Accurate and stable recognition of a driver's lateral intention is a crucial prerequisite for the proper functioning of advanced driver-assistance systems (ADAS). Existing studies usually rely on auxiliary sensor signals, such as cameras and eye trackers; however, this reliance poses challenges in applying these methods to vehicles lacking such auxiliary sensors. Furthermore, existing studies have not fully leveraged the inherent temporal dependence of lateral intentions, leading to difficulties in avoiding erroneous recognition interruptions. Thus, this study proposes a deep-learning-based lateral intention recognition method to achieve accurate and stable recognition of lateral intention using onboard sensor signals. First, a real vehicle is used to collect a vast amount of driving data, and thus guarantee the robustness and practicality of the recognition model. Subsequently, vehicle trajectories are extracted, and a trajectory clustering method is used to label lateral intentions of the driving data; these intention labels and a feature selection algorithm are utilized to select the most representative recognition features. Therefore, a lateral driving intention recognition model is constructed using double convolutional neural networks with a long short-term memory layer (CNN-LSTM). This network architecture can fully utilize the temporal dependence of lateral intentions. Finally, the recognition performance of the designed double CNN-LSTM networks is validated using the existing driving data and real-world vehicle tests. The results indicate that the double CNN-LSTM networks can achieve stable recognition of lateral intention in real-time and the accuracy reaches 98.64% in the experiment.

中文翻译：

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利用自然驾驶数据准确稳定地识别驾驶员横向意图的深度学习方法

准确、稳定地识别驾驶员的横向意图是高级驾驶员辅助系统 (ADAS) 正常运行的关键先决条件。现有的研究通常依赖于辅助传感器信号，例如摄像头和眼动仪；然而，这种依赖给将这些方法应用于缺乏此类辅助传感器的车辆带来了挑战。此外，现有研究尚未充分利用横向意图固有的时间依赖性，导致难以避免错误的识别中断。因此，本研究提出一种基于深度学习的横向意图识别方法，利用车载传感器信号实现准确稳定的横向意图识别。首先，使用实车采集大量的驾驶数据，从而保证识别模型的鲁棒性和实用性。随后提取车辆轨迹，并利用轨迹聚类方法标注驾驶数据的横向意图；这些意图标签和特征选择算法用于选择最具代表性的识别特征。因此，利用具有长短期记忆层的双卷积神经网络（CNN-LSTM）构建了横向驾驶意图识别模型。这种网络架构可以充分利用横向意图的时间依赖性。最后，使用现有的驾驶数据和实际车辆测试来验证所设计的双 CNN-LSTM 网络的识别性能。结果表明，双CNN-LSTM网络能够实现稳定的实时横向意图识别，实验中准确率达到98.64%。