Imagry (israel) Ltd

Suhail Habib

Adham Ghazali

Khaled Fahoum

Majed Jubeh

Sergey Ten

Itai Orr

Jose Ariel Keselman

Ilan Shaviv

Abed Abu Dbai

A computer program product, a computerized apparatus and a method for generating movement pattern for an autonomous vehicle. The method comprises obtaining a driving scenario comprising a representation of a road segment and a driving destination of an autonomous vehicle driving in the road segment. The driving scenario comprises a second vehicle in the road segment. The method comprises selecting a driving model for the second vehicle from a plurality of driving models. The plurality of driving models comprises at least an autonomous vehicle driving model and a human driver driving model. The method comprises predicting, using the driving model, a predicted movement pattern of the second vehicle within the driving scenario; and based on the predicted movement pattern of the second vehicle, generating a movement pattern for the autonomous vehicle.

Alternative driving models for autonomous vehicles

A method, an apparatus and a computer program product for enhancing training of autonomous movement models based on map-based annotations. The method comprises augmenting an original training set with map based annotated modified scenarios at least one of which affects a path and at least one of which does not affect a path. The method comprises generating modified driving scenarios introducing a synthetic path altering object into the functional map representing the original movement or driving scenario, that induces a driving path that is different than the original path; and other modified driving scenarios that introduce a path non-altering object into the functional map of the original scenario, that the original path is applicable therein. An autonomous driving model for predicting driving path within a road segment based on a functional map representation is trained using the modified driving scenarios.

Map based annotation for autonomous movement models training

A method and a system for training a machine-learning model to identify real-world elements using a simulated environment (SE) may include (a) receiving at least one set of appearance parameters, corresponding to appearance of real-world element; (b) generating one or more realistic elements, each corresponding to a variant of at least one real-world element; (c) generating one or more abstract-elements; (d) placing the elements within the SE; (e) producing at least one synthetic image from the SE; (f) providing the at least one synthetic image to a machine-learning model; and (g) training the machine-learning model to identify at least one real-world element from the at least one synthetic image, that corresponds to at least one realistic element in the SE.

System and method for training a machine-learning model to identify real-world elements

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Haifa, Israel

Suhail Habib