Abstract: A computing system can determine moving objects in a sequence of images based on recursively calculating red-green-blue (RGB) eccentricity 249 k based on a video data stream. A vehicle can be operated based on the determined moving objects. The video data stream can be acquired by a color video sensor included in the vehicle or a traffic infrastructure system.

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 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: Methods, apparatus, systems, and articles of manufacture are disclosed to perform visual odometry using a monocular camera. An example apparatus includes a method fusioner to determine a visual odometry method using a surface characterization to calculate first odometry data, a displacement determiner to calculate second odometry data based on sensor information, an odometry calculator to calculate an odometry parameter of a target object with respect to a vehicle based on the first and the second odometry data, and a vehicle command generator to generate a vehicle command based on the odometry parameter.

Abstract: Systems, methods, and devices for estimating a shape of an object based on sensor data and determining a presence of a fault or a failure in a perception system. A method of the disclosure includes receiving sensor data from a range sensor and calculating a current shape reconstruction of an object based on the sensor data. The method includes retrieving from memory a prior shape reconstruction of the object based on prior sensor data. The method includes calculating a quality score for the current shape reconstruction by balancing a function of resulted variances of the current shape reconstruction and a similarity between the current shape reconstruction and the prior shape reconstruction.

Abstract: Methods, apparatus, systems, and articles of manufacture are disclosed to perform visual odometry using a monocular camera. An example apparatus includes a method fusioner to determine a visual odometry method using a surface characterization to calculate first odometry data, a displacement determiner to calculate second odometry data based on sensor information, an odometry calculator to calculate an odometry parameter of a target object with respect to a vehicle based on the first and the second odometry data, and a vehicle command generator to generate a vehicle command based on the odometry parameter.

Abstract: A method and an apparatus for background subtraction highly applicable in autonomous driving scenarios are described. The method involves a reduction of illumination effects by constructing a normality background image from a normality model based on a plurality of baseline images taken under different illuminating conditions. A subtracted image is obtained by subtracting the normality background image from a scene image pixel-wise (i.e., pixel-by-pixel). The scene image may contain one or more foreground objects. The foreground objects are identified by highlighting the pixels in the subtracted image whose intensity is more than a predetermined standard deviation in the normality model. An illumination-invariant color space transformation algorithm may optionally be utilized to further reduce the variant illumination effects.

Abstract: A vehicle controller receives images from a camera upon arrival and upon departure. A location of the vehicle may be tracked and images captured by the camera may be tagged with a location. A departure image may be compared to an arrival image captured closest to the same location as the arrival image. A residual image based on a difference between the arrival and departure images is evaluated for anomalies. Attributes of the anomaly such as texture, color, and the like are determined and the anomaly is classified based on the attributes. If the classification indicates an automotive fluid, then an alert is generated.

Abstract: A vehicle controller receives images from a camera upon arrival and upon departure. A location of the vehicle may be tracked and images captured by the camera may be tagged with a location. A departure image may be compared to an arrival image captured closest to the same location as the arrival image. A residual image based on a difference between the arrival and departure images is evaluated for anomalies. Attributes of the anomaly such as texture, color, and the like are determined and the anomaly is classified based on the attributes. If the classification indicates an automotive fluid, then an alert is generated. A machine learning algorithm for generating classifications from image data may be trained using arrival and departure images obtained by rendering of a three-dimensional model or by adding simulated fluid leaks to two-dimensional images.

Abstract: An autonomous vehicle includes interior sensors including an IR camera and a visible light camera. Images of an interior of the vehicle are captured using the cameras both before and after a passenger rides in the vehicle. The IR images from before and after are subtracted to obtain a difference image. Pixels above a threshold intensity a clustered. Clusters having an above-threshold size are determined to be anomalies. Portions of images from the visible light camera corresponding to the anomalies are sent to a dispatcher, who may then clear the vehicle to pick up another passenger or proceed to a cleaning station. Anomalies may be identified based on a combination of the IR images and visible light images.

Abstract: A method and an apparatus for background subtraction highly applicable in autonomous driving scenarios are described. The method involves a reduction of illumination effects by constructing a normality background image from a normality model based on a plurality of baseline images taken under different illuminating conditions. A subtracted image is obtained by subtracting the normality background image from a scene image pixel-wise (i.e., pixel-by-pixel). The scene image may contain one or more foreground objects. The foreground objects are identified by highlighting the pixels in the subtracted image whose intensity is more than a predetermined standard deviation in the normality model. An illumination-invariant color space transformation algorithm may optionally be utilized to further reduce the variant illumination effects.

Abstract: An autonomous vehicle includes interior sensors including an IR camera and a visible light camera. Images of an interior of the vehicle are captured using the cameras both before and after a passenger rides in the vehicle. The IR images from before and after are subtracted to obtain a difference image. Pixels above a threshold intensity a clustered. Clusters having an above-threshold size are determined to be anomalies. Portions of images from the visible light camera corresponding to the anomalies are sent to a dispatcher, who may then clear the vehicle to pick up another passenger or proceed to a cleaning station. Anomalies may be identified based on a combination of the IR images and visible light images.

Abstract: A vehicle controller receives images from a camera upon arrival and upon departure. A location of the vehicle may be tracked and images captured by the camera may be tagged with a location. A departure image may be compared to an arrival image captured closest to the same location as the arrival image. A residual image based on a difference between the arrival and departure images is evaluated for anomalies. Attributes of the anomaly such as texture, color, and the like are determined and the anomaly is classified based on the attributes. If the classification indicates an automotive fluid, then an alert is generated. A machine learning algorithm for generating classifications from image data may be trained using arrival and departure images obtained by rendering of a three-dimensional model or by adding simulated fluid leaks to two-dimensional images.

Abstract: A vehicle controller receives images from a camera upon arrival and upon departure. A location of the vehicle may be tracked and images captured by the camera may be tagged with a location. A departure image may be compared to an arrival image captured closest to the same location as the arrival image. A residual image based on a difference between the arrival and departure images is evaluated for anomalies. Attributes of the anomaly such as texture, color, and the like are determined and the anomaly is classified based on the attributes. If the classification indicates an automotive fluid, then an alert is generated.