Understanding and predicting human driving behavior play an important role in the development of intelligent vehicle systems, particularly for Advanced Driver Assistance System (ADAS) to estimate dangerous operations and take appropriate actions. However, well-predicted lane-changing (LC) behavior is still challenging on account of the complexity and uncertainty of traffic status, and labeled data are required. To address this problem, we propose a novel framework, denoted as LCNet, for lane-changing behavior prediction via joint learning of the front view video images and driver physiological signals. Firstly, with a temporal consistency module, both labeled and unlabeled video frames can be utilized in the training phase, while no extra computation is required during inference. Secondly, a new penalty term is introduced for learning sequential physiological signals, which is sensitive to local continuity property. Finally, a new loss function is designed for LCNet to learn co-occurrence features from the video scene-optical flow branch and physiology branch efficiently. Moreover, the experiments are conducted on a real-world driving data set. The experimental results demonstrate that the LCNet can learn the underlying features of upcoming lane-changing behavior and significantly outperform the other advanced models.

了解和预测人类驾驶行为在智能车辆系统的开发中发挥着重要作用，特别是高级驾驶辅助系统 (ADAS) 可以估计危险操作并采取适当的行动。然而，由于交通状况的复杂性和不确定性，良好预测的换道（LC）行为仍然具有挑战性，并且需要标记数据。为了解决这个问题，我们提出了一个新的框架，称为 LCNet，用于通过前视视频图像和驾驶员生理信号的联合学习来预测变道行为。首先，使用时间一致性模块，标记和未标记的视频帧都可以在训练阶段使用，而在推理过程中不需要额外的计算。第二，引入了一个新的惩罚项来学习对局部连续性敏感的连续生理信号。最后，为 LCNet 设计了一种新的损失函数，以有效地从视频场景-光流分支和生理分支中学习共现特征。此外，实验是在真实世界的驾驶数据集上进行的。实验结果表明，LCNet 可以学习即将到来的变道行为的潜在特征，并显着优于其他高级模型。