Abstract
In 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.
Highlights
The topic of car-following behavior has become increasingly important in traffic engineering and safety research [1,2,3]
It has the ability to identify the leading vehicle type in real-time because the hidden Markov model (HMM) hidden state can be predicted with limited historical data; The prediction accuracy is ensured by training the model with a large number of naturalistic driving data; Its responsiveness to dynamical conditions is achieved by estimating the optimum state of a car-following model based on historical data
The rest of the paper is organized as follows: Section 2 introduces the data set used in this study; the idea and structure of this new car-following model are described in Section 3; how to obtain the model parameters based on the naturalistic data are explained in Section 4; the effectiveness of this model is validated in Section 5; Sections 6 and 7 give out some applications and further discussions about this study; and Section 8 concludes the paper
Summary
The topic of car-following behavior has become increasingly important in traffic engineering and safety research [1,2,3]. Classical car-following models are designed to characterize the interactional phenomena between the individual driver and the traffic, e.g., microscopic simulation [7,8] These types of car-following models include the Gazis-Herman-Rothery (GHR) model, safety distance or collision avoidance (CA) model, linear models, action point models (AP), and Fuzzy logic-based models [1,9]. It has the ability to identify the leading vehicle type in real-time because the HMM hidden state can be predicted with limited historical data; The prediction accuracy is ensured by training the model with a large number of naturalistic driving data; Its responsiveness to dynamical conditions is achieved by estimating the optimum state of a car-following model based on historical data. The rest of the paper is organized as follows: Section 2 introduces the data set used in this study; the idea and structure of this new car-following model are described in Section 3; how to obtain the model parameters based on the naturalistic data are explained in Section 4; the effectiveness of this model is validated in Section 5; Sections 6 and 7 give out some applications and further discussions about this study; and Section 8 concludes the paper
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