Detecting dangerous driving has been of critical interest for the past few years. However, a practical yet minimally intrusive solution remains challenging as existing technologies heavily rely on visual features or physical proximity. With this motivation, we explore the feasibility of purely using mmWave radars to detect dangerous driving behaviors. We first study characteristics of dangerous driving and find some unique patterns of range-doppler caused by 9 typical dangerous driving actions. We then develop a novel Fused-CNN model to detect dangerous driving instances from regular driving and classify 9 different dangerous driving actions. Through extensive experiments with 5 volunteer drivers in real driving environments, we observe that our system can distinguish dangerous driving actions with an average accuracy of > 95%. We also compare our models with existing state-of-the-art baselines to establish their significance.

翻译：检测危险驾驶行为近年来一直备受关注。然而，当前技术因严重依赖视觉特征或物理接近性，仍难以实现兼具实用性与低侵入性的方案。受此推动，我们探究了纯粹利用毫米波雷达检测危险驾驶行为的可行性。首先，我们研究了危险驾驶的特性，发现了九类典型危险驾驶动作在距离-多普勒域中呈现的独特模式。随后，我们提出了一种新型融合卷积神经网络（Fused-CNN）模型，用于从常规驾驶行为中检测危险驾驶实例，并对九种不同危险驾驶动作进行分类。通过在真实驾驶环境中对5名志愿者驾驶员进行大量实验，我们观察到该系统能以超过95%的平均准确率区分危险驾驶行为。此外，我们将模型与现有最先进基线方法进行对比，验证了其显著优势。