Data Augmentation of IMU Signals and Evaluation via a Semi-Supervised Classification of Driving Behavior
(2020) 23rd IEEE International Conference on Intelligent Transportation Systems, ITSC 2020 In 2020 IEEE 23rd International Conference on Intelligent Transportation Systems, ITSC 2020- Abstract
Over the past years, interest in classifying drivers' behavior from data has surged. Such interest is particularly relevant for car insurance companies who, due to privacy constraints, often only have access to data from Inertial Measurement Units (IMU) or similar. In this paper, we present a semi-supervised learning solution to classify portions of trips according to whether drivers are driving aggressively or normally based on such IMU data. Since the amount of labeled IMU data is limited and costly to generate, we utilize Recurrent Conditional Generative Adversarial Networks (RCGAN) to generate more labeled data. Our results show that, by utilizing RCGAN-generated labeled data, the classification of the drivers is improved in 79% of... (More)
Over the past years, interest in classifying drivers' behavior from data has surged. Such interest is particularly relevant for car insurance companies who, due to privacy constraints, often only have access to data from Inertial Measurement Units (IMU) or similar. In this paper, we present a semi-supervised learning solution to classify portions of trips according to whether drivers are driving aggressively or normally based on such IMU data. Since the amount of labeled IMU data is limited and costly to generate, we utilize Recurrent Conditional Generative Adversarial Networks (RCGAN) to generate more labeled data. Our results show that, by utilizing RCGAN-generated labeled data, the classification of the drivers is improved in 79% of the cases, compared to when the drivers are classified with no generated data.
(Less)
- author
- Jaafer, Amani ; Nilsson, Gustav and Como, Giacomo LU
- organization
- publishing date
- 2020
- type
- Chapter in Book/Report/Conference proceeding
- publication status
- published
- subject
- keywords
- data evaluation, data generation, driving behaviors, IMU sensor
- host publication
- 2020 IEEE 23rd International Conference on Intelligent Transportation Systems, ITSC 2020
- series title
- 2020 IEEE 23rd International Conference on Intelligent Transportation Systems, ITSC 2020
- article number
- 9294496
- publisher
- IEEE - Institute of Electrical and Electronics Engineers Inc.
- conference name
- 23rd IEEE International Conference on Intelligent Transportation Systems, ITSC 2020
- conference location
- Rhodes, Greece
- conference dates
- 2020-09-20 - 2020-09-23
- external identifiers
-
- scopus:85099661398
- ISBN
- 9781728141497
- DOI
- 10.1109/ITSC45102.2020.9294496
- language
- English
- LU publication?
- yes
- id
- 9c6f4c12-bec9-4318-8785-0fc4f2594372
- date added to LUP
- 2021-02-05 09:15:45
- date last changed
- 2022-04-27 00:01:22
@inproceedings{9c6f4c12-bec9-4318-8785-0fc4f2594372, abstract = {{<p>Over the past years, interest in classifying drivers' behavior from data has surged. Such interest is particularly relevant for car insurance companies who, due to privacy constraints, often only have access to data from Inertial Measurement Units (IMU) or similar. In this paper, we present a semi-supervised learning solution to classify portions of trips according to whether drivers are driving aggressively or normally based on such IMU data. Since the amount of labeled IMU data is limited and costly to generate, we utilize Recurrent Conditional Generative Adversarial Networks (RCGAN) to generate more labeled data. Our results show that, by utilizing RCGAN-generated labeled data, the classification of the drivers is improved in 79% of the cases, compared to when the drivers are classified with no generated data.</p>}}, author = {{Jaafer, Amani and Nilsson, Gustav and Como, Giacomo}}, booktitle = {{2020 IEEE 23rd International Conference on Intelligent Transportation Systems, ITSC 2020}}, isbn = {{9781728141497}}, keywords = {{data evaluation; data generation; driving behaviors; IMU sensor}}, language = {{eng}}, publisher = {{IEEE - Institute of Electrical and Electronics Engineers Inc.}}, series = {{2020 IEEE 23rd International Conference on Intelligent Transportation Systems, ITSC 2020}}, title = {{Data Augmentation of IMU Signals and Evaluation via a Semi-Supervised Classification of Driving Behavior}}, url = {{http://dx.doi.org/10.1109/ITSC45102.2020.9294496}}, doi = {{10.1109/ITSC45102.2020.9294496}}, year = {{2020}}, }