Aggressive driving, such as frequent lane changes, endangers other persons or property but is challenging to be continuously tracked by existing traffic surveillance systems. In this paper, we use wrist-mounted wearables, such as a smartwatch to monitor the driver’s forearm acceleration and to detect lane changes. Because the forearm acceleration of lane changes can be significantly affected by traveling speed and steering angle, our system transforms the time-domain acceleration data to the frequency domain for clearly depicting the signal distribution over a range of frequencies. To further improve detection accuracy, we develop an adaptive algorithm which dynamically determines the target frequency band and adjusts the signal energy evaluation threshold based on current traveling speed. The algorithm will also examine the signal energy distribution over other frequencies besides the target frequency band to avoid false alarms when driving on road curves. We have evaluated our system in real driving environments, including both the low-speed local roads and high-speed expressways, and the results show that the system achieves high detection accuracy at low computational complexity.

中文翻译：

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利用腕戴式可穿戴设备进行车道变换检测

激进驾驶，例如频繁变道，会危及他人或财产，但现有交通监控系统难以持续跟踪。在本文中，我们使用腕戴式可穿戴设备（例如智能手表）来监控驾驶员的前臂加速度并检测车道变化。由于车道变换的前臂加速度会受到行驶速度和转向角的显着影响，因此我们的系统将时域加速度数据转换为频域，以便清楚地描绘在一定频率范围内的信号分布。为了进一步提高检测精度，我们开发了一种自适应算法，该算法动态确定目标频带并根据当前行驶速度调整信号能量评估阈值。该算法还将检查目标频带以外的其他频率上的信号能量分布，以避免在道路弯道上行驶时出现误报。我们在真实的驾驶环境中对我们的系统进行了评估，包括低速地方道路和高速高速公路，结果表明该系统在低计算复杂度的情况下实现了高检测精度。