Transition patterns of driving style from a traditional driving environment to a connected vehicle environment: A case of an extra-long tunnel road |
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| Affiliation: | 1. Civil Aviation University of China, Tianjin 300300, PR China;2. Beijing University of Technology, Beijing 100124, PR China;1. School of Engineering, RMIT University, Australia;2. Metro Trains Melbourne, Australia;1. TU Delft University of Technology, the Netherlands;2. SWOV Institute for Road Safety Research, The Hague, the Netherlands;3. TNO Traffic & Transport, The Hague, the Netherlands;4. Eindhoven University of Technology, the Netherlands;1. Academy of Professional Studies Sumadija, Department in Kragujevac, Kosovska 8, 34000 Kragujevac, Serbia;2. University of Belgrade, Faculty of Transport and Traffic Engineering, Vojvode Stepe 305, 11000 Belgrade, Serbia;3. P.E. GSP Belgrade, Knjeginje Ljubice 29, 11000 Belgrade, Serbia;4. University of Montenegro, Faculty of Mechanical Engineering, Blv. Dzordza Vasingtona bb, 81000 Podgorica, Montenegro;1. Monash University Accident Research Centre, Monash University, Victoria, australia;2. Road Safety Victoria, Department of Transport, Victoria, Australia;3. Victorian Institute of Forensic Medicine, Victoria, Australia;4. Monash University Eastern Health Clinical School, Victoria, Australia;5. Turner Institute for Brain and Mental Health, Monash University, Victoria, Australia;6. Sunnybrook Hospital, Ontario, Canada;7. Société de l’assurance automobile du Québec, Québec, Canada;8. Royal College of Physicians of Ireland, Dublin, Ireland;1. Department of Cognitive Robotics, Faculty Mechanical, Maritime and Materials Engineering, Delft University of Technology, Mekelweg 2, 2628 CD Delft, the Netherlands;2. Group Renault, Chassis Systems Department, 1 Avenue du Golf, 78280 Guyancourt, France;3. Department of Computer and System Engineering/U2IS, ENSTA Paris, Institut Polytechnique de Paris, 828 Boulevard des Maréchaux, 91762 Palaiseau Cedex, France |
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| Abstract: | To provide a better understanding of individual driver’s driving style classification in a traditional and a CV environment, spatiotemporal characteristics of vehicle trajectories on a road tunnel were extracted through a driving simulator-based experiment. Speed, acceleration, and rate of acceleration changes are selected as clustering indexes. The dynamic time warping and k-means clustering were adopted to classify participants into different risk level groups. To assess the driver behavior benefits in a CV environment, an indicator BI (behavior indicator, BI) was defined based on the standard deviation of speed, the standard deviation of acceleration, and the standard deviation of the rate of acceleration change. Then, the index BI of each driver was calculated. Furthermore, this paper explored driving style classification, not in terms of traditional driving environment, but rather the transition patterns from a traditional driving environment to a CV environment. The results revealed that inside a long tunnel, 80 % of drivers benefited from a CV environment. Moreover, drivers might need training before using a CV system, especially female drivers who have low driving mileage. In addition, the results showed that the driving style of 69 % of the drivers’ transferred from a high risk-level to a low risk-level when driving in a CV environment. The study results can be expected to improve driving training education programs and also to provide a valuable reference for developing individual in-vehicle human-machine interface projects and other proactive safety countermeasures. |
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| Keywords: | Connected vehicle Vehicle trajectory Driving style Driving simulator Driving behavior classification Road tunnel |
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