Abstract: A system and method for estimating a velocity of a vehicle, including obtaining readings of at least one inertial sensor that is attached to the vehicle, calculating a time difference between a time when an irregularity is sensed in a first location of the vehicle and a time when the irregularity is sensed in a second location of the vehicle, and calculating the velocity of the vehicle based on the time difference.

Abstract: A system and method for estimating a velocity of a vehicle, including obtaining readings of at least one inertial sensor that is attached to the vehicle, calculating a time difference between a time when an irregularity is sensed in a first location of the vehicle and a time when the irregularity is sensed in a second location of the vehicle, and calculating the velocity of the vehicle based on the time difference.

Abstract: A system and method for providing localization, including, during a training phase: obtaining a training dataset of accelerations, angular velocities, and known locations over time of vehicles moving in a defined area; and training a machine learning model to provide location estimation in the defined area based on the accelerations and angular velocities using the training dataset; and during runtime phase: obtaining runtime accelerations and angular velocities overtime of a vehicle moving in the defined area; and using the trained model to obtain current location of the vehicle based on the runtime acceleration and angular velocities.

Abstract: A system and method for estimating a velocity of a vehicle, including obtaining readings of at least one inertial sensor that is attached to the vehicle, calculating a time difference between a time when an irregularity is sensed in a first location of the vehicle and a time when the irregularity is sensed in a second location of the vehicle, and calculating the velocity of the vehicle based on the time difference.

Abstract: A method of tracking ground vehicles based on Inertial Measurement Unit (IMU) data, comprising accumulating motion data captured by IMU(s) deployed in a vehicle, the motion data captured periodically in a plurality of time points starting from a known initial location of the vehicle comprises at least acceleration and angular velocity, computing, based on the motion data, a measured heading angle time-series of the vehicle expressing a movement direction of the vehicle over time, analyzing map data of an area of the known initial location to identify candidate routes, simulating, based on the map data, a simulated heading angle time-series for each candidate route expressing a simulated movement direction along the respective candidate route over time, ranking the candidate routes based on match between each simulated heading angle time-series and the measured heading angle time-series, and estimating a location of the vehicle based on the ranking.

Abstract: A system and method for providing localization, including, during a training phase: obtaining a training dataset of accelerations, angular velocities, and known locations over time of vehicles moving in a defined area; and training a machine learning model to provide location estimation in the defined area based on the accelerations and angular velocities using the training dataset; and during runtime phase: obtaining runtime accelerations and angular velocities overtime of a vehicle moving in the defined area; and using the trained model to obtain current location of the vehicle based on the runtime acceleration and angular velocities.

Abstract: A system and method for estimating a location of a vehicle, including, using a processor: measuring, via an inertial sensor attached to a vehicle, at least one of an acceleration or angular velocity of the vehicle; determining, via the processor, in the at least one of the acceleration or the angular velocity a signal signature based on a pattern recognition algorithm, wherein the signal signature is associated with a particular mark located at a particular location; determining, via the processor, that the vehicle is located at the particular location based on the predetermined association between the particular mark and the particular location; and providing, via the processor, the particular location to a human user or to a computerized application.

Abstract: A system and method for providing localization, including, during a training phase: obtaining a training dataset of accelerations, angular velocities, and known locations over time of vehicles moving in a defined area; and training a machine learning model to provide location estimation in the defined area based on the accelerations and angular velocities using the training dataset; and during runtime phase: obtaining runtime accelerations and angular velocities over time of a vehicle moving in the defined area; and using the trained model to obtain current location of the vehicle based on the runtime acceleration and angular velocities.

Abstract: A system and method for providing localization, including, during a training phase: obtaining a training dataset of accelerations, angular velocities, and known locations over time of vehicles moving in a defined area; and training a machine learning model to provide location estimation in the defined area based on the accelerations and angular velocities using the training dataset; and during runtime phase: obtaining runtime accelerations and angular velocities over time of a vehicle moving in the defined area; and using the trained model to obtain current location of the vehicle based on the runtime acceleration and angular velocities.