Abstract: A method for augmented driving related virtual fields, the method includes obtaining object information regarding one or more objects located within an environment of a vehicle; and determining, by a processing circuit, and based on the object information, a desired virtual acceleration of the vehicle, wherein the determining of the desired virtual acceleration of the vehicle is based on a virtual physical model that represents an impact of the one or more objects on a behavior of the vehicle, wherein the virtual physical model is built based on one or more physical laws and at least one additional driving related parameter.

Abstract: A method for virtual fields driving related operations, the method includes (i)obtaining coarse navigation information regarding at least a segment of a path of a vehicle to a destination of the vehicle; and (ii) determining one or more virtual forces, by using a processing circuitry and based on the coarse navigation information, the one or more virtual forces are for use in applying a driving related operation of the vehicle, the one or more virtual forces belong to a virtual physical model and represent, at least in part, the coarse navigation information.

Abstract: A method for field related driving, the method includes obtaining object information regarding one or more objects located within an environment of the vehicle; determining, by using one or more neural network (NNs), and based on the object information, one or more virtual elastic fields and one or more virtual inelastic fields of the one or more objects; and determining, based on the one or more virtual elastic fields and the one or more virtual inelastic fields, a virtual force for use in applying a driving related operation of the vehicle, wherein the virtual force is associated with a physical model and representing an impact of the one or more objects on a behavior of the vehicle.

Abstract: A method for personalizing a driving experience, the method includes (a) obtaining a neural network (NN) that generates suggested driving patterns and represents a virtual force applied on a vehicle by one or more objects for use in applying a driving related operation of the vehicle, the virtual force is related to a virtual physical model that represents impacts of the one or more objects on a behavior of the vehicle; and (b) fine tuning at least a portion of the neural network based on one or more fine tuning parameters.

Abstract: A method for communicating with a driver of a vehicle, the method includes (i) obtaining object information regarding one or more objects located within an environment of the vehicle; (ii) analyzing the object information; (iii) determining, by using one or more neural network (NNs), and based on the object information, a virtual force associated with a physical model and representing an impact of the one or more objects on a behavior of the vehicle; and (iv) determining, based on at least the virtual force, a force feedback for use in providing a physical restraining force to be applied in association with a driver action made by the driver using the vehicle.

Abstract: A method for generating the at least one highway exit indicator, the method may include receiving video information and location information obtained during driving sessions of the plurality of vehicles; determining, based on the location information, multiple suspected highway exit events; selecting video information segments, wherein each selected video information segment is acquired before a suspected highway exit event and in timing proximity to the suspected highway exit event; and applying a machine learning process on at least some of the selected video information segments to find the at least one highway exit indicator.

Abstract: A method for cut in maneuver alert, the method may include receiving by a vehicle computerized system of a vehicle, one or more cut in maneuver predictors, each cut in maneuver predictor is (a) indicative of a predicted cut in maneuver initiated by another vehicle, and is (b) generated by a machine learning process; obtaining sensed information regarding an environment of the vehicle; processing the sensed information, wherein the processing comprises searching for a cut in maneuver predictor; and finding the cut in maneuver predictor.

Abstract: A method for generating a sparse binary representation (SBR) of neural network intermediate features (NNIFs) of a neural network (NN). The method includes (i) feeding the neural network by input information; (ii) neural network processing the input information to provide, at least, the NNIFs; (iii) SBR processing, by a SBR module, the NNIFs, to provide the SBR representation of the NNIFs; and (iv) outputting the SBR representation. The SBR module has undergone a training process that used a loss function that takes into account a sparsity of training process SBR representations.

Abstract: A method for determining driving policies for a vehicle includes: receiving, in real-time during a trip of a vehicle, at least a set of input multimedia content elements captured by at least one sensor deployed in proximity to the vehicle, determining a plurality of possible future scenarios based at least on the set of input multimedia content elements, determining a probability score for each of the plurality of possible future scenarios, determining at least one driving policy according to at least the probability score for at least one of the plurality of possible future scenarios, and controlling the vehicle according to the at least one driving policy.

Abstract: A system and method for providing recommendations based on current user interests. The method includes identifying at least one current variable, wherein each current variable is associated with a user device or a user; determining, based on the identified at least one current variable, at least one current user interest of a user profile, the user profile including at least one contextual insight, wherein each contextual insight is based on at least one signature for at least one multimedia content element associated with the user; searching for at least one multimedia content element that matches the at least one current user interest; and causing a display of the at least one matching content item as a recommendation.

Abstract: A method for bypass alert, the method may include obtaining during a driving session of a vehicle, sensed information about an environment of the first vehicle, by one of more vehicle sensors of the vehicle; detecting that the vehicle is within a certain distance from a destination of the driving session; detecting, using a first machine learning process, one or more parking indicators that are indicative of an allowable parking area that precedes the vehicle; detecting a vacant parking space within the parking area that is large enough to facilitate a parking of the vehicle within the vacant parking space; and responding to the vacant parking space.

