Qualcomm Incorporated

Volodimir Slobodyanyuk

Mohammed Ataur Rahman Shuman

Sundar Subramanian

Makesh Pravin John Wilson

Arnold Jason Gum

Radhika Dilip Gowaikar

Amin Ansari

Evgeni Petrovich Gousev

Amit Goel

Karim Arabi

John Michael Wyrwas

Michael John Hamilton

Manav Raina

Kalin Mitkov Atanassov

Stephen Michael Verrall

Albrecht Johannes Lindner

Avdhut Joshi

Arunandan Sharma

Shantanu Chaisson Sanyal

Ahmed Kamel Sadek

Dan Vassilovski

Matthew Stuart Grob

Russell Wayne Gruhlke

Roberto Rimini

Richard Martin

Udara Fernando

Jonathan Petit

Rajesh K Pankaj

Donald Hutson

Christopher Brunner

Daniel Keyes Butterfield

Tadeusz Jarosinski

Sajith Balraj

Mohammed Ataur Shuman

Elbert McLaren

Nitin Jonathan Myers

Vijay Varadarajan

Behnaz Rezaei

Donald Bolden Hutson

Richard George Martin

John Wyrwas

In some aspects, a device may receive sensor data associated with a vehicle and a set of frames. The device may aggregate, using a first pose, the sensor data associated with the set of frames to generate an aggregated frame, wherein the aggregated frame is associated with a set of cells. The device may obtain an indication of a respective occupancy label for each cell from the set of cells, wherein the respective occupancy label includes a first occupancy label or a second occupancy label, and wherein a subset of cells from the set of cells are associated with the first occupancy label. The device may train, using data associated with the aggregated frame, a machine learning model to generate an occupancy grid, based on a loss function that only calculates a loss for respective cells from the subset of cells. Numerous other aspects are described.

Machine learning based occupancy grid generation

In some aspects, a device may receive, from a radar scanner or a LIDAR scanner of a vehicle, point data that identifies a first point and a second point. The device may receive grid information that identifies cells of a grid that is associated with mapping a physical environment of the vehicle. The device may designate, based on determining that a distance between the first point and the second point satisfies a distance threshold, a subset of the cells as an occupied cluster that is associated with the first point and the second point. The device may perform an action associated with the vehicle based on location information associated with the occupied cluster. Numerous other aspects are described.

Occupancy clustering according to radar data

A device for processing image data is disclosed. The device can obtain a radar point cloud and one or more frames of camera data. The device can determine depth estimates of one or more pixels of the one or more frames of camera data. The device can generate a pseudo lidar point cloud using the depth estimates of the one or more pixels of the one or more frames of camera data, wherein the pseudo lidar point cloud comprises a three-dimensional representation of at least one frame of the one or more frames of camera data. The device can determine one or more object bounding boxes based on the radar point cloud and the pseudo lidar point cloud.

Radar and camera data fusion

In some aspects, a system may receive, from a first one-dimensional radar array, first information based at least in part on first reflections associated with an azimuthal plane. The system may further receive, from a second one-dimensional radar array, second information based at least in part on second reflections associated with an elevation plane. Accordingly, the system may detect an object based at least in part on the first information and may determine an elevation associated with the object based at least in part on the second information. Numerous other aspects are described.

Object detection and ranging using one-dimensional radar arrays

The present disclosure generally relates to an object detection system. For example, aspects of the present disclosure relate to systems and techniques for performing object detection using sensor information, such as elevation and/or velocity information from one or more light-based sensors. One example apparatus generally includes one or more processors operably configured to: obtain sensor information indicating at least two objects in an environment; determine at least one of a velocity or an elevation associated with each object of the at least two objects; consolidate the at least two objects into a common object based on the at least one of the velocity or the elevation; and output an indication of the common object.

Sensor based object detection

According to some aspects of the disclosure, techniques for compression techniques for the radar data that can be used in real-time applications for automated or self-driving vehicles. One or more compression techniques can be selected and/or configured based on information regarding operational conditions provided by a central (vehicle) computer. Operational conditions can include environmental data (e.g., weather, traffic), processing capabilities, mode of operation, and more. Compression techniques can facilitate transport of compressed radar data from a radar sensor to the central computer for processing of the radar data for object detection, identification, positioning, etc.

Techniques for radar data compression

In some aspects, a device may receive point data associated with a cell of an occupancy grid for controlling a vehicle. The device may determine, based on the point data, a characteristic of the cell that is associated with an occupancy probability of the cell, wherein the occupancy probability is determined according to a first technique based on the point data. The device may configure, based on the characteristic, the occupancy probability for the cell, within the occupancy grid, according to a second technique. Numerous other aspects are described.

