In this study, a novel deep neural network (DNN) architecture, CarSpeedNet, is introduced to estimate car speed using three-axis accelerometer data from smartphones. Utilizing 13 hours of data collected from smartphones mounted in vehicles navigating through various regions in Israel, the CarSpeedNet effectively learns the relationship between measured smartphone acceleration and car speed. Ground truth speed data was obtained at 1[Hz] from the GPS receiver in the smartphones. The proposed model enables high-frequency speed estimation, incorporating historical inputs. Our trained model demonstrates exceptional accuracy in car speed estimation, achieving a precision of less than 0.72[m/s] during an extended driving test, solely relying on smartphone accelerometer data without any connectivity to the car.

通过使用智能手机的三轴加速度计数据，本研究引入了一种新颖的深度神经网络（DNN）架构CarSpeedNet，用于估计汽车速度。通过收集在以色列不同地区行驶的车辆中安装的智能手机的13小时数据，CarSpeedNet有效地学习了测量的智能手机加速度和汽车速度之间的关系。作者提出的模型能够进行高频速度估计，并整合历史输入。在仅依靠智能手机加速度计数据而不与汽车进行任何连接的情况下，在一次长时间驾驶测试中，我们训练的模型表现出了卓越的车速估计准确性，达到了小于0.72[m/s]的精度。

CarSpeedNet：基于智能手机加速度计的深度神经网络汽车速度估计