Abstract: Disclosed herein is a neural network based pre-decoder comprising a permutation embedding engine, a permutation classifier each comprising one or more trained neural networks and a selection unit. The permutation embedding engine is trained to compute a plurality of permutation embedding vectors each for a respective one of a plurality of permutations of a received codeword encoded using an error correction code and transmitted over a transmission channel subject to interference. The permutation classifier is trained to compute a decode score for each of the plurality of permutations expressing its probability to be successfully decoded based on classification of the plurality of permutation embedding vectors coupled with the plurality of permutations. The selection unit is configured to output one or more selected permutations having a highest decode score. One or more decoders may be then applied to recover the encoded codeword by decoding the one or more selected permutations.

Abstract: 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.

Abstract: Provided herein are methods and systems for applying active learning to train neural network based decoders to decode error correction codes transmitted over transmission channels subject to interference. The decoder may be trained using training samples actively by mapping a distribution of a large pool of samples and selecting samples estimated to most contribute to the training, specifically to exclude high SNR samples expected to be correctly decoded and low SNR samples which are potentially un-decodable. Further presented are ensembles of neural network based decoders applied to decode error correction codes. Each of the decoders of the ensemble is actively learned and trained using samples mapped into a respective region of the training samples distribution and is therefore optimized for the respective region. In runtime, the received code may be directed to one or more of the ensemble's decoders according to the region into which the received code is mapped.

Abstract: 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.

Abstract: 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.