Ford Global Technolgoies, LLC

Apple Inc.

Sneha Kadetotad

Ashley Elizabeth Micks

Jinesh J Jain

Harpreetsingh Banvait

Scott Vincent Myers

Marcos Paul Gerardo Castro

Brielle Reiff

Behrooz Shahsavari

Eugene Antony Whang

Benjamin Andrew Shaffer

Vidya Nariyambut Murali

Jared Mitchell Kole

Derek William Wright

Weiyu Huang

Venkatapathi Raju Nallapa

Edward Siahaan

Esge Brandt Andersen

Adil Nizam Siddiqui

Onur I Ilkorur

Daniel Ray Bloom

Lee M Panecki

Dongran Liu

Baboo V Gowreesunker

Jacob E Mattingley

Dustin A Hatfield

Phillip Qian

Ashok E Rodrigues

Yuta Kuboyama

Peter Gyumyeong Joh

Tian Shi Li

Rajesh Anantharaman

Daniel Levine

Robert D Zupke

Christopher S Erickson

Tsu-Hui Lin

Edwin J Corona Aparicio

Audrey L Sheng

Axit H Patel

Alex C Hellwig

Mike B Minerbi

Oliver M Hewitt

Jason J Leblanc

Sean J Docherty

Miikka O Tikander

A controller receives outputs from a plurality of sensors such as a camera, LIDAR sensor, RADAR sensor, and ultrasound sensor, which may be rearward facing. A probability is updated each time a feature in a sensor output indicates presence of an object. The probability may be updated as a function of a variance of the sensor providing the output and a distance to the feature. Where the variance of a sensor is directional, directional probabilities may be updated according to these variances and the distance to the feature. If the probability meets a threshold condition, actions may be taken such as a perceptible alert or automatic braking. The probability may be decayed in the absence of detection of objects. Increasing or decreasing trends in the probability may be amplified by further increasing or decreasing the probability.

Multi-sensor probabilistic object detection and automated braking

A method for generating training data is disclosed. The method may include executing a simulation process. The simulation process may include traversing a virtual, forward-looking sensor over a virtual road surface defining at least one virtual railroad crossing. During the traversing, the virtual sensor may be moved with respect to the virtual road surface as dictated by a vehicle-motion model modeling motion of a vehicle driving on the virtual road surface while carrying the virtual sensor. Virtual sensor data characterizing the virtual road surface may be recorded. The virtual sensor data may correspond to what a real sensor would have output had it sensed the road surface in the real world.

Virtual sensor-data-generation system and method supporting development of algorithms facilitating navigation of railway crossings in varying weather conditions

This disclosure includes several different features suitable for use in circumaural and supra-aural headphones designs. Designs that include earpad assemblies that improve acoustic isolation are discussed. User convenience features that include automatically detecting the orientation of the headphones on a user's head are also discussed. Various power-saving features, design features, sensor configurations and user comfort features are also discussed.

Headphones with magnetic sensor

A wireless power transmission system has a wireless power receiving device with a wireless power receiving coil that is located on a charging surface of a wireless power transmitting device with a wireless power transmitting coil array. Control circuitry in the wireless power transmitting device may use inverter circuitry to supply alternating-current signals to coils in the coil array, thereby transmitting wireless power signals. The control circuitry may also be used to detect foreign objects on the coil array such as metallic objects without wireless power receiving coils. For example, control circuitry may use inductance measurements from the coils in the coil array to determine a probability value indicative of whether a foreign object is present on the charging surface. The control circuitry may compare the probability value to a threshold and take suitable action in response to the comparison.

Wireless charging system with machine-learning-based foreign object detection

The disclosure relates generally to methods, systems, and apparatuses for automated or assisted driving and more particularly relates to identification, localization, and navigation with respect to bollard receivers. A method for detecting bollard receivers includes receiving perception data from one or more perception sensors of a vehicle. The method includes determining, based on the perception data, a location of one or more bollard receivers in relation to a body of the vehicle. The method also includes providing an indication of the location of the one or more bollard receivers to one or more of a driver and component or system that makes driving maneuver decisions.

Bollard receiver identification

Systems, methods, and devices for detecting a vehicle's turn signal status for collision avoidance during lane-switching maneuvers or otherwise. A method includes detecting, at a first vehicle, a presence of a second vehicle in an adjacent lane. The method includes identifying, in an image of the second vehicle, a sub-portion containing a turn signal indicator of the second vehicle. The method includes processing the sub-portion of the image to determine a state of the turn signal indicator. The method also includes notifying a driver or performing a driving maneuver, at the first vehicle, based on the state of the turn signal indicator.

