Abstract: A method for driving policy visualization, the method includes (i) receiving, by a processing circuit, perception information that comprises environmental information about an environment of a vehicle and kinematic information regarding a movement of the vehicle; (ii) receiving, by the processing circuit, a multidimensional virtual force field representation of a driving policy applicable to the vehicle; (iii) reducing a dimension of the multidimensional virtual force field representation, based on the received perception information, to produce a reduced dimensional virtual force field representation that conforms with a driving of the vehicle; and (iv) dynamically visualizing, by applying the reduced dimensional virtual force field representation, the driving policy in the driving of the vehicle.

Abstract: A method for localized driving, the method includes (a) obtaining information about locations that are associated with multi-domain identifiers (MDIs) statistics, MDIs of each location are indicative of elements affecting a vehicle at the location; (b) obtaining an expected local path of a vehicle; (c) identifying path related locations, by a processing circuit, based on the expected local path and the information about the locations; and (d) determining, by the processing circuit, expected local path MDIs statistics for use in at least partially autonomous driving of a vehicle through the expected local path.

Abstract: A method for overcoming a detection limitation of a neural network, the method includes obtaining a sensed information unit that captures an object; obtaining an indication for a detection limitation of the neural network with respect to the object, wherein the detection limitation of the neural network prevents the neural network from generating a neural network output that is indicative of the object with at least a desirable certainty; feeding the sensed information unit to the neural network to provide a neural network output; and controlling a detection of the object by the neural network based on an indication that the object is captured in the sensed information unit.

Abstract: A method for off road driving, the method may include obtaining environment sensed information about an environment of a vehicle of a certain model, by one of more vehicle sensors of the vehicle and while driving over an off road path; detecting, by a machine learning process, an off road driving event; determining, by the machine learning process, a characteristic behavior of vehicles of the certain model when facing the off road driving event; and responding, at least in part by the machine learning process, to the occurrence of the off road driving event.

Abstract: A method that is computer implemented and for accuracy for object detection, the method includes (i) receiving content from an information source located outside of a vehicle; (ii) obtaining object information regarding one or more objects located within an environment of the vehicle; and (iii) generating, by applying one or more machine learning models, one or more object related estimations for use in a detection of the one or more objects; wherein at least one of the obtaining and the generating is impacted by the content.

Abstract: A method for training and testing a machine learning process, the method includes (a) learning virtual fields based on simulations of behaviors of a vehicle when faced with situations involving objects within environments of the vehicle, the virtual fields represent potential impacts of objects on the behaviors of the vehicle, wherein the learning is based on a virtual physical mode; (b) training the machine learning process to generate the virtual fields by applying a training process that uses outcomes of the simulations to provide a trained machine learning process; and (c) testing the trained machine learning process by feeding the trained machine learning process with other situations to provide test results.

Abstract: A method for augmented driving related virtual fields, the method includes (a) obtaining object information regarding one or more objects located within an environment of a vehicle; wherein the object information comprises spatial and temporal information extracted from a set of sensed information units (SIUs) of the environment of the vehicle that were acquired at different points in time; and (b) determining, by a processing circuit, and based on the object information, one or more virtual fields of the one or more objects, wherein the determining of the one or more virtual fields is based on a virtual physical model, wherein the one or more virtual fields represent a potential 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.

Abstract: A method for steering assistance, the method may include obtaining by vehicle camera, an image of an environment of the vehicle; feeding the image to a machine learning process; outputting, by the machine learning process, road line metadata that comprises a line offset value, a line angle value, a line curvature value and a line curvature rate; and preforming a steering related response to the road line metadata.

Abstract: A method for explainable representation, the method includes: (a) receiving, by an auxiliary representation network, information regarding an environment of a vehicle; the information being destined to be processed by a policy model, to provide driving related decisions at a current point of time; and (b) generating, by the auxiliary representation network, an interpretable representation of predicted outcomes of the policy model during a period of time that ends after the current point of time.

Abstract: A method for passing a curve, the method may include sensing, by a vehicle sensor, environment information regarding an environment of the vehicle; sensing at least one current propagation parameter of the vehicle; detecting, based on at least the environment information, (a) that the vehicle is about to reach the curve, (b) one or more first road conditions of a first road segment that precedes the curve; determining one or more curve passing propagation parameters to be applied by the vehicle while passing the curve, wherein the determining is based, at least in part, on the one or more first road conditions and on the at least one current propagation parameter; and responding to the determining.

Abstract: A method for perception fields driving related operations, the method may include (i) obtaining object information regarding one or more objects located within an environment of a vehicle; (ii) determining, using one or more neural network (NNs), one or more virtual forces that are applied on the vehicle, wherein the one or more virtual forces represent one or more impacts of the one or more objects on a behavior of the vehicle; wherein the one or more virtual forces belong to a virtual physical model; and (iii) performing one or more driving related operations of the vehicle based on the one or more virtual forces.

