Print Email Facebook Twitter Driver Profile and Driving Pattern Recognition for Road Safety Assessment Title Driver Profile and Driving Pattern Recognition for Road Safety Assessment: Main Challenges and Future Directions Author Tselentis, D. (TU Delft Safety and Security Science) Papadimitriou, E. (TU Delft Safety and Security Science) Date 2023 Abstract This study reviews the Artificial Intelligence and Machine Learning approaches developed thus far for driver profile and driving pattern recognition, representing a set of macroscopic and microscopic behaviors respectively, to enhance the understanding of human factors in road safety, and therefore reduce the number of crashes. It provides a definition of the two scientific fields in terms of safety, and identifies the most efficient approaches used regarding methodology, data collection and driving metrics. Results show that K-means and Neural Networks are the most commonly used methodologies for driver profile identification, and Dynamic Time Warping for driving pattern detection. Most studies discovered driver profiles related to aggressiveness, considering mainly speed and acceleration as driving metrics. Based on the gaps and challenges identified, this paper provides a new framework for combining microscopic and macroscopic driving behavior analysis, instead of examining them separately as is the state-of-theart. Such combined results can potentially improve the development of traffic risk models, which could be exploited in applications that monitor drivers in real-time and provide feedback. These models will represent human behavior more accurately, which can eventually lead to the recognition of 'optimal' human driving patterns that Automated Vehicles (AV) could 'mimic' to become safer. Subject Artificial IntelligenceBehavioral sciencesDriver ProfilesDriving BehaviorDriving PatternsMachine LearningMeasurementMicroscopyNaturalistic Driving DataPattern recognitionRoad safetySafetyVehicles To reference this document use: http://resolver.tudelft.nl/uuid:023f770e-5e15-4e86-90fd-32720849c974 DOI https://doi.org/10.1109/OJITS.2023.3237177 ISSN 2687-7813 Source IEEE Open Journal of Intelligent Transportation Systems, 4, 83-100 Part of collection Institutional Repository Document type journal article Rights © 2023 D. Tselentis, E. Papadimitriou Files PDF Driver_Profile_and_Drivin ... ctions.pdf 2.76 MB Close viewer /islandora/object/uuid:023f770e-5e15-4e86-90fd-32720849c974/datastream/OBJ/view