Faculty Publications
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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.Item Classification of Lateral Driving Events Using Vehicle Response Signals for Profiling Driving Behaviors(Springer Science and Business Media Deutschland GmbH, 2025) Arichandran, R.; Kumar, A.; Krishnakar, H.S.; Mohan, M.Classification of driving events is crucial in profiling driving behaviors, which could significantly increase road safety. In several studies classifying driving events, drivers were asked to perform hard turns and lane changes. However, these represented simulated situations much different from real-life scenarios. The main aim of this study was to classify lateral driving events (turn) and non-events from naturalistic driving data in actual driving conditions. A stretch of 8 km state highway was identified as the study road, and the data were collected using 8 drivers. The acceleration and gyroscope data were collected using Inertial Measurement Unit (IMU) sensors with a frequency of approximately 200 Hz with the timestamps. A dashboard camera was fixed to capture the driver’s view with timestamps. The start and end times of the turns (left turn and right turn) and non-events were manually marked using the timestamps in the recorded videos. The total count of marked events and non-events was 1246, and their start and end times were used to label the driving events in the IMU sensor data. These labeled driving events were split into the train (934 driving events) and test (312 driving events) datasets. The Hidden Markov Model (HMM) algorithm was adopted to create classification models for the driving events. HMM models were developed using the training dataset for various features, such as lateral and longitudinal acceleration. The accuracy of these models was then compared to a test dataset. The models achieved 96.09% and 95.1% accuracy in classifying turns and non-events using data from the gyroscope’s y-axis. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.Item Assessment of Safety Orientation in Driving Skills Aligned With Performance: A Data-Triangulation Approach(Lund University Faculty of Engineering, 2024) Arichandran, R.; Mohan, M.; Sreekumar, M.Accurate assessment of Subjective Driving Skills (SDS) is crucial for improving road safety, as direct methods are often biased and do not align well with actual driving performance. This study aimed to develop an unbiased SDS assessment method aligned with driving performance. The specific objectives are (1) reducing bias in SDS assessments, (2) verifying alignment between assessed safety orientation and ground driving performance, and (3) exploring the influence of socio-demographic factors on safety orientation. A combined questionnaire and photographic speed survey were conducted among 389 experienced car drivers in Mangalore, India. Factor analysis, a Double Lane Change (DLC) test conducted on the ground with a test vehicle equipped with Inertial Measurement Unit (IMU) sensors, correlation analysis and multiple linear regression were performed. Factor analysis confirmed the two-factor structure: Perceptual-Motor (PM) and safety skills. Further, PM and safety skills scores were calculated using factor loadings, and safety orientation was determined from their difference. DLC results showed that the assessed safety orientation aligned with the ground performance. Correlation and regression analyses showed that male drivers perceived slightly higher PM skills than female drivers. PM skills decreased with age, while safety orientation increased. Academic education had no significant effect on safety skills or safety orientation. While on-road exposure improved PM skills, weekly driving distance reduced safety orientation. Formally trained drivers had slightly higher safety skills and safety orientation than lay-instructed drivers. These findings provide several valuable insights for enhancing road safety. They suggest that safety programs address overconfidence in male drivers, incorporate road safety awareness into educational curriculums, and offer enhanced training for all experienced drivers. Younger drivers may benefit from targeted safety campaigns, while professional drivers could require specialised safety programs. Regular safety assessments and refresher courses are crucial for maintaining safety awareness, particularly for drivers with higher weekly driving distances. © 2024, Lund University Faculty of Engineering. All rights reserved.