The DOI matches its Crossref record; 4 of the 4 references it cites were found in a registry or an index.
Driver classification and driving style recognition using inertial sensors
Abstract
Currently there are many research focused on using smartphone as a data collection device. Many have shown its sensors ability to replace a lab test bed. These inertial sensors can be used to segment and classify driving events fairly accurately. In this research we explore the possibility of using the vehicle's inertial sensors from the CAN bus to build a profile of the driver to ultimately provide proper feedback to reduce the number of dangerous car maneuver. Braking and turning events are better at characterizing an individual compared to acceleration events. Histogramming the time-series values of the sensor data does not help performance. Furthermore, combining turning and braking events helps better differentiate between two similar drivers when using supervised learning techniques compared to separate events alone, albeit with anemic performance.
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Cites
LIBSVM: A library for support vector machines
Chang, C.-C., & Lin, C.-J. (2011) · ACM Transactions on Intelligent Systems and Technology · doi:10.1145/1961189.1961199 · Cited by 40,918
Driving style recognition using a smartphone as a sensor platform
Johnson, D. A., & Trivedi, M. M. (2011) · 2011 14th International IEEE Conference on Intelligent Transportation Systems (ITSC) · doi:10.1109/itsc.2011.6083078 · Cited by 727
Self-citationOn-road prediction of driver's intent with multimodal sensory cues
Doshi, A., Morris, B. T., & Trivedi, M. M. (2011) · IEEE Pervasive Computing · doi:10.1109/mprv.2011.38 · Cited by 110
Self-citationDoshi, A., & Trivedi, M. M. (2010) · 2010 IEEE Intelligent Vehicles Symposium · doi:10.1109/ivs.2010.5547969 · Cited by 77
Self-citation
Cited by
Driving Style Recognition for Intelligent Vehicle Control and Advanced Driver Assistance: A Survey
Marina Martinez, C., Heucke, M., Wang, F.-Y., Gao, B., & Cao, D. (2018) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2017.2706978 · Cited by 620
Driving Style Classification Using a Semisupervised Support Vector Machine
Wang, W., Xi, J., Chong, A., & Li, L. (2017) · IEEE Transactions on Human-Machine Systems · doi:10.1109/thms.2017.2736948 · Cited by 265
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
Driving style classification using long-term accelerometer information
Vaitkus, V., Lengvenis, P., & Zylius, G. (2014) · 2014 19th International Conference on Methods and Models in Automation and Robotics (MMAR) · doi:10.1109/mmar.2014.6957429 · Cited by 144
Machine Learning for Security in Vehicular Networks: A Comprehensive Survey
Talpur, A., & Gurusamy, M. (2022) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2021.3129079 · Cited by 107
Metadata
- Authors
- Van Ly, M., Martin, S., & Trivedi, M. M.
- Venue
- 2013 IEEE Intelligent Vehicles Symposium (IV)
- Publisher
- IEEE
- Published
- 2013-06
- Pages
- 1040-1045
- ISSN
- 1931-0587 (ISSN-L)
- OpenAlex
- W2083863819
- Cited by
- 306
- References
- 4
- FWCI
- 28.25
- Type
- conference paper
- Language
- en
- Open access
- closed
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- Van Ly, M., Martin, S., & Trivedi, M. M. (2013). Driver classification and driving style recognition using inertial sensors. 2013 IEEE Intelligent Vehicles Symposium (IV). https://doi.org/10.1109/ivs.2013.6629603
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