Occupancy mapping for autonomous control of a vehicle

A method includes, by a mobile device, receiving a Global Navigation Satellite System (GNSS) signal, and receiving, from a wireless device, via a PC5 interface, a message including a location of a reference structure. The method also includes determine whether the GNSS signal is a spoofing signal based on a difference between the location of the reference structure and a location of the mobile device determined based on the GNSS signal.

GNSS spoofing detection and recovery

Techniques are discussed herein for detecting anomalous signals such as spoofed satellite positioning system (SPS) signals and for the transmission of accurate location estimates between user equipments (UEs) when the SPS signals are not reliable. A UE determines an SPS derived location estimate and determines an associated confidence level. The confidence level is determined based on time or location derived from the SPS signals, e.g., relative to local time or non-SPS information, such as stored previous location estimates, non-SPS sensor information, and location information from other UEs. The UE transmits location information to other UEs that includes a selected location estimate, confidence level, and the source of the location estimate, e.g., where the SPS derived location estimate is selected if the confidence level is high and the non-SPS derived location estimate is selected if the confidence level is low.

Positioning when SPS information is spoofed

An electronic imaging device and method for image capture are described. The imaging device includes a camera configured to obtain image information of a scene and that may be focused on a region of interest in the scene. The imaging device also includes a LIDAR unit configured to obtain depth information of at least a portion of the scene at specified scan locations of the scene. The imaging device is configured to detect an object in the scene and provides specified scan locations to the LIDAR unit. The camera is configured to capture an image with an adjusted focus based on depth information, obtained by the LIDAR unit, associated with the detected object.

Systems and methods for depth map sampling

Techniques for determining an alternative communication mode for vehicle-to-vehicle communication at a host vehicle can include monitoring the primary mode of RF communication to ensure it is effectively communicating and, if not, intelligently selecting a backup communication mode comprising one or more other sensors and/or systems of the vehicle. The selection of the backup communication mode may take into account various factors that can affect the various modes of communication from which the backup communication mode is selected.

Selection and use of backup communication mode for vehicle-to-vehicle messaging

Embodiments provided herein allow for identification of one or more regions of interest in a radar return signal that would be suitable for selected application of super-resolution processing. One or more super-resolution processing techniques can be applied to the identified regions of interest. The selective application of super-resolution processing techniques can reduce processing requirements and overall system delay. The output data of the super-resolution processing can be provided to a mobile computer system. The output data of the super-resolution processing can also be used to reconfigure the radar radio frequency front end to beam form the radar signal in region of the detected objects. The mobile computer system can use the output data for implementation of deep learning techniques. The deep learning techniques enable the vehicle to identify and classify detected objects for use in automated driving processes. The super-resolution processing techniques can be implemented in analog and/or digital circuitry.

Super-resolution enhancement techniques for radar

Aspects of the disclosure are related to a Lidar device, comprising: a vibrating fiber optic cantilever system on a transmit (TX) path; and a two-dimensional (2D) light sensor array on a receive (RX) path.

Implementation of the focal plane 2D APD array for hyperion Lidar system

Methods and apparatus for providing dynamically adjusted radiated signals are disclosed. In one aspect, a method of detecting one or more objects in a path of travel of a vehicle may include generating a laser with radiated power. The method may further include emitting the laser in a direction of travel of the vehicle and receiving one or more reflections of the emitted laser reflected from the one or more objects located in the direction of travel of the vehicle. The method may also further include generating a signal indicating that the one or more objects are in a path of the vehicle based on the received one or more reflections. The method may also include dynamically adjusting the radiated power of the laser based on an input corresponding to one or more of (i) a current speed of the vehicle or (ii) a current position of the vehicle.

Methods, systems, and apparatus for dynamically adjusting radiated signals

Various aspects of the present disclosure generally relate to vehicle sensors. In some aspects, a device associated with a vehicle may obtain, from an external source, calibration data including a first set of measurements related to a position of one or more objects in a reference frame associated with the external source. The device may identify a second set of measurements related to the position of the one or more objects within a field of view of one or more onboard sensors, and update a calibration table based at least in part on a comparison of one or more of the first set of measurements and one or more of the second set of measurements that are most time-aligned. Accordingly, the device may convert sensor-derived location information from a source reference frame to a reference frame associated with the vehicle using the updated calibration table. Numerous other aspects are provided.

Growing community of inventors

San Diego, CA, United States of America

Volodimir Slobodyanyuk