Abstract
A correct lane-changing plays a crucial role in traffic safety. Predicting the lane-changing behavior of a driver can improve the driving safety significantly. In this paper, a hybrid neural network prediction model based on recurrent neural network (RNN) and fully connected neural network (FC) is proposed to predict lane-changing behavior accurately and improve the prospective time of prediction. The dynamic time window is proposed to extract the lane-changing features which include driver physiological data, vehicle kinematics data, and driver kinematics data. The effectiveness of the proposed model is validated through the experiments in real traffic scenarios. Besides, the proposed model is compared with five prediction models, and the results show that the proposed prediction model can effectively predict the lane-changing behavior more accurate and earlier than the other models. The proposed model achieves the prediction accuracy of 93.5% and improves the prospective time of prediction by about 2.1 s on average.
Highlights
Driver lane-changing behavior is a key factor in driving safety
Dou et al [27] introduced a prediction model based on the support vector machine (SVM) and BP neural network, which combined the results of SVM and BP neural network to improve the prediction accuracy, and the results showed that the average combined accuracy exceeded 92%
A Seq2Seq-FC neural network for prediction of driver lane-changing behavior is introduced. e proposed model has two levels, where the first level denotes the Seq2Seq network whose function is to process the time series data and the second level denotes a fully connected neural network which works as a nonlinear classifier
Summary
Driver lane-changing behavior is a key factor in driving safety. An improper lane-changing behavior may cause a vehicle collision [1, 2] or even a traffic accident [3,4,5]. In [25], a fully connected neural network was applied to predict the lane-changing behavior of drivers; especially, the network model input consisted of multivehicle data, and the prediction accuracy of more than 90% was achieved. A multifeature fusion neural network [26] that takes into account the physiological factors such as driver’s head rotation was proposed to predict driver lane change behavior and the prediction accuracy exceeded 85%, while the prospective time was 1.5 s. An MTSDeepNet using a convolution kernel to process the multivariate time series data and a fully connected neural network to classify the lane-changing behavior were designed to predict the lane changing in [28], and the accuracy of this model exceeded 91.0%.
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