Autotalks Ltd.

Nir Raviv

Onn Haran

Ariel Feldman

Leonid Prokupets

Methods and systems for processing vehicle to everything (V2X) messages for use by machine learning applications are disclosed. From each of a plurality of vehicles, one or more V2X messages are received, each V2X message including vehicle-related data associated with the vehicle and the received message. A sequence of frames is generated based on the vehicle-related data from at least a subset of vehicles in the plurality of vehicles. Slices of the sequence of frames are aggregated to generate a plurality of time-lapse images. One or more time-lapse images are processed using a machine learning algorithm to generate an output indicative of a traffic-related prediction.

V2X message processing for machine learning applications

System and methods for plausibility check in vehicle-to-everything dynamic environments in which a local vehicle communicates with remote vehicles. The system comprises means for obtaining a measured RSSI from a specific remote vehicle, and a modified plausibility check unit configurable and operable to apply a dynamic RSSI model to detect implausible positioning of the specific remote vehicle and/or of the local vehicle based on the measured RSSI of the specific remote vehicle and on a RSSI calculated for the specific remote vehicle. Decisions on respective further actions to be performed by the specific remote vehicle and by the local vehicle are made based on respective plausibility checks applied to both vehicles using the dynamic RSSI model.

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Tel Aviv, Israel

Nir Raviv