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 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: 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: 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.

Abstract: A LIDAR system for use with a roadway vehicle traveling on a highway 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 scanning cycle of a field of view. The processor may also be configured to control at least one deflector to deflect light from the at least one light source in order to scan the field of view. The processor may also be configured to coordinate the control of the at least one light source with the control of the at least one light deflector such that during scanning of the field of view that encompasses a central region, a right peripheral region, and a left peripheral region, more light is directed to the central region than to the peripheral regions.

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 scans of a field of view using light from the at least one light source; control at least one light deflector to deflect light from the at least one light source; receive from at least one sensor, reflections signals indicative of light reflected from objects in the field of view; determine, based on the reflections signals of an initial light emission, whether an object is located in an immediate area of the LIDAR system and within a threshold distance from the at least one light deflector, and when no object is detected in the immediate area, control the at least one light source such that an additional light emission is projected toward the immediate area.

Abstract: A LIDAR system for use in a vehicle may include at least one processor configured to control at least one light source in a manner enabling light flux of at least one light source to vary over scans of a field of view. The processor may also be configured to control at least one light deflector to deflect light from the at least one light source in order to scan the field of view. The processor may also be configured to receive input indicative of a current driving environment of the vehicle, and based on the current driving environment, coordinate the control of the at least one light source with the control of the at least one light deflector to dynamically adjust an instantaneous detection distance by varying an amount of light projected and a spatial light distribution of light across the scan 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 of light from at least one light source to vary over a scanning cycle of a field of view; receive from at least one sensor reflections signals indicative of light reflected from objects in the field of view; coordinate light flux and scanning in a manner to cause at least three sectors of the field of view to occur in a scanning cycle, a first sector having a first light flux and an associated first detection range, a second sector having a second light flux and an associated second detection range, and a third sector having third light flux and an associated a third detection range; and detect an object in the second sector.

Abstract: A LIDAR system is provided. The LIDAR system comprises at least one processor configured to: access an optical budget stored in memory, the optical budget being associated with at least one light source and defining an amount of light that is emittable in a predetermined time period by the at least one light source; receive information indicative of a platform condition for the LIDAR system; based on the received information, dynamically apportion the optical budget to a field of view of the LIDAR system based on at least two of: scanning rates, scanning patterns, scanning angles, spatial light distribution, and temporal light distribution; and output signals for controlling the at least one light source in a manner enabling light flux to vary over scanning of the field of view in accordance with the dynamically apportioned optical budget.

Abstract: A LIDAR system for use in a vehicle may include at least one processor configured to control at least one light source in a manner enabling light flux of light to vary over a scanning cycle of a field of view. The processor may also be configured to control at least one deflector to deflect light to scan the field of view. The processor may also be configured to obtain input indicative of an impending cross-lane turn of the vehicle, and in response, coordinate the control of the at least one light source with the control of the at least one light deflector to increase light flux on a side of the vehicle opposite a direction of the cross-lane turn, causing a detection range opposing the direction of the cross-lane turn of the vehicle to temporarily exceed a detection range toward a direction of the cross-lane turn.

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 intensity to vary over a scan of a field of view using light from the at least one light source; control at least one light deflector to deflect light from the at least one light source; obtain an identification of at least one distinct region of interest in the field of view; and increase light allocation to the at least one distinct region of interest relative to other regions, such that following a first scanning cycle, light intensity in at least one subsequent second scanning cycle at locations associated with the at least one distinct region of interest is higher than light intensity in the first scanning cycle at the locations associated with the at least one distinct region of interest.

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 of at least one light source to vary over a plurality of scans of a field of view, the field of view including a near-field portion and a far-field portion; control at least one light deflector to deflect light from the at least one light source in a manner scanning the field of view; implement a first scanning rate for first frames associated with scanning cycles that cover the near-field portion and a second scanning rate for second frames associated with scanning cycles that cover the far-field portion; and control the at least one light source to alter a light source parameter and thereby project light in a manner enabling detection of objects in the second frames associated with the far-field portion.