Abstract: A system includes at least one processor configured to detect, based on point cloud information, portions of a particular object, and determine, based on the detected portions, at least a first portion having a first reflectivity corresponding to a license plate, and at least two additional spaced-apart portions corresponding to locations on the particular object other than a location of the first portion. The at least two additional portions have reflectivity substantially lower than the first reflectivity. The at least one processor is further configured to classify the particular object as a vehicle, based on a spatial relationship and a reflectivity relationship between the first portion and the at least two additional portions.

Abstract: A vehicle-assistance system for classifying objects in a vehicle's surroundings. The system includes: at least one memory configured to store classification information for classifying a plurality of objects; and at least one processor configured to receive a plurality of detection results associated with light detection and ranging system (LIDAR) detection results, each detection result including location information, and further information indicative of at least two of the following detection characteristics: object surface reflectivity; temporal spreading of signal reflected from the object; object surface physical composition; ambient illumination measured at a LIDAR dead time; difference in detection information from a previous frame; and confidence level associated with another detection characteristic.

Abstract: A LIDAR system includes at least one processor configured to control at least one light source for projecting light toward a field of view and receive from at least one first sensor first signals associated with light projected by the at least one light source and reflected from an object in the field of view, wherein the light impinging on the at least one first sensor is in a form of a light spot having an outer boundary. The processor may further be configured to receive from at least one second sensor second signals associated with light noise, wherein the at least one second sensor is located outside the outer boundary; determine, based on the second signals received from the at least one second sensor, an indicator of a magnitude of the light noise; and determine, based on the indicator the first signals received from the at least one first sensor and, a distance to the object.

Abstract: Automatic landlord systems, methods and computer program products for settling debtors' debts following termination or end of a property contract are provided. The systems, methods and computer products interface with the landlord to update a debtor's database, collect in a debtor database various data and conditions relating to the debt of each debtor, and negotiate the debt payment by automatically issuing settlement proposals based on the data, receiving debtors' responses and analyzing them to determine agreements and refusals, the former handled by issuing a release document and the latter by further negotiations or by 3rd party collection. The negotiation may be carried out by generative artificial intelligence (GenAI) algorithms implementing multiple agents to represent multiple sides in the process (communication analysis, market analysis, collection, owners, debtors and negotiations strategies) and thereby optimize the debt collection process.

Abstract: Property managing firm's systems, methods and computer program products are provided, for automatically renewing property lease contracts. Ranking of tenants, units and applicants are derived and ranked with respect to all available information, and multiple models are implemented to derive the tenant likelihood of renewal and an unbiased tenant score; and a market estimator model estimating market demand. A determination unit is configured to integrate the derived parameters to order tenants and applicants by their quality scores and a contract generator engages the prospective new tenants.

Abstract: In some embodiments, a LIDAR system may include at least one processor configured to control at least one light source for projecting light toward a field of view and receive from at least one first sensor first signals associated with light projected by the at least one light source and reflected from an object in the field of view, wherein the light impinging on the at least one first sensor is in a form of a light spot having an outer boundary. The processor may further be configured to receive from at least one second sensor second signals associated with light noise, wherein the at least one second sensor is located outside the outer boundary; determine, based on the second signals received from the at least one second sensor, an indicator of a magnitude of the light noise; and determine, based on the indicator the first signals received from the at least one first sensor and, a distance to the object.

Abstract: In some embodiments, a LIDAR system may include at least one processor configured to control at least one light source for projecting light toward a field of view and receive from at least one first sensor first signals associated with light projected by the at least one light source and reflected from an object in the field of view, wherein the light impinging on the at least one first sensor is in a form of a light spot having an outer boundary. The processor may further be configured to receive from at least one second sensor second signals associated with light noise, wherein the at least one second sensor is located outside the outer boundary; determine, based on the second signals received from the at least one second sensor, an indicator of a magnitude of the light noise; and determine, based on the indicator the first signals received from the at least one first sensor and, a distance to the object.

Abstract: A system includes at least one processor configured to detect, based on point cloud information, portions of a particular object, and determine, based on the detected portions, at least a first portion having a first reflectivity corresponding to a license plate, and at least two additional spaced-apart portions corresponding to locations on the particular object other than a location of the first portion. The at least two additional portions have reflectivity substantially lower than the first reflectivity. The at least one processor is further configured to classify the particular object as a vehicle, based on a spatial relationship and a reflectivity relationship between the first portion and the at least two additional portions.

Abstract: A vehicle-assistance system for classifying objects in a vehicle's surroundings. The system includes: at least one memory configured to store classification information for classifying a plurality of objects; and at least one processor configured to receive a plurality of detection results associated with light detection and ranging system (LIDAR) detection results, each detection result including location information, and further information indicative of at least two of the following detection characteristics: object surface reflectivity; temporal spreading of signal reflected from the object; object surface physical composition; ambient illumination measured at a LIDAR dead time; difference in detection information from a previous frame; and confidence level associated with another detection characteristic.

Abstract: A LIDAR system for use in a vehicle is provided. The LIDAR system may include at least one processor configured to control at least one light source for illuminating a field of view and scan a field of view by controlling movement of at least one deflector at which the at least one light source is directed. The at least one processor may also be configured to receive, from at least one sensor, reflections signals indicative of light reflected from an object in the field of view. The at least one processor may further be configured to detect at least one temporal distortion in the reflections signals, and determine from the at least one temporal distortion an angular orientation of at least a portion of the object.

