A Vehicle Type Dependent Car-following Model Based on Naturalistic Driving Study
AbstractIn this paper, a car-following model considering the preceding vehicle type is proposed to describe the longitudinal driving behavior closer to reality. Based on the naturalistic driving data sampled in real traffic for more than half a year, the relation between ego vehicle velocity and relative distance was analyzed by a multi-variable Gaussian Mixture model, from which it is found that the driver following behavior is influenced by the type of leading vehicle. Then a Hidden Markov model was designed to identify the vehicle type. This car-following model was trained and tested by using the naturalistic driving data. It can identify the leading vehicle type, i.e., passenger car, bus, and truck, and predict the ego vehicle velocity and relative distance based on a series of limited historical data in real time. The experimental validation results show that the identification accuracy of vehicle type under the static and dynamical conditions are 96.6% and 83.1%, respectively. Furthermore, comparing the results with the well-known collision avoidance model and intelligent driver model show that this new model is more accurate and can be used to design advanced driver assist systems for better adaptability to traffic conditions. View Full-Text
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Wu, P.; Gao, F.; Li, K. A Vehicle Type Dependent Car-following Model Based on Naturalistic Driving Study. Electronics 2019, 8, 453.
Wu P, Gao F, Li K. A Vehicle Type Dependent Car-following Model Based on Naturalistic Driving Study. Electronics. 2019; 8(4):453.Chicago/Turabian Style
Wu, Ping; Gao, Feng; Li, Keqiang. 2019. "A Vehicle Type Dependent Car-following Model Based on Naturalistic Driving Study." Electronics 8, no. 4: 453.
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