Conference Papers
Permanent URI for this collectionhttps://idr.nitk.ac.in/handle/123456789/28506
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Item Classification of Longitudinal Driving Events Using Vehicle Response Signals for Profiling Driving Behaviors(Springer Science and Business Media Deutschland GmbH, 2025) Arichandran, R.; Krishnakar, H.S.; Kumar, A.; Mohan, M.Driving behavior profiling (DBP) involves evaluating driving patterns to determine a safety score for drivers. Proper classification of driving events increases the accuracy of profiling driving behaviors. Most driving event classification models consider lateral driving events, such as turning and lane changes. In heavy traffic conditions, it is impossible to perform lateral events independent of the position of other vehicles. This research aims to develop a model for classifying longitudinal driving events (acceleration and braking) and nonevents using vehicle response signals. To develop the model, naturalistic driving data were collected using a passenger car on a 19 km road stretch. Vehicle response signals were collected using Inertial Measurement Unit (IMU) sensors fixed on the test vehicle with a frequency of approximately 200 Hz with timestamps. The driver’s pedal operation was also captured with timestamps using a camera to map the ground truth labels with vehicle response signals. The data were collected from 5 drivers, totaling a dataset for approximately 190 km. The start and end times of all 634 events (444 driving events and 190 nonevents) were used to label the driving events in the IMU sensor data. These labeled driving events were split into the train (476 events) and test (158 events) datasets. Hidden Markov Model (HMM) algorithm was used to develop classification models for the driving events. The models were developed for various combinations of accelerations using the training dataset. The accuracy of these models was then compared to a test dataset. The models achieved 90.99% and 77.08% accuracy, respectively, in classifying events and nonevents using data from the accelerometer. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.