Abstract: Systems and methods are provided for improving a high-volume manufacturing (HVM) line that has a test pass ratio of at least 90%, by constructing a genetic neural architecture search (GNAS) network that detects anomalies in the HVM line at a detection rate of at least 85%. Disclosed systems and methods combine data balancing of the highly skewed raw data with a network construction that is based on building blocks that reflect technical knowledge related to the HVM line. The GNAS network construction is made thereby both simpler and manageable and provides meaningful insights for improving the production process.

Abstract: Systems and methods are provided for improving a high-volume manufacturing (HVM) line by assessing robustness and performance of an early fault detection machine learning (EFD ML) models. Learning curve(s) may be constructed from a received amount of data from the electronics' production line, the learning curve representing a relation between a performance of the EFD ML model and a sample size of the data on which the EFD ML model is based. Learning curve(s) may be used to derive estimation(s) of model robustness by (i) fitting the learning curve to a power law function and (ii) estimating a tightness of the fitting and/or by (iii) applying a machine learning algorithm that is trained on a given plurality of learning curves and related normalized performance values.

Abstract: A vehicle-assistance system for classifying objects in a vehicle's surroundings is provided. The system may include at least one memory configured to store classification information for classifying a plurality of objects and at least one processor configured to receive, on a pixel-by-pixel basis, a plurality of measurements associated with LIDAR detection results. The measurements may include at least one of: a presence indication, a surface angle, object surface physical composition, and a reflectivity level. The at least one processor may also be configured to receive, on the pixel-by-pixel basis, at least one confidence level associated with each received measurement, and access the classification information. The at least one processor may further be configured to, based on the classification information and the received measurements with the at least one associated confidence level plurality, identify a of pixels as being associated with a particular object.

Abstract: A vehicle-assistance system for classifying objects in a vehicle's surroundings is provided. The system may include at least one memory configured to store classification information for classifying a plurality of objects and at least one processor configured to receive, on a pixel-by-pixel basis, a plurality of measurements associated with LIDAR detection results. The measurements may include at least one of: a presence indication, a surface angle, object surface physical composition, and a reflectivity level. The at least one processor may also be configured to receive, on the pixel-by-pixel basis, at least one confidence level associated with each received measurement, and access the classification information. The at least one processor may further be configured to, based on the classification information and the received measurements with the at least one associated confidence level plurality, identify a of pixels as being associated with a particular object.

Abstract: In some embodiments, a LIDAR system may include at least one processor configured to control at least one light source for projecting light toward a field of view and receive from at least one first sensor first signals associated with light projected by the at least one light source and reflected from an object in the field of view, wherein the light impinging on the at least one first sensor is in a form of a light spot having an outer boundary. The processor may further be configured to receive from at least one second sensor second signals associated with light noise, wherein the at least one second sensor is located outside the outer boundary; determine, based on the second signals received from the at least one second sensor, an indicator of a magnitude of the light noise; and determine, based on the indicator the first signals received from the at least one first sensor and, a distance to the object.

Abstract: A LIDAR system may include a processor configured to control a LIDAR light source for illuminating a field of view (FOV), receive, from a group of light detectors, input signals indicative of reflections of light from objects in the FOV, and process a first subset of the input signals associated with a first region of the FOV to detect a first object in the first region. The processor may also process a second subset of the input signals associated with a second region of the FOV to detect a second object in the second region. The second object may be located at a greater distance from the light source than the first object.

Abstract: A LIDAR system for use in a vehicle is provided. The LIDAR system may include at least one processor configured to control at least one light source for illuminating a field of view and scan a field of view by controlling movement of at least one deflector at which the at least one light source is directed. The at least one processor may also be configured to receive, from at least one sensor, reflections signals indicative of light reflected from an object in the field of view. The at least one processor may further be configured to detect at least one temporal distortion in the reflections signals, and determine from the at least one temporal distortion an angular orientation of at least a portion of the object.

Abstract: A vehicle-assistance system for classifying objects in a vehicle's surroundings is provided. The system may include at least one memory configured to store classification information for classifying a plurality of objects and at least one processor configured to receive, on a pixel-by-pixel basis, a plurality of measurements associated with LIDAR detection results. The measurements may include at least one of: a presence indication, a surface angle, object surface physical composition, and a reflectivity level. The at least one processor may also be configured to receive, on the pixel-by-pixel basis, at least one confidence level associated with each received measurement, and access the classification information. The at least one processor may further be configured to, based on the classification information and the received measurements with the at least one associated confidence level plurality, identify a of pixels as being associated with a particular object.

Abstract: A LIDAR system is provided. The LIDAR system may include at least one processor configured to: control at least one light source in a manner enabling light flux of light from at least one light source to vary over a scan of a field of view; control at least one deflector to deflect light from the at least one light source in order to scan the field of view; receive from at least one sensor information indicative of ambient light in the field of view; identify in the received information an indication of a first portion of the field of view with more ambient light than in a second portion of the field of view; and alter a light source parameter such that when scanning the field of view, light flux of light projected toward the first portion of the field of view is greater than light flux of light projected toward the second portion of the field of view.

Abstract: A LIDAR system is provided. The LIDAR system comprises at least one processor configured to: control at least one light source in a manner enabling light flux to vary over a scan of a field of view, the field of view including a first portion and a second portion; receive on a pixel-by-pixel basis, signals from at least one sensor; estimate noise in at least some of the signals associated with the first portion of the field of view; alter a sensor sensitivity for reflections associated with the first portion of the field of view; estimate noise in at least some of the signals associated with the second portion of the field of view; and alter a sensor sensitivity for reflections associated with the second portion of the field of view based on the estimation of noise in the second portion of the field of view.