Driver Skill Profiling Using Machine Learning

Abstract

Road safety is a critical aspect of public safety, and driving skills are essential to ensuring safety on the road. An accurate understanding of one's driving abilities is crucial in promoting safe driving practices and reducing the risk of accidents. Overconfidence and underestimating road events can lead to a false sense of handling emergencies and may result in a higher risk of traffic offenses and accidents. Young and novice drivers are particularly susceptible to these issues and may overestimate their abilities, leading to a higher risk tolerance. Machine learning is a viable approach that can compare perceived and actual skills to measure subjective driving skills accurately. A scoring system based on machine learning algorithms can quantify driver skills effectively and improve self-awareness, ultimately contributing to increased road safety. The proposed scoring system can give drivers an accurate assessment of their abilities, helping them take necessary corrective actions to work on their weaknesses. Driving style, encompassing violations, errors, and lapses, and driving skills, including perceptual motor skills and safety skills, are the two main components of the human factor in driving. Training sessions may be conducted based on the proposed scoring system using machine learning that can help improve drivers' self-awareness and reduce the risk of accidents. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.