Anticipating lane change intentions of surrounding vehicles is crucial for
efficient and safe driving decision making in an autonomous driving system.
Previous works often adopt physical variables such as driving speed,
acceleration and so forth for lane change classification. However, physical
variables do not contain semantic information. Although 3D CNNs have been
developing rapidly, the number of methods utilising action recognition models
and appearance feature for lane change recognition is low, and they all require
additional information to pre-process data. In this work, we propose an
end-to-end framework including two action recognition methods for lane change
recognition, using video data collected by cameras. Our method achieves the
best lane change classification results using only the RGB video data of the
PREVENTION dataset. Class activation maps demonstrate that action recognition
models can efficiently extract lane change motions. A method to better extract
motion clues is also proposed in this paper.

利用采集的视频数据，我们提出了一个包含两个行动识别方法的端到端框架，用于识别车道变更，并且在 PREVENTION 数据集上表现最佳，表明行动识别模型可以有效地提取车道变更动作；该论文还提出了一种更好地提取运动线索的方法。