Journal
IEEE SENSORS LETTERS
Volume 3, Issue 11, Pages -Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/LSENS.2019.2945117
Keywords
Sensor signal processing; sensor applications; driving maneuvers; feature selection; random forest
Funding
- 111 Project
- Fundamental Research Funds for the Central Universities [JUSRP11924]
- Jiangsu Key Laboratory of Advanced Food Manufacturing Equipment and Technology [FM-2019-06]
- National Natural Science Foundation of China [61902154]
- Natural Science Foundation of Jiangsu Province [BK2019043526]
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Driving behavior classification is highly correlated with vehicle accidents and injury. Automatically recognizing different driving behaviors is important for improving road safety. This article proposes a maneuver-based driving behavior classification system. For each driving maneuver, we first generate driving behavior events based on its given timestamp using three different strategies. Then, 19 temporal features of each behavior event are calculated using signals captured by accelerometers, gyroscopes, and GPS. Next, reliefF is incorporated for selecting features. Finally, random forest is used for classifying maneuver-based driving behaviors. Experimental results using the UAH-DirveSet show that our proposed system can achieve an averaged F1-score of 70.47% using leave-one-driver-out validation. For different maneuvers, we find that the highest F1-score is obtained for braking which is 75.38%.
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