Abstract: A LIDAR-based method of determining an absolute speed of an object at a relatively longer distance from an ego vehicle, including: estimating a self speed of the ego vehicle using a first frame t-1 and a second frame t obtained from a LIDAR sensor by estimating an intervening rotation ? about a z axis and translation in orthogonal x and y directions using a deep learning algorithm over a relatively closer distance range; dividing each of the first frame t-1 and the second frame t into multiple adjacent input ranges and estimating a relative speed of the object at the relatively longer distance by subsequently processing each frame using a network, with each input range processed using a corresponding convolutional neural network; and combining the estimation of the estimating the self speed with the estimation of the estimating the relative speed to obtain an estimation of the absolute speed.

Abstract: A camera mount assembly for holding a camera or other perception sensor that may be fixed with respect to a ground plane and used for incremental angle measurement of the camera or other perception sensor in three axes, such that the precise spatial relationships of imaged objects with respect to the orientation/position of the camera or other perception sensor may be determined. A base structure of the camera mount assembly may be affixed to a fixed or moveable support structure and multiple leveling devices and/or laser devices are provided to establish a reliable reference relative to the ground plane. Thus, the exact orientation of the camera or other perception sensor in space may be determined frame to frame, such that the precise orientation of imaged objects with respect to the orientation/position of the camera or other perception sensor relative to the ground plane may be determined over time.

Abstract: A cross-range 3D object detection method and system operable for training a 3D object detection model with N sub-groups of a point cloud corresponding to N detection distance ranges to form N 3D object detection models forming an ensemble 3D object detection model. Training the 3D object detection model with the N sub-groups of the point cloud corresponding to the N detection distance ranges includes training the 3D object detection model progressively from distant to near. Training the 3D object detection model with the N sub-groups of the point cloud corresponding to the N detection distance ranges includes, each time the 3D object detection model converges, saving resulting weights and adding a corresponding network to the ensemble 3D object detection model.

Abstract: The present disclosure provides a system and method for removing noise from an ultrasonic signal using a generative adversarial network (GAN). The present disclosure provides three input formats for the neural network (NN) in order to feed one-dimensional (1D) input data to the network. The system is generalizable to multiple noise sources, as it learns from different motion functions and noise types. The end-to-end system of the present disclosure is trained on raw ultrasonic signals with very little pre-processing or feature extraction.

Abstract: The present disclosure provides an in-vehicle music system and method that leverages current external cameras, interior cameras, interior microphones, global positioning system (GPS) and navigation maps, artificial intelligence (AI) systems, and the like to provide driving pace-adapted music, external location, scene, weather, and road condition-adapted music, interior noise-adapted music, driver mood-adapted music, and big data-trained personalized playlists taking these functionalities into account.

Abstract: A camera mount assembly for holding a camera or other perception sensor that may be fixed with respect to a ground plane and used for incremental angle measurement of the camera or other perception sensor in three axes, such that the precise spatial relationships of imaged objects with respect to the orientation/position of the camera or other perception sensor may be determined. A base structure of the camera mount assembly may be affixed to a fixed or moveable support structure and multiple leveling devices and/or laser devices are provided to establish a reliable reference relative to the ground plane. Thus, the exact orientation of the camera or other perception sensor in space may be determined frame to frame, such that the precise orientation of imaged objects with respect to the orientation/position of the camera or other perception sensor relative to the ground plane may be determined over time.

Abstract: An apparatus and method for performing environment perception is described. An example technique may include receiving a point cloud from a sensor, such as a LiDAR sensor, the point cloud including a plurality of points representing positions of objects relative to the LiDAR sensor. The example techniques may further include dividing the point cloud into a plurality of distances ranges, processing the points in each distance range of the point cloud with a different section of a plurality of sections of a piece-wise network structure, and outputting environment perception data from the piece-wise network structure.

Abstract: A method and apparatus for annotating point cloud data. An apparatus may be configured to cause display of the point cloud data, label points in the point cloud data with a plurality of annotation points, the plurality of annotation points corresponding to points on a human body, move, in response to a user input, one or more of the annotation points to define a human pose and create annotated point cloud data, and output the annotated point cloud data.

Abstract: An on-board vehicle system and method for camera or sensor-based parking spot detection and identification is provided. This system and method utilizes a standard front (or side or rear) camera or sensor image to detect and identify one or more parking spots at a distance via vector or like representation using a deep neural network trained with data annotated using an annotation tool, without first transforming the standard camera or sensor image(s) to a bird's-eye-view (BEV) or the like. The system and method can be incorporated in a driver-assist (DA) or autonomous driving (AD) system.

Abstract: A system for providing in-vehicle emergency vehicle detection and positional alerts, including: a camera configured to obtain an image of surroundings of an ego vehicle; an emergency vehicle recognition and localization module operable for segmenting an emergency vehicle from the image of the surroundings in order to detect and locate the emergency vehicle relative to the ego vehicle; a microphone configured to obtain an auditory signal from the surroundings of the ego vehicle; a siren detection and directional positioning module operable for discriminating an emergency vehicle siren from the auditory signal from the surroundings in order to detect and locate the emergency vehicle relative to the ego vehicle; and one or more of a visual alert, an audible alert, and a haptic alert operable for alerting a driver of the ego vehicle to a presence and the location of the emergency vehicle.

