Learning-Based Speed Estimation from Accelerometer-Only Inertial Sensing

Abstract

The proposed model, CarSpeedNet, estimates scalar vehicle speed from a window of three-axis smartphone acceleration, without gyroscope, wheel-odometry, vehicle-bus, or positioning input at inference. The reported experiment comprises 13.2 hours of on-road driving. Beyond the network comparison, a finite-context analysis treats window length as part of the sensing problem. For nested histories, the minimum Bayes mean-square error is non-increasing with context; a complementary cue-coverage relation links the same window to the amount of speed-dependent vibration presented to the network and to the observation horizon. At a 1-s input, CarSpeedNet is compared with five temporal-network alternatives; the effect of temporal context is then measured over six window lengths. On the 0.5-hour holdout, a 4-s window yielded a root-mean-square error (RMSE) of 1.8 m/s and a mean absolute error (MAE) of 0.72 m/s, compared with 2.9 and 1.3 m/s at 1 s. The 178,169-parameter network requires about 0.68 MiB for 32-bit weights.