Abstract: A vehicle system comprises a hitch ball mounted on a vehicle. The system further comprises a plurality of sensor devices comprising an ultrasonic sensor and an image sensor. A controller is configured to process image data from the image sensor identifying a coupler position of a trailer. The controller is further configured to process ultrasonic data from the ultrasonic sensor identifying a proximity of the trailer. Based on the proximity of the trailer, the system is configured to identify the trailer in a detection range of the image sensor.

Abstract: A system for assisting in aligning a vehicle for hitching with a trailer includes a steering system that adjusts a steering angle of the vehicle, a braking system that adjusts a speed of the vehicle, an imaging system that receives image data of a trailer disposed in an area proximate the vehicle, and a controller. The controller detects, within a threshold distance defined from a stationary point on the vehicle and the trailer, a position of a coupler from the image data, and, responsive to the position being less than the threshold distance, a status of the trailer based on the image data being indicative of a position of a hitch ball relative to the coupler within a second threshold. The controller maneuvers, via the steering and braking systems, the vehicle based on the status.

Abstract: A hitch assist system for a vehicle comprises an imaging system that receives image data of a hitch ball and trailer coupler, and a controller. The controller estimates a distance between a point on each of the hitch ball and trailer coupler. The points are parallel to a plane defined by a surface beneath the hitch ball and trailer coupler. The controller tracks the distance with a pixel-domain comparison between each of the points responsive to the distance being less than a first threshold, and maneuvers, via steering and braking systems, the vehicle along a path such that the distance is indicative of the points being coaxial.

Abstract: A vehicle has a trailer hitch, a vehicle electrical connector configured to electrically connect to a trailer electrical connector, and an imaging system oriented to captures images proximate to the trailer hitch. The vehicle also includes a proximity sensing system arranged to sense objects proximate to the trailer hitch and generate a proximity signal, an electrical connection detection system to detect an electrical connection to the vehicle trailer connector and generate an electrical connection signal, and a controller processing the captured images, the proximity signal and the electrical connector signal and determining a connection state of a trailer to the vehicle based on the captured images, the proximity sensor and the electrical connector signal, and generating an output indicative of the determined connection state.

Abstract: A computer, including a processor and a memory, the memory including instructions to be executed by the processor to determine an eccentricity map based on video image data and determine vehicle motion data by processing the eccentricity map and two red, green, blue (RGB) video images with a deep neural network trained to output vehicle motion data in global coordinates. The instructions can further include instructions to operate a vehicle based on the vehicle motion data.

Abstract: A computer, including a processor and a memory, the memory including instructions to be executed by the processor to detect a moving object in video stream data based on determining an eccentricity map. The instructions can further include instructions to determine a magnitude and direction of motion of the moving object, transform the magnitude and direction to global coordinates and operate a vehicle based on the transformed magnitude and direction.

Abstract: An autonomous vehicle includes a microphone sensing sounds in the interior of the vehicle. The output of the interior microphone is processed according to an unsupervised machine learning model such that anomalies are indicated by the model. In response to detection of an anomaly, a remote dispatcher is notified, who may then dismiss the anomaly or transmit an instruction to the vehicle to alter its trajectory. The output of an exterior microphone and infotainment system may be removed from the output of the interior microphone prior to processing. An anomaly may be found to occur in response to detecting speaking of a keyword in the output of the interior microphone.

Abstract: The present disclosure describes systems and methods that include calculating, via a reinforcement learning agent (RLA) controller, a plurality of state-action values based on sensor data representing an observed state, wherein the RLA controller utilizes a deep neural network (DNN) and generating, via a fuzzy controller, a plurality of linear models mapping the plurality of state-action values to the sensor data.

Abstract: A computing system can receive an image including foreground pixels. The foreground pixels can be determined based on determining eccentricity ?k based on a sequence of images acquired by a stationary sensor. The vehicle can determine moving objects in the image based on the foreground pixels. The vehicle can be operated based on the moving objects in the image.