Abstract: A method for generating at least one school zone indicator, the method may include receiving by a vehicle computerized system, school zone indicators, wherein the school zone indicators are indicative of school zone elements; obtaining sensed information regarding an environment of the vehicle; processing the sensed information, wherein the processing comprises searching for one or more school zone indicators of the school zone indicators; wherein the school zone element is selected out of (i) a school zone object and (ii) a school zone situation; autonomously determining, when finding at least one of the one or more school zone identifiers, that the vehicle is driving towards a school zone or is within the school zone; and generating an alert when determining that the vehicle is driving towards the school zone or is within the school zone.

Abstract: A method for operation of narrow artificial intelligence (AI) agents for at least partially autonomous driving, the method includes: (a) receiving, by multiple perception modules, multi-domain information about elements affecting a vehicle; wherein each one of the multiple perception module is associated with a dedicated domain of the multi-domain information; (b) generating, by the multiple perception modules, class signatures that are indicative of classes of the elements of the multi-domain information; (c) determining a multi-domain identifier that identifies the generated class signatures of the multiple perception modules; and (d) identifying, based on the multi-domain identifier, one or more narrow AI agents that are relevant to a processing of at least a part of the multi-domain information.

Abstract: A method for field related driving, the method includes receiving content from an information source located outside of a vehicle; obtaining object information regarding one or more objects located within an environment of the vehicle; estimating, by using a neural network (NN), and based on the object information, one or more virtual fields of the one or more objects; and determining, based on the one or more virtual 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. At least one step of the obtaining, the estimating and the determining is impacted by the content.

Abstract: A method for passive readout, the method may include (i) obtaining a group of descriptors that were outputted by of one or more neural network layers; wherein descriptors of the group of descriptors comprise a first number (N1) of descriptor elements; and (ii) generating a lossless and sparse representation of the group of descriptors. The generating may include (a) applying a dimension expanding convolution operation on the group of descriptors to provide a group of expanded descriptors; wherein expanded descriptors of the group of expanded descriptors comprises a second number (N2) of expanded descriptor elements, wherein N2 exceeds N1; and (b) quantizing the group of expanded descriptors to provide a group of binary descriptors that form a lossless and a sparse representation of the group of descriptors.

Abstract: A method for operating an ensemble of narrow AI agents related to a vehicle, the method may include obtaining one or more sensed information units; determining, by a perception unit and based on the one or more sensed information units, one or more relevant narrow AI agents of the ensemble that are relevant to a processing of the one or more sensed information units; wherein the ensemble is relevant to a first plurality of scenarios; processing the one or more sensed information units, by the one or more relevant narrow AI agents, to provide one or more narrow AI agent driving decisions; wherein each narrow AI agent is relevant to a respective fraction of the first plurality of scenarios; and processing, by a driving decision unit, the one or more narrow AI agent driving decisions to provide an output driving decision.

Abstract: A method for detecting a state of a motorcycle, the method includes (i) obtaining, by a camera of the motorcycle, one or images of an environment of the vehicle; wherein there is a known angular relationship between the field of view of the camera and predefined part or a vertical axis of the motorcycle; (ii) generating signatures of the one or more images; (iii) searching, using the signatures, multiple groups of concept data structures, for one or more matching concept data structures; wherein different groups of concept data structures represent images virtually acquired at different roll angles; and (iv) determining a roll angle of the motorcycle based on an outcome of the searching and the known angular relationship.

Abstract: A method for managing a group of narrow artificial intelligence (AI) agents for at least partially autonomous driving, the method includes (i) obtaining, by a prediction circuit, a stream of metadata segments generated at multiple points in time and associated with a selection of one or more sub-groups of the group of narrow AI agents; wherein the metadata segments are selected out of (a) selected narrow AI agent identifiers, and (b) multiple multi-domain identifier, the multiple multi-domain identifier are indicative of multiple instances of multi-domain information about elements affecting a vehicle in relation to the multiple points in time; wherein a multi-domain identifier generated at a given point of time of the multiple points in time is a combination of class signatures that are indicative of classes of elements of a multi-domain information associated with the given point in time; (ii) finding, by the prediction circuit, a segment of the stream that is a predictor to a receiving of a next cluster id

Abstract: A method for generating at least one non-human animal crossing indicator, the method may include receiving by a vehicle computerized system, non-human animal crossing indicators; obtaining sensed information regarding an environment of the vehicle; processing the sensed information, wherein the processing comprises searching for one or more non-human animal crossing indicators of the non-human animal crossing indicators; wherein the non-human animal crossing element is selected out of (i) a non-human animal crossing object and (ii) a non-human animal crossing situation; autonomously determining, when finding at least one of the one or more non-human animal crossing identifiers, that the vehicle is driving towards a non-human animal crossing or is within the non-human animal crossing; and generating an alert when determining that the vehicle is driving towards the non-human animal crossing or is within the non-human animal crossing.

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.