Abstract: A method and system by which a bounding box disposed around a segmented object in a camera (or other perception sensor) 2D image can be used to produce an estimate for both the location of the object—its position relative to the position of the camera that obtained the image (i.e., translation)—and the angle of rotation of the surface that the object is located on. The method and system may be used by an advanced driver assistance system (ADAS), an autonomous driving (AD) system, or the like. The input includes a simple camera (or other perception sensor) 2D image, with the ego vehicle generating 2D or 3D bounding boxes for objects detected at the scene. The output includes, for each object, its estimated distance from the ego vehicle camera/perception sensor and the angle of rotation of the surface underneath the object relative to the surface underneath the ego vehicle.

Abstract: Methods and systems for digital image segmentation and annotation, including: receiving a digital image depicting, in part, an object of interest from an input file; one or more of manually and automatically adding a polygon around the object of interest to generate a segmented digital image; one or more of manually and automatically appending a label to the polygon around the object of interest to generate a segmented and annotated digital image, wherein the label indicates one or more of an identity and a characteristic of the object of interest; and outputting information related to the segmented and annotated digital image to an output file. Optionally, the polygon is one of a bounding box and a 4-point polygon. Optionally, the object of interest is a parking spot.

Abstract: A method performed by a vehicle system for handling images of the surroundings of a vehicle. An image of the surroundings of the vehicle is obtained. The image is obtained from at least one side image capturing device mounted in or on the vehicle, and the image capturing device comprises a fisheye camera lens. At least a part of the distortions in the image is corrected to obtain a corrected image. The corrected image is rotationally transformed using a first rotational transformation to obtain a first transformed image. The corrected image is rotationally transformed using a second rotational transformation to obtain a second transformed image. The first and second transformed images are consecutive or adjacent images.

Abstract: An apparatus and method for estimating the pose of one or more persons is described. An example technique may include receiving a point cloud from a LiDAR sensor, the point cloud including a plurality of points representing positions of objects relative to the LiDAR sensor, processing the point cloud to produce a voxelized frame including a plurality of voxels, processing the voxelized frame using a deep neural network to determine one or more persons relative to the LiDAR sensor and a pose for each of the one or more persons, and outputting a location of the determined one or more persons and the pose for each of the determined one or more persons.

Abstract: A LIDAR-based method of determining an absolute speed of an object at a relatively longer distance from an ego vehicle, including: estimating a self speed of the ego vehicle using a first frame t-1 and a second frame t obtained from a LIDAR sensor by estimating an intervening rotation ? about a z axis and translation in orthogonal x and y directions using a deep learning algorithm over a relatively closer distance range; dividing each of the first frame t-1 and the second frame t into multiple adjacent input ranges and estimating a relative speed of the object at the relatively longer distance by subsequently processing each frame using a network, with each input range processed using a corresponding convolutional neural network; and combining the estimation of the estimating the self speed with the estimation of the estimating the relative speed to obtain an estimation of the absolute speed.

Abstract: A parking availability map system and method, including: a sensor-based parking space detection system coupled to a vehicle and operable for detecting and identifying open and occupied parking spaces within a predetermined vicinity of the vehicle; and an application server disposed remotely from the vehicle and executing an algorithm operable for receiving open and occupied parking space data from the sensor-based parking space detection system, receiving a parking space availability query from a user disposed remotely from the server, and, responsive to the parking space availability query, transmitting parking space availability information to the user. Optionally, the sensor-based parking space detection system includes a camera-based parking space detection system.

Abstract: An apparatus and method for performing environment perception is described. An example technique may include receiving a point cloud from a sensor, such as a LiDAR sensor, the point cloud including a plurality of points representing positions of objects relative to the LiDAR sensor. The example techniques may further include dividing the point cloud into a plurality of distances ranges, processing the points in each distance range of the point cloud with a different section of a plurality of sections of a piece-wise network structure, and outputting environment perception data from the piece-wise network structure.

Abstract: Parking spot detection methods and systems that detect and pair parking line markers, improving the accuracy and speed of parking spot detection in vehicular driver assist (DA) and autonomous driving (AD) applications. These methods and systems incorporate three major steps: (1) preprocessing—one or more standard camera images are input into the algorithm and a bird's-eye-view (BEV) image is output from the algorithm; (2) deep neural network (DNN) segmentation of the parking line markers—the BEV image is input into the algorithm and a binary image is output from the algorithm (with, e.g., the parking line markers areas displayed in white and the background displayed in black); and (3) binary image processing—the binary image is input into the algorithm and detected and paired parking line markers representing the parking spots are output from the algorithm.

Abstract: A method performed by a vehicle system for handling images of surroundings of a vehicle. An image of surroundings of the vehicle is obtained. The image is obtained from at least one image capturing device mounted in or on the vehicle, and the image capturing device comprises a fisheye lens. At least a part of distortions in the image is corrected to obtain a corrected image. The corrected image is rotationally transformed using a first rotational transformation to obtain a first transformed image. The corrected image is rotationally transformed using a second rotational transformation to obtain a second transformed image. The first and second rotational transformations are different from each other, and the first and second transformed images are consecutive images.

Abstract: An on-board vehicle system and method for camera or sensor-based parking spot detection and identification is provided. This system and method utilizes a standard front (or side or rear) camera or sensor image to detect and identify one or more parking spots at a distance via vector or like representation using a deep neural network trained with data annotated using an annotation tool, without first transforming the standard camera or sensor image(s) to a bird's-eye-view (BEV) or the like. The system and method can be incorporated in a driver-assist (DA) or autonomous driving (AD) system.