Abstract: A method that includes (a) monitoring a physiological state of a driver to provide physiological state information; (b) receiving or generating an indication about a dangerous event; (c) searching for a predicting physiological parameter that is associated with a predicting pattern that was indicative of an occurrence of the dangerous event; and (d) responding to the when finding of the predicting physiological parameter.

Abstract: A method for image processing at different illumination conditions, the method may include acquiring an image of an environment of a vehicle; selecting a set of pixels located within a region of interest that is located at an upper part of the image; calculating an illumination condition indicator based on values of the set of pixels; selecting a selected machine learning process, out of a machine learning processes, based on the illumination condition indicator; wherein different machine learning processes are trained to different illumination conditions; and processing the image by the selected machine learning process to provide processing results.

Abstract: A method for driving a first vehicle based on information received from a second vehicle, the method may include receiving, by the first vehicle, acquired image information regarding (a) a signature of an acquired image that was acquired by the second vehicle, (b) a location of acquisition of the acquired image; extracting, from the acquired image information, information about objects within the acquired image; and preforming a driving related operation of the first vehicle based on the information about objects within the acquired image.

Abstract: An autonomous driving system for a vehicle comprising: an I/O module operative to communicate with an obstacle avoidance server; at least one sensor operative to provide at least an indication of an obstacle in a path of the vehicle; processing circuitry; and an autonomous driving manager to be executed by the processing circuitry and operative to: detect the at least an indication of an obstacle based on data provided by the at least one sensor, drive the vehicle in accordance with a driving policy associated with the obstacle, and send an obstacle report with obstacle information associated with the at least an indication of an obstacle to the obstacle avoidance server.

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 bump alert, the method may include receiving by a vehicle computerized system, at least one visual bump indicator that is visible before driving over at least one bump; obtaining sensed information regarding an environment of the vehicle; processing the sensed information, wherein the processing comprises searching a visual bump indicator of the at least one visual bump indicator; determining whether the vehicle approaches a bump; and generating the bump alert when determining that the vehicle approaches the bump.

Abstract: A method for pedestrian crossing alert, the method may include receiving, by a vehicle computerized system, pedestrian crossing indicators; obtaining sensed information regarding an environment of the vehicle; processing the sensed information, wherein the processing comprises searching for at least one of the pedestrian crossing indicators; autonomously determining, when finding the at least one of the pedestrian crossing identifiers, that the vehicle is approaching a pedestrian crossing; and generating an alert when determining that the vehicle is approaching the pedestrian crossing.

Abstract: A method for detecting obstacles, the method may include receiving, from a plurality of vehicles, and by an I/O module of a computerized system, visual information acquired during executions of vehicle maneuvers that are suspected as being obstacle avoidance maneuvers; determining, based at least on the visual information, at least one visual obstacle identifier for visually identifying at least one obstacle; and transmitting to one or more of the plurality of vehicles, the at least one visual obstacle identifier.

Abstract: A method for detecting allowable street parking slots, the method may include (i) receiving, by a computerized allowable street parking slot locating (CASPSL) system, street static vehicles information from multiple vehicles; wherein the street static vehicle information is indicative of (a) locations of static vehicles that are located at least partially within one or more streets, (b) relationship information indicative of spatial relationships between the static vehicles and the one or more street borders; and (c) timing information regarding timings of sensing of the static vehicles; and (ii) determining, by the CASPSL system, based on the street static vehicle information, allowable street parking slots metadata indicative of (a) locations of allowable street parking slots, and (b) time windows of allowed parking in the allowable street parking slot, and (c) spatial relationships between the static vehicles and the one or more street borders.

Abstract: A method for determining a location of a vehicle, the method may include receiving reference visual information that represents multiple reference images acquired at predefined locations; acquiring, by a visual sensor of the vehicle, an acquired image of an environment of the vehicle; generating, based on the acquired image, acquired visual information related to the acquired image, wherein the acquired visual information comprises acquired static visual information that is related to the environment of the vehicle; searching for a selected reference image out of the multiple reference images, the selected reference image comprises selected reference static visual information that best matches the acquired static visual information; and determining an actual location of the vehicle based on a predefined location of the selected reference image and to a relationship between the acquired static visual information and to the selected reference static visual information; and wherein the determining of the actual

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 assisting in turning a vehicle, the method may include detecting or estimating that the vehicle is about to turn to a certain direction or is turning to the certain direction; sensing a relevant portion of an environment of the vehicle to provide sensed information, wherein the relevant portion of the environment is positioned at a side of the vehicle that corresponds with the certain direction; applying an artificial intelligence process on the sensed information to (i) detect objects within the relevant portion of the environment and (ii) estimate expected movement patterns of the objects within a time frame that ends with an expected completion of the turn of the vehicle; determining, given an expected trajectory of the vehicle during the turn and the expected movement patterns of the objects, whether at least one of the objects is expected to cross the trajectory of the vehicle during the turn; and responding to an outcome of the determining.