Vehicle turn signal detection

A wireless power transmission system has a wireless power receiving device that is located on a charging surface of a wireless power transmitting device. The wireless power receiving device has a wireless power receiving coil and the wireless power transmitting device has a wireless power transmitting coil array. Signal measurement circuitry coupled to the coil array may make measurements while the control circuitry uses the inverter circuitry to apply excitation signals to each of the coils. Foreign objects on the coil array such as metallic objects without wireless power receiving coils can be detected using foreign object detection. When multiple wireless power receiving devices are present on the wireless power transmitting device, steps may be taken to isolate measurements from the coils associated with each wireless power receiving device. Foreign object detection may then be performed on these modified measurements.

Foreign object detection in wireless charging systems with multiple power receiving devices present on a power transmitting device

A system for detecting and identifying foliage includes a tracking component, a tracking parameters component, and a classification component. The tracking component is configured to detect and track one or more features within range data from one or more sensors. The tracking parameters component is configured to determine tracking parameters for each of the one or more features. The tracking parameters include a tracking age and one or more of a detection consistency and a position variability. The classification component is configured to classify a feature of the one or more features as corresponding to foliage based on the tracking parameters.

Detecting foliage using range data

A method and an apparatus pertaining to generating training data. The method may include executing a simulation process. The simulation process may include traversing one or more virtual sensors over a virtual driving environment defining a plurality of lane markings or virtual objects that are each sensible by the one or more virtual sensors. During the traversing, each of the one or more virtual sensors may be moved with respect to the virtual driving environment as dictated by a vehicle-dynamic model modeling motion of a vehicle driving on a virtual road surface of the virtual driving environment while carrying the one or more virtual sensors. Virtual sensor data characterizing the virtual driving environment may be recorded. The virtual sensor data may correspond to what an actual sensor would produce in a real-world environment that is similar or substantially matching the virtual driving environment.

Lane boundary detection data generation in virtual environment

Example metal bridge detection systems and methods are described. In one implementation, a method receives LIDAR data from a LIDAR system mounted to a vehicle and receives camera data from a camera system mounted to the vehicle. The method analyzes the received LIDAR data and the camera data to identify a metal bridge proximate the vehicle. If a metal bridge is identified, the method adjusts vehicle operations to improve vehicle control as it drives across the metal bridge.

Metal bridge detection systems and methods

The present invention extends to methods, systems, and computer program products for detecting available parking spaces in a parking environment. Radar systems are utilized to gather data about a parking lot environment. The radar data is provided to a neural network model as an input. Algorithms employing neural networks can be trained to recognize parked vehicles and conflicting data regarding debris, shopping carts, street lamps, traffic signs, pedestrians, etc. The neural network model processes the radar data to estimate parking space boundaries and to approximate the parking space boundaries as splines. The neural network model outputs spline estimations to a vehicle computer system. The vehicle computer system utilizes the spline estimates to detect available parking spaces. The spline estimates are updated as the vehicle navigates the parking environment.

Detecting available parking spaces

A wireless power transmission system has a wireless power receiving device with a wireless power receiving coil that is located on a charging surface of a wireless power transmitting device with a wireless power transmitting coil array. Control circuitry in the wireless power transmitting device may use inverter circuitry to supply alternating-current signals to coils in the coil array, thereby transmitting wireless power signals. The control circuitry may also be used to detect foreign objects on the coil array such as metallic objects without wireless power receiving coils. For example, control circuitry may use inductance measurements from the coils in the coil array to identify segments of the coil array that correspond to potential wireless power receiving devices. The control circuitry may control wireless power transmission based on a comparison between the number of identified segments corresponding to potential wireless power receiving devices and a number of received device-identifiers.

Wireless charging system with image-processing-based foreign object detection

A scenario is defined that including models of vehicles and a typical driving environment. A model of a subject vehicle is added to the scenario and sensor locations are defined on the subject vehicle. Perception of the scenario by sensors at the sensor locations is simulated to obtain simulated sensor outputs. The simulated sensor outputs are annotated to indicate the location of obstacles in the scenario. The annotated sensor outputs may then be used to validate a statistical model or to train a machine learning model. The simulates sensor outputs may be modeled with sufficient detail to include sensor noise or may include artificially added noise to simulate real world conditions.

Generating simulated sensor data for training and validation of detection models

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Cupertino, CA, United States of America

Sneha Kadetotad