Abstract
The modeling of car-following behavior is an attractive research topic in traffic simulation and intelligent transportation. The driver plays an important role in car following but is ignored by most car-following models. This paper presents a novel car-following driver model, which can retain aspects of human driving styles. First, simulated car-following data are generated by using the speed control driver model and the real-world driving behavior data if the real-world car-following data are not available. Then, the car-following driver model is established by imitating human driving maneuver during real-world car following. This is accomplished by using a neural network-based learning control paradigm and car-following data. Finally, the FTP-72 driving cycle is borrowed as the speed profile of the leading vehicle for the model test. The driving style is quantitatively analyzed by AESD. The results show that the proposed car-following driver model is capable of retaining the naturalistic driving styles while well accomplishing the car-following task with the error of relative distance mostly less than 5 meters for every driving styles.
Highlights
Car-following behavior is common in real-world driving, and its modeling has been an important research topic in tra c simulation and vehicle technology
A car-following driver model is established for each Driving Behavior Data (DBD) sample, which results in 36 Car-Following Driver Model (CFDM)
Each established CFDM is applied to the car-following test simulation, where the leading vehicle speed profile is
Summary
Car-following behavior is common in real-world driving, and its modeling has been an important research topic in tra c simulation and vehicle technology. We propose to establish the style-retaining Car-Following Driver Model (CFDM) by imitating human driving behaviors. We propose to employ the neural network-based learning control paradigm and the real-world car-following data (CFD) to make the style-retained driver modeling possible. 2. Review on STDM, DBD, and ESD e speed-tracking driver model (STDM) is established based on the DBD and is applied to generate the simulated car-following data. E actual speed of the real-world DBD is adopted as the speed profile of the leading vehicle, and the resulted follower’s TP/BP operations, vehicle speed, and the relative distance constitute the simulated car-following data. To deal with the possible drastic variations on a local or intermediate scale within the driving behavior data, the B-spline neural network (BSNN) is employed to implement the car-following driver model. Where biq(x) is the ith basis function of order q, λi is the ith knot, and Ii is the ith interval
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