Abstract: One or more embodiments herein can provide a process to determine dead-reckoning localization of a vehicle. An exemplary system can comprise a memory that stores computer executable components, and a processor that executes the computer executable components stored in the memory, wherein the computer executable components can comprise an obtaining component that obtains plural sensor readings defining movement of a vehicle, wherein the plural sensor readings comprise an inertial sensor reading, a kinematics sensor reading, and an odometry sensor reading, and a generation component that generates, based on the plural sensor readings, a pose value defining a position of the vehicle relative to an environment in which the vehicle is disposed. A sensing sub-system of the exemplary system can comprise an inertial measurement unit sensor, a kinematics sensor, and an odometry sensor.

Abstract: One or more embodiments herein can enable identification of an obstacle free area about an object. An exemplary system can comprise a memory that stores computer executable components, and a processor that executes the computer executable components stored in the memory, wherein the computer executable components can comprise an obtaining component that obtains raw data defining a physical state of an environment around an object from a vantage of the object, and a generation component that, based on the raw data, generates a dimension of a portion or more of a virtual polygon representing a boundary about the object, wherein the boundary bounds free space about the object. A sensing sub-system can comprise both an ultrasonic sensor and a camera that can separately sense the environment about the object from the vantage of the object to thereby generate separate polygon measurement sets.

Abstract: The present disclosure adds automotive millimeter-wave (mmWave) radars to the current perception system and makes them an effective augmentation for ultrasonic sensors (USSs). Relying on the superior range and Doppler resolution, mmWave radars generate denser point clouds than the intersection detections of USSs, making it possible to form a more robust and accurate radar occupancy grid. The radar occupancy grid can be formulated as a polygon with multiple nodes. Thus,, the memory-consuming occupancy grid is simplified as a polygon that consists of a bunch of points, which can be used in the downstream application for relieving computational burden. Radars measure and estimate the Doppler velocity of detected targets such that one can assign a moving velocity to each node of the radar polygon. This makes it possible to predict the shape of a future radar polygon and feed the predicted radar polygon to downstream applications.

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 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: 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: 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.