Abstract: A method for tracking after an entity, the method may include tracking, by a monitor of a vehicle, a movement of an entity that appears in various images acquired during a tracking period; generating, by a processing circuitry of the vehicle, an entity movement function that represents the movement of the entity during the tracking period; generating, by the processing circuitry of the vehicle, a compressed representation of the entity movement function; and responding to the compressed representation of the entity movement function.

Abstract: A method for detecting obstacles, the method may include receiving, from a plurality of vehicles, and by an I/O module of a computerized system, visual information acquired during executions of vehicle maneuvers that are suspected as being obstacle avoidance maneuvers; determining, based at least on the visual information, at least one visual obstacle identifier for visually identifying at least one obstacle; and transmitting to one or more of the plurality of vehicles, the at least one visual obstacle identifier.

Abstract: A method for driving a vehicle based on local driving patterns, the method may include receiving or generating local driving pattern information about driving patterns applied by different drivers at a certain location when facing different situations; when at the certain location, sensing a vicinity of the vehicle to provide sensed information; processing the sensed information to detect a current situation; determining, based on the local driving pattern information, at least one local driving pattern that should be applied when facing the current situations; and determining at least one driving scheme based on the at least one local driving pattern.

Abstract: A method that may include (a) feeding multiple tagged media units to a neural network to provide, from one or more intermediate layers of the neural network, multiple feature vectors of segments of the media units; wherein the neural network was trained to detect current objects within media units; wherein the new category differs from each one of the current categories; wherein at least one media unit comprises at least one segment that is tagged as including the new object; (b) calculating similarities between the multiple feature vectors; (c) clustering the multiple feature vectors to feature vector clusters, based on the similarities; and (d) finding, out of the feature vector clusters, a new feature vector cluster that identifies media unit segments that comprise the new object.

Abstract: A method for detecting fallen 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 fallen cargo and to classify the fallen cargo to fallen cargo classes; estimating, from the sensed information, an impact of at least some of the fallen cargo classes on a behavior of at least some of the multiple vehicles; and determining, based on the impact, at least one suggested vehicle behavior as a response to a detection of at least some of the fallen cargo classes.

Abstract: A method for detecting an improperly driven vehicle, that may include detecting, based on information sensed by at least one sensor of a monitoring vehicle, a behavior of a monitored vehicle; determining whether the behavior of the monitored vehicle is an improper behavior; generating, by a processing unit of the monitoring vehicle, an improper vehicle label that comprises a unique vehicle identifier that identifies the monitored vehicle, when the behavior of the monitored vehicle is determined to be improper; and sending the improper vehicle label to at least one shared database accessible to other vehicles

Abstract: In one embodiment, an automotive prediction system includes a processing circuitry to obtain labels labelling media content elements identified in road-scene media content sequences, each label including a content descriptor selected from different content descriptors describing at least one media content element, the different content descriptors including a first and second content descriptor, calculate a correlation of the first and second content descriptor based on a count of occurrences of the first content descriptor being used for labelling after, but within a given temporal distance of the first content descriptor being used for labelling in the road-scene media content sequences, and populate an automotive prediction database with the correlation of the first and second content descriptor for use in making decisions during driving of a vehicle. Related apparatus and methods are also described.

Abstract: A method for estimating a potential blind spot event related to a potential blind spot of a first vehicle, the method may include detecting, using at least one sensor of a second vehicle, the first vehicle and a spatial relationship between the first vehicle and the second vehicle; estimating, based on at least one blind spot parameter of the first vehicle, an occurrence of the potential blind spot event; wherein the potential blind spot event occurs when the second vehicle is within a potential blind spot of the first vehicle or is about to enter the potential blind spot of the first vehicle; and responding to the estimated occurrence of the potential blind spot event.