribution
AN - SCOPUS:85156114525
SN - 979-8-3503-9745-1
T3 - Proceedings - International Conference on Tools with Artificial Intelligence, ICTAI
SP - 766
EP - 773
BT - 2022 IEEE 34th International Conference on Tools with Artificial Intelligence, ICTAI 2022
A2 - Reformat, Marek
A2 - Zhang, Du
A2 - Bourbakis, Nikolaos G.
T2 - 34th IEEE International Conference on Tools with Artificial Intelligence, ICTAI 2022
Y2 - 31 October 2022 through 2 November 2022
ER -