Abstract: A method for reducing the computational cost for an autonomous driving system is disclosed. The method may include the following steps: a) acquiring data related to a task for operating a vehicle; b) training a deep learning model using the data acquired, wherein the deep learning model includes an encoder and a policy head for the task; c) reducing a complexity of the data acquired in step a) by passing the data to the encoder to produce a compressed latent representation of the data; and d) determining a driving operation by the policy head using the compressed latent representation of the data.

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 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 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 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 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 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 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 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 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 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.