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.

中文翻译：

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基于深度学习的驾驶员换行行为预测

正确的换道对交通安全起着至关重要的作用。预测驾驶员的变道行为可以显着提高驾驶安全性。本文提出了一种基于递归神经网络（RNN）和全连接神经网络（FC）的混合神经网络预测模型，以准确地预测变道行为并缩短预测的预期时间。提出了动态时间窗以提取车道改变特征，其包括驾驶员生理数据，车辆运动学数据和驾驶员运动学数据。通过在实际交通场景中的实验验证了所提模型的有效性。此外，将所提出的模型与五个预测模型进行了比较，结果表明，所提出的预测模型能够比其他模型更准确，更早地有效预测变道行为。该模型的预测准确率达到93.5％，平均预期时间缩短了约2.1 s。