Leveraging Wrist-Mounted Wearables for Lane-Change Detection

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.