Abstract: A method for real time analysis, the method includes producing, by a classification unit having a neural network, classification decisions for sensed information units obtained in an environment of a vehicle; automatically generating, by one or more computing devices and having auto-labeling capabilities running in a real-time driving of the vehicle, an automated ground truth labeling for a determined set of sensed information units; detecting, by the one or more computing devices and based on a performance indication related to the automated ground truth labeling, an issue with respect to the classification detection; and responsive to the detecting, addressing the detected issue in the real-time driving of the vehicle, using a signature associated with at least the classification decision or the detected issue. The neural network is in a same state in the producing the classification decision, detecting the issue, and addressing the issue.

Abstract: A method decorrelated topic based representation of road elements for classification, the method includes obtaining, at a machine learning process, a sparse binary representation corresponding to an initial embedding space of a road element captured in a sensed information unit; and selecting, by checking values of topic information of the sparse binary representation using the machine learning process, a topic, from a set of topics respectively characterized in the initial embedding space, the selected topic corresponding to a reduced space, each of the selected set of topics determined in decorrelation from another topic based on at least in part a measurement of an entropy distribution of bits of the sparse binary representation.

Abstract: A method for real time analysis, the method includes producing, by a classification unit having a neural network, classification decisions for sensed information units obtained in an environment of a vehicle; automatically generating, by one or more computing devices and having auto-labeling capabilities running in a real-time driving of the vehicle, an automated ground truth labeling for a determined set of sensed information units; detecting, by the one or more computing devices and based on a performance indication related to the automated ground truth labeling, an issue with respect to the classification detection; and responsive to the detecting, addressing the detected issue in the real-time driving of the vehicle, using a signature associated with at least the classification decision or the detected issue. The neural network is in a same state in the producing the classification decision, detecting the issue, and addressing the issue.

Abstract: A method for real time management of detected issues, the method includes producing, by a classification unit having a neural network, a classification decision for sensed information obtained in an environment of a vehicle; generating, by one or more computing devices with auto-labeling capabilities, an automated ground truth labeling for the sensed information; detecting, by the one or more computing devices and based on a performance indication related to the automated ground truth labeling, an issue with respect to the classification decision; and responsive to the detecting, addressing the detected issue in a driving in the environment of the vehicle by a computer device associated with the vehicle, using a signature generated in association with at least the classification decision or with the detected issue. The neural network is in a same state in the producing of the classification decision, the detecting the issue, and the addressing the detected issue.

Abstract: A system for multi-agent mission planning comprises a) a mission generator (MG) configured to compute a set of Mission Agent Language (MAL) missions; b) a group of agents (TGPs), each capable of performing at least one MAL mission; and c) at least one Multi-Agent Planner (MAP) adapted to receive said set of MAL missions from said MG to build an allocation vector between said MAL missions and said agents in said group; wherein each agent (TGP) is further capable of performing its assigned MAL mission per said allocation vector, while considering all other agents in its corresponding tactical group.

Abstract: A receiver and a method for channel estimation, the method includes calculating at least one initial channel estimate; characterized by calculating an estimate of the channel based upon a mathematical relationship between a first group of pilot subcarriers and a second group of pilot subcarriers; whereas a difference between locations of pilot subcarriers of the first group and locations of corresponding pilot subcarriers of the second group is substantially constant; and at least one of the following conditions are fulfilled: (i) pilot subcarriers that belong to the same group of subcarriers are non-evenly spaced in a frequency domain, (ii) a pilot subcarrier of the first group and a corresponding pilot subcarrier of the second group are proximate to each other in the frequency domain.

Abstract: A receiver and a method for channel estimation, the method includes calculating at least one initial channel estimate; characterized by calculating an estimate of the channel based upon a mathematical relationship between a first group of pilot subcarriers and a second group of pilot subcarriers; whereas a difference between locations of pilot subcarriers of the first group and locations of corresponding pilot subcarriers of the second group is substantially constant; and at least one of the following conditions are fulfilled: (i) pilot subcarriers that belong to the same group of subcarriers are non-evenly spaced in a frequency domain, (ii) a pilot subcarrier of the first group and a corresponding pilot subcarrier of the second group are proximate to each other in the frequency domain.