Handheld phone distraction is the leading cause of traffic accidents. However, few efforts have been devoted to detecting when the phone distraction happens, which is a critical input for taking immediate safety measures. This work proposes a phone-use monitoring system, which detects the start of the driver's handheld phone use and eliminates the distraction at once. Specifically, the proposed system emits periodic ultrasonic pulses to sense if the phone is being held in hand or placed on support surfaces (e.g., seat and cup holder) by capturing the unique signal interference resulted from the contact object's damping, reflection and refraction. We derive the short-time Fourier transform from the microphone data to describe such impacts and develop a CNN-based binary classifier to discriminate the phone use between the handheld and the handsfree status. Additionally, we design an adaptive window-based filter to correct the classification errors and identify each handheld phone distraction instance, including its start, end, and duration. Extensive experiments with fourteen people, three phones and two car models show that our system achieves 99% accuracy of recognizing handheld phone-use instances and 0.76-second median error to estimate the distraction's start time.
翻译:手持电话干扰是交通事故的主要原因。 但是,很少努力在电话干扰发生时进行检测,这是立即采取安全措施的关键投入。 这项工作提议了电话使用监测系统,检测司机手持电话使用的起始时间,并同时消除干扰。 具体地说, 提议的系统将定期超声波脉冲释放出来,以感知如果手机被手持或放在辅助表面(例如座椅和杯架),通过捕捉接触对象的阻隔、反射和反射所产生的独特信号干扰。 我们从麦克风数据中抽取短时间的 Fourier变换,以描述这种影响,并开发一个基于CNN的二进制分类器,以区分手持手机和手无手状态之间的电话使用。 此外,我们设计一个基于适应窗口的过滤器,以纠正分类错误,并确定每个手持电话干扰实例,包括其启动、结束和持续时间。 与14人、3部手机和2部汽车模型进行的广泛实验显示,我们的系统在识别手持电话使用情况方面实现了99%的精确度, 和0.76秒的中位误算法。