Autobrains Technologies Ltd.

Omer Jackobson

Igal Raichelgauz

Adam Harel

Boaz Mell

A method for radar based object classification, the method may include obtaining multiple radar samples of an object; the multiple radar samples were acquired at different acquisition times; wherein the multiple radar samples comprise a plurality of first radar sample parameters; calculating second radar sample parameters for the multiple radar samples, by applying one or more non-linear functions on at least some of the plurality of first radar sample parameters of at least some of the multiple radar samples; generating an object signature that comprises temporal information and inter-parameter correlation information; wherein the generating comprises feeding, to each one of a deep neural network (DNN) and a time delay neural network (TDNN), (a) at least some of the plurality of first radar sample parameters, and (b) at least some of the second radar sample parameters; and classifying, by a classifier, the signature to a signature class.

Radar based object classification

A method for pedestrian crossing alert, the method may include receiving, by a vehicle computerized system, pedestrian crossing indicators; obtaining sensed information regarding an environment of the vehicle; processing the sensed information, wherein the processing comprises searching for at least one of the pedestrian crossing indicators; autonomously determining, when finding the at least one of the pedestrian crossing identifiers, that the vehicle is approaching a pedestrian crossing; and generating an alert when determining that the vehicle is approaching the pedestrian crossing.

Pedestrian crossing alert

A method for predicting behaviors of road users, the method may include sensing a vicinity of a vehicle to provide sensed information; processing the sensed information to provide compact contextual signatures of sensed road users within the vicinity of the vehicle; wherein a compact contextual signature of each a sensed road user includes (a) coarse contextual metadata regarding the sensed road user, (b) coarse location information regarding the sensed road user, (c) identifiers of other sensed road users, and (d) coarse situation information; feeding the compact contextual signatures to a machine learning process trained to estimate behaviors of road users based on compact contextual signatures of road users; and predicting, by the machine learning process, the behaviors of the sensed road users.

Growing community of inventors

Tel Aviv, Israel

Omer Jackobson