Abstract: A method that may include training a student ODNN to mimic a teacher ODNN. The training may include calculating a teacher student detection loss that is based on a pre-bounding-box output of the teacher ODNN. The pre-bounding-box output of the teacher ODNN is a function of pre-bounding-box outputs of different ODNNs that belong to the teacher ODNN. The method may also include detecting one or more objects in an image, by feeding the image to the trained student ODNN; outputting by the trained student ODNN a student pre-bounding-box output; and calculating one or more bounding boxes based on the student pre-bounding-box output.

Abstract: Systems, and method and computer readable media that store instructions for configuring spanning elements of a signature generator.

Abstract: A method for driving-related object detection, the method may include receiving an input image by an input of an object detector; and detecting, by an object detector, objects that appear in the input image. The detecting includes searching for (i) a first object having a first size that is within a first size range and belongs to a four wheel vehicle class, (ii) a second object having a second size that is within a second size range and belongs to a subclass out of multiple four wheel vehicle subclasses, (iii) a pedestrian, and (iv) a two wheel vehicle; wherein a maximum of the first size range does not substantially exceed a minimum of the second size range.

Abstract: A method for risk based processing, the method may include detecting, based on first sensed information sensed at a first period, a suspected risk within an environment of a vehicle; selecting, from reference information, a situation related subset of the reference information, wherein the situation related subset of the reference information is related to the situation; selecting, from reference information, a suspected risk related subset reference information, wherein the potential risk related subset of the reference information is related to the potential risk; and determining whether the suspected risk is an actual risk, based at least on part on the suspected risk related subset reference information.

Abstract: A system and method for enriching a concept database. The method includes determining, based on at least one signature of a first multimedia data element (MMDE) and signatures of a plurality of existing concepts in the concept database, at least one first concept among the plurality of existing concepts, wherein each of the at least one first concept matches a portion of the at least one signature of the first MMDE; generating a reduced representation of the first MMDE, wherein generating the reduced representation further comprises removing the portion of the first MMDE matching the at least one first concept; comparing the reduced representation of the first MMDE to signatures representing a plurality of second MMDEs to determine a plurality of matching second MMDEs; generating, based on the reduced representation of the first MMDE and the plurality of matching second MMDEs, at least one second concept; and adding the generated at least one second concept to the concept database.

Abstract: A method for finding at least one trigger for human intervention in a control of a vehicle, the method may include receiving, from a plurality of vehicles, and by an I/O module of a computerized system, visual information acquired during situations that are suspected as situations that require human intervention in the control of at least one of the plurality of vehicles; determining, based at least on the visual information, the at least one trigger for human intervention; and transmitting to one or more of the plurality of vehicles, the at least one trigger.

Abstract: A method that may include training a student ODNN to mimic a teacher ODNN. The training may include calculating a teacher student detection loss that is based on a pre-bounding-box output of the teacher ODNN. The pre-bounding-box output of the teacher ODNN is a function of pre-bounding-box outputs of different ODNNs that belong to the teacher ODNN. The method may also include detecting one or more objects in an image, by feeding the image to the trained student ODNN; outputting by the trained student ODNN a student pre-bounding-box output; and calculating one or more bounding boxes based on the student pre-bounding-box output.

Abstract: A method for estimating a future fall of a cargo, the method may include receiving by a computerized system, sensed information related to driving sessions of multiple vehicles; applying a machine learning process on the sensed information to detect actual or estimated cargo falling events and generate one or more future falling cargo predictors for multiple types of cargo; estimating, from the sensed information, an impact of cargo falling events related to at least some of the types of cargo; and responding to the estimating, wherein the responding comprises at least one out of (a) storing the one or more future falling cargo predictors for the multiple types of cargo, (b) transmitting the one or more future falling cargo predictors for the multiple types of cargo; (c) storing the estimated impact of cargo falling events related to the at least some of the types of cargo, and (d) transmitting the impact of cargo falling events related to the at least some of the types of cargo.

Abstract: Using a read sensor to sense wrong-way driving. A method may include sensing, by a rear sensor of a vehicle, an environment of the vehicle to provide rear sensed information; processing the rear sensed information to provide at least one rear-sensed vehicle progress direction indications; generating or receiving at least one front-sensed vehicle progress direction indications; wherein the at least one front-sensed vehicle progress direction indications is generated by processing front-sensed information acquired during right-way progress; comparing at least one rear-sensed vehicle progress direction indications to the at least one front-sensed vehicle progress direction indications to determine whether the vehicle is wrong-way driving; and responding to the finding of the wrong-way driving.