CarSpeedNet: A Deep Neural Network-based Car Speed Estimation from
  Smartphone Accelerometer
Barak Or
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.