Ford Global Technolgoies, LLC

Toyota Motor Engineering & Manufacturing North America, Inc.

Faizan Shaik

Mostafa Parchami

Ankit Girish Vora

Robert Parenti

Siddharth Agarwal

Medha Karkare

Enrique Corona

Gintaras Vincent Puskorius

John Anthony Lockwood

Helen Elizabeth Kourous-Harrigan

Juan Enrique Castorena Martinez

Jakob Nikolaus Hoellerbauer

Kunjan Singh

Sangjin Lee

Stephen Giardinelli

Matthew Baer Wilmes

Armin Parchami

Sensor fusion is performed for efficient deep learning processing. A camera image is received from an image sensor and supplemental sensor data is received from one or more supplemental sensors, the camera image and the supplemental sensor data including imaging of a cabin of a vehicle. Regions of interest in the camera image are determined based on one or more of the camera image or the supplemental sensor data, the regions of interest including areas of the camera image flagged for further image analysis. A machine-learning model is utilized to perform object detection on the regions of interest of the camera image to identify one or more objects in the camera image. The objects are placed into seating zones of the vehicle.

Sensor fusion area of interest identification for deep learning

An acceleration of a head of an occupant of a vehicle relative to the vehicle is determined based on data describing motion of the vehicle and respective poses of the head from a plurality of consecutive frames of image data. A vehicle suspension is adjusted based on the determined acceleration.

Enhanced vehicle suspension

Performing object localization inside a cabin of a vehicle is provided. A camera image is received of a cabin of a vehicle by an image sensor at a first location with respect to a plurality of seating zones of a vehicle. Object detection on the camera image is performed to identify one or more objects in the camera image. A machine-learning model trained on images taken at the first location is utilized to place the one or more objects into the seating zones of the vehicle according to a plurality of bounding boxes corresponding to the plurality of seating zones for the first location.

Camera-based in-cabin object localization

A system, including a processor and a memory, the memory including instructions to be executed by the processor to determine camera calibration parameters including camera focal distance, camera height and camera tilt based on a two-dimensional calibration pattern located parallel to and coincident with a ground plane determined by a roadway surface and determine object data including object location, object speed and object direction for one or more objects in a field of view of the video camera.

Traffic camera calibration

A reference pose of an object in a coordinate system of a map of an area is determined. The reference pose is based on a three-dimensional (3D) reference model representing the object. A first pose of the object is determined as the object moves with respect to the coordinate system. The first pose is determined based on the reference pose and sensor data collected by the sensor at a first time. A second pose of the object is determined as the object continues to move with respect to the coordinate system. The second pose is determined based on the reference pose, the first pose, and sensor data collected by the sensor at a second time consecutive to the first time.

Localizing a moving object

A system, including a processor and a memory, the memory including instructions to be executed by the processor to identify first object features from sensor data acquired by a stationary sensor at a first time step, determine second object features at a second time step. The instructions can include further instructions to determine one or more object clusters of first object features by determining distances measured in pixels between the first object features and corresponding second object features and comparing the distances to one or more mean distances and determine one or more object groups of inlier first object features in the one or more object clusters by determining a plurality of similarity transformations for a plurality of random samples of first object features and determining inlier first object features based on maximizing the number of first object features included in a similarity transformation.

Group object-tracking

A reference pose graph can be generated from first point cloud data collected at each of a plurality of mobile nodes. A location of a stationary node can be specified based on the reference pose graph and second point cloud data collected by the stationary node.

Transportation infrastructure communication and control

An infrastructure component includes a sensor, a transmitter, and a computer communicatively coupled to the sensor and to the transmitter. The computer is programmed to, in response to receiving data from the sensor indicating an object approaching a predesignated zone, determine an estimated time until the object reaches the predesignated zone; and then instruct the transmitter to broadcast a message to nearby vehicles, the message including an identifier of the predesignated zone and the estimated time.

Infrastructure component broadcast to vehicles

A system includes a processor and a memory. The memory stores instructions executable by the processor to determine a plurality of waypoints for mapping an area by analyzing a received aerial image of the area, to select an optimal number of the waypoints for mapping, and to actuate the vehicle to traverse a route determined to cover the selected waypoints.

High definition 3D mapping

A method for parking an autonomous vehicle in a parking lot. The method comprises receiving primary data from a first source and receiving secondary data from a second source. The primary data may include information sufficient to identify a location for one or more of a plurality of parking spaces within the parking lot. The secondary data may include status identifier information for at least one of the plurality of parking spaces. The method includes using the primary data and the secondary data to identify at least one vacant parking space in the plurality of parking spaces, and occupying the at least one vacant parking space.

Growing community of inventors

Ann Arbor, MI, United States of America

Faizan Shaik