We introduce a wearable driving status recognition device and our open-source dataset, along with a new real-time method robust to changes in lighting conditions for identifying driving status from eye observations of drivers. The core of our method is generating event frames from conventional intensity frames, and the other is a newly designed Attention Driving State Network (ADSN). Compared to event cameras, conventional cameras offer complete information and lower hardware costs, enabling captured frames to encode rich spatial information. However, these textures lack temporal information, posing challenges in effectively identifying driving status. DriveGazen addresses this issue from three perspectives. First, we utilize video frames to generate realistic synthetic dynamic vision sensor (DVS) events. Second, we adopt a spiking neural network to decode pertinent temporal information. Lastly, ADSN extracts crucial spatial cues from corresponding intensity frames and conveys spatial attention to convolutional spiking layers during both training and inference through a novel guide attention module to guide the feature learning and feature enhancement of the event frame. We specifically collected the Driving Status (DriveGaze) dataset to demonstrate the effectiveness of our approach. Additionally, we validate the superiority of the DriveGazen on the Single-eye Event-based Emotion (SEE) dataset. To the best of our knowledge, our method is the first to utilize guide attention spiking neural networks and eye-based event frames generated from conventional cameras for driving status recognition. Please refer to our project page for more details: https://github.com/TooyoungALEX/AAAI25-DriveGazen.

翻译：本文介绍了一种可穿戴驾驶状态识别设备及开源数据集，并提出一种对光照变化鲁棒的新型实时方法，该方法通过驾驶员眼部观测识别驾驶状态。我们方法的核心包括两方面：一是从传统强度帧生成事件帧，二是新设计的注意力驱动状态网络（ADSN）。与传统事件相机相比，常规相机能提供完整信息且硬件成本更低，使捕获的帧能够编码丰富的空间信息。然而这些纹理缺乏时间信息，这给有效识别驾驶状态带来了挑战。DriveGazen从三个角度解决该问题：首先，我们利用视频帧生成逼真的合成动态视觉传感器（DVS）事件；其次，采用脉冲神经网络解码相关时间信息；最后，ADSN从对应强度帧中提取关键空间线索，并通过新颖的引导注意力模块在训练和推理过程中将空间注意力传递至卷积脉冲层，以指导事件帧的特征学习与特征增强。为验证方法有效性，我们专门采集了驾驶状态（DriveGaze）数据集。此外，我们在单眼事件情感（SEE）数据集上验证了DriveGazen的优越性。据我们所知，本方法首次将引导注意力脉冲神经网络与基于传统摄像头生成的眼部事件帧相结合，用于驾驶状态识别。更多细节请访问项目页面：https://github.com/TooyoungALEX/AAAI25-DriveGazen。