Learning Car Speed Using Inertial Sensors

• URL: http://arxiv.org/abs/2205.07883v1
• Date: Sun, 15 May 2022 17:46:59 GMT
• Title: Learning Car Speed Using Inertial Sensors
• Authors: Maxim Freydin and Barak Or
• Abstract summary: A deep neural network (DNN) is trained to estimate the speed of a car driving in an urban area. Three hours of data was collected by driving through the city of Ashdod, Israel in a car equipped with a global navigation satellite system. The trained model is shown to substantially improve the position accuracy during a 4 minutes drive without the use of position updates.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: A deep neural network (DNN) is trained to estimate the speed of a car driving in an urban area using as input a stream of measurements from a low-cost six-axis inertial measurement unit (IMU). Three hours of data was collected by driving through the city of Ashdod, Israel in a car equipped with a global navigation satellite system (GNSS) real time kinematic (RTK) positioning device and a synchronized IMU. Ground truth labels for the car speed were calculated using the position measurements obtained at the high rate of 50 [Hz]. A DNN architecture with long short-term memory layers is proposed to enable high-frequency speed estimation that accounts for previous inputs history and the nonlinear relation between speed, acceleration, and angular velocity. A simplified aided dead reckoning localization scheme is formulated to assess the trained model which provides the speed pseudo-measurement. The trained model is shown to substantially improve the position accuracy during a 4 minutes drive without the use of GNSS position updates.

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