Existing studies on autonomous driving methods focus on the fusion of onboard sensor data. However, the driving behavior might be unsteady because of the uncertainties of environments. In this article, an expectation line is proposed to quantify the driving behavior motivated by the driving continuity of human drivers. Furthermore, the smooth driving could be achieved by predicting the future trajectory of the expectation line. First, a convolutional neural network-based method is applied to detect lanes in images sampled from driving video. Second, the expectation line is defined to model driving behavior of an autonomous vehicle. Finally, the long short-term memory-based method is applied to the expectation line so that the future trajectory of the vehicle could be predicted. By incorporating convolutional neural network- and long short-term memory-based methods, the autonomous vehicles could smoothly drive because of the prior information. The proposed method is evaluated using driving video data, and the experimental results demonstrate that the proposed method outperforms methods without trajectory predictions.

关于自动驾驶方法的现有研究集中于车载传感器数据的融合。但是，由于环境的不确定性，驾驶行为可能会不稳定。在本文中，提出了一条期望线来量化由人类驾驶员的驾驶连续性引起的驾驶行为。此外，可以通过预测期望线的未来轨迹来实现平稳行驶。首先，基于卷积神经网络的方法被应用于检测从驾驶视频采样的图像中的车道。其次，定义期望线以对自动驾驶车辆的驾驶行为进行建模。最后，将基于长期记忆的长期方法应用于期望线，以便可以预测车辆的未来轨迹。通过结合卷积神经网络和基于长期短期记忆的方法，由于先验信息，自动驾驶汽车可以平稳行驶。利用驾驶视频数据对所提出的方法进行了评估，实验结果表明，所提出的方法优于没有轨迹预测的方法。