Abstract: A vehicle maneuvering system comprises at least one image device configured to capture image data and a controller. The controller is configured to identify a coupler position of a trailer in the image data and control motion of the vehicle navigating a hitch ball of the vehicle toward the coupler position. The controller is further configured to monitor a coupler distance extending from the coupler position and the hitch ball. In response to the coupler distance being less than or equal to the distance threshold, the controller is configured to classify a plurality of portions of the image data as trailer portions and non-trailer portions and identify the coupler position by processing the image via a feature extraction operation.

Abstract: Example vehicle inspection systems and methods are described. In one implementation, a method activates an unmanned aircraft inside a vehicle to capture images of the vehicle interior. The method accesses a flight path for the unmanned aircraft and receives data associated with the vehicle's current movement. The method adjusts the flight path of the unmanned aircraft to compensate for the vehicle's current movement.

Abstract: Example vehicle occupancy management systems and methods are described. In one implementation, a method receives a current vehicle image representing a current interior of a vehicle. An occupancy management system detects at least one passenger in the vehicle based on the current vehicle image and determines a seating location of the passenger. A seat map is generated that identifies the seating location of the passenger in the vehicle.

Abstract: A trailer angle identification system comprises an imaging device configured to capture an image. An angle sensor is configured to measure a first angle of the trailer relative to a vehicle. A controller is configured to process the image in a neural network and estimate a second angle of the trailer relative to the vehicle based on the image. The controller is further configured to train the neural network based on a difference between the first angle and the second angle.

Abstract: A hitch assist system is provided herein that includes a sensing system configured to detect a trailer proximate a vehicle. The hitch assist system also includes a controller for detecting a coupler of the trailer; determining a trailer-heading direction-based on a coupler orientation; and determining a path for aligning a hitch assembly on the vehicle with a coupler on the trailer based on the trailer-heading direction.

Abstract: A computing system can receive, in a vehicle, moving object information is determined by processing lidar sensor data acquired by a stationary lidar sensor. The moving object information can be determined using typicality and eccentricity data analysis (TEDA) on the lidar sensor data. The vehicle can be operated based on the moving object information.

Abstract: A computing system can receive a stabilized image stream, wherein the stabilized image stream is drift-corrected based on determining that an input image stream is stable and then applying drift correction to maintain a stabilized field of view, wherein the field of view is stabilized with respect to the real world. The computing system can operate a vehicle based on determining at least one moving object in the stabilized image stream.

Abstract: A hitch assist system is provided herein. The hitch assist system includes a sensing system having an imager and a proximity sensor. The hitch assist system also includes a controller for receiving signals from the proximity sensor and generating a feature map; determining a coupler location based on the detected features; and maneuvering the vehicle along a path to align a hitch ball with a coupler of the trailer.

Abstract: Systems, methods, and devices for reducing dimensionality and improving neural network operation in light of uncertainty or noise are disclosed herein. A method for reducing dimensionality and improving neural network operation in light of uncertainty or noise includes receiving raw data including a plurality of samples, wherein each sample includes a plurality of input features. The method includes generating fuzzy data based on the raw data. The method includes inputting the raw data and the fuzzy data into an input layer of a neural network autoencoder.

Abstract: A vehicle system comprises a hitch ball mounted on a vehicle. The system further comprises a plurality of sensor devices comprising an ultrasonic sensor and an image sensor. A controller is configured to process image data from the image sensor identifying a coupler position of a trailer. The controller is further configured to process ultrasonic data from the ultrasonic sensor identifying a proximity of the trailer. Based on the proximity of the trailer, the system is configured to identify the trailer in a detection range of the image sensor.

Abstract: A computing system can receive an image including foreground pixels. The foreground pixels can be determined based on determining eccentricity ?k based on a sequence of images acquired by a stationary sensor. The vehicle can determine moving objects in the image based on the foreground pixels. The vehicle can be operated based on the moving objects in the image.