The DOI matches its Crossref record; 32 of the 32 references it cites were found in a registry or an index.
Driver behavior profiling: An investigation with different smartphone sensors and machine learning
Open accessAbstract
Driver behavior impacts traffic safety, fuel/energy consumption and gas emissions. Driver behavior profiling tries to understand and positively impact driver behavior. Usually driver behavior profiling tasks involve automated collection of driving data and application of computer models to generate a classification that characterizes the driver aggressiveness profile. Different sensors and classification methods have been employed in this task, however, low-cost solutions and high performance are still research targets. This paper presents an investigation with different Android smartphone sensors, and classification algorithms in order to assess which sensor/method assembly enables classification with higher performance. The results show that specific combinations of sensors and intelligent methods allow classification performance improvement.
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Cited by
Machine learning for next-generation intelligent transportation systems: A survey
Yuan, T., Da Rocha Neto, W., Rothenberg, C. E., Obraczka, K., Barakat, C., & Turletti, T. (2022) · Transactions on Emerging Telecommunications Technologies · doi:10.1002/ett.4427 · Cited by 179
Methodology and Mobile Application for Driver Behavior Analysis and Accident Prevention
Kashevnik, A., Lashkov, I., & Gurtov, A. (2020) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2019.2918328 · Cited by 139
Wearable Sensor-Based Human Activity Recognition with Hybrid Deep Learning Model
Luwe, Y. J., Lee, C. P., & Lim, K. M. (2022) · Informatics · doi:10.3390/informatics9030056 · Cited by 92
Gamified Mobile Applications for Improving Driving Behavior: A Systematic Mapping Study
El hafidy, A., Rachad, T., Idri, A., & Zellou, A. (2021) · Mobile Information Systems · doi:10.1155/2021/6677075 · Cited by 21
Metadata
- Authors
- Ferreira, J., Carvalho, E., Ferreira, B. V., de Souza, C., Suhara, Y., Pentland, A., & Pessin, G.
- Venue
- PLOS ONE
- Publisher
- Public Library of Science (PLoS)
- Published
- 2017-04-10
- Volume
- 12
- Issue
- 4
- Pages
- e0174959
- ISSN
- 1932-6203 (ISSN-L)
- OpenAlex
- W2605751614
- Cited by
- 258
- References
- 32
- FWCI
- 40.49
- Type
- journal article
- Language
- en
- Open access
- gold
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
