Abstract: A method includes obtaining a fisheye image of a scene and identifying multiple regions of interest in the fisheye image. The method also includes applying one or more transformations to transform and rotate one or more of the regions of interest in the fisheye image to produce one or more transformed regions. The method further includes generating a collage image having at least one portion based on the fisheye image and one or more portions containing the one or more transformed regions. In addition, the method includes performing object detection to identify one or more objects captured in the collage image.

Abstract: An apparatus includes at least one camera configured to capture an image of a traffic lane in front of a vehicle. The apparatus also includes a path tracking controller configured to detect lane boundaries and a path curvature for the traffic lane from the image, determine a lateral offset of the vehicle from a reference path for the traffic lane and a heading offset for the vehicle from the path curvature, determine a yaw rate maintaining the vehicle within the traffic lane using a kinematics control, determine a steering angle maintaining the vehicle within the traffic lane using a dynamics control and the yaw rate determined by the kinematics control, and activate a steering control based on the determined steering angle.

Abstract: An apparatus includes at least one camera configured to capture at least one image of a traffic lane, an inertial measurement unit (IMU) configured to detect motion characteristics, and at least one processor. The at least one processor is configured to obtain a vehicle motion trajectory using the IMU and based on one or more vehicle path prediction parameters, obtain a vehicle vision trajectory based on the at least one image, wherein the vehicle vision trajectory includes at least one lane boundary, determine distances between one or more points on the vehicle and one or more intersection points of the at least one lane boundary based on the obtained vehicle motion trajectory, determine at least one time to line crossing (TTLC) based on the determined distances and a speed of the vehicle, and activate a lane departure warning indicator based on the determined at least one TTLC.

Abstract: Operation and motion control, by a vehicle's ADAS or AD features, is improved in ways suitable to EVs having higher driving and handling performance. The vehicle dynamic model for high rates of lateral acceleration (e.g., sharp cornering or taking curves having a small radius of curvature as faster speeds) is improved by one or more of optimizing time cornering stiffness with a sigmoid function and/or altering front/rear steering angle to account for roll steer and compliance steer, based on vehicle testing. Indicators for lane departure warning or collision warning, evasive steering, or emergency braking are therefore reliably extended to allow higher performance maneuvers.

Abstract: A method includes obtaining, using at least one processing device, image data associated with a scene. The method also includes identifying, using the at least one processing device, multiple line segments based on the image data. The method further includes identifying, using the at least one processing device, one or more boundaries around one or more objects detected in the image data. In addition, the method includes estimating, using the at least one processing device, a position of a vanishing point associated with the image data based on multiple collections of the line segments while excluding, from the multiple collections, one or more of the line segments that overlap with or that are included within the one or more boundaries.

Abstract: A method includes obtaining, using at least one processing device, a vanishing point and a boundary based on image data associated with a scene, where the boundary is associated with a detected object within the scene. The method also includes repeatedly, during multiple iterations and using the at least one processing device, (i) identifying multiple patches within the boundary and (ii) determining a similarity of the image data contained within the multiple patches. The method further includes identifying, using the at least one processing device, a modification to be applied to the boundary based on the identified patches and the determined similarities. In addition, the method includes generating, using the at least one processing device, a refined boundary based on the modification, where the refined boundary identifies a specified portion of the detected object.

Abstract: A method includes obtaining, using at least one processing device, a refined boundary identifying a specified portion of a detected object within a scene, where the refined boundary is associated with image data. The method also includes repeatedly, during multiple iterations and using the at least one processing device, (i) identifying multiple regions within the refined boundary and (ii) determining a similarity of the image data contained within the multiple regions. In addition, the method includes identifying, using the at least one processing device, one or more locations of one or more components of the detected object based on the identified regions and the determined similarities.

Abstract: An apparatus includes an inertial measurement unit (IMU) configured to detect motion characteristics of a vehicle. The apparatus also includes an odometry system configured to detect a wheel speed of each wheel of the vehicle. The apparatus further includes at least one processor communicatively connected to the IMU and the odometry system, the at least one processor configured to determine first parameters for predicting a path of the vehicle, determine second parameters for predicting the path of the vehicle, and predict the path of the vehicle using a combination of the first parameters and the second parameters, wherein the combination is weighted based on a longitudinal acceleration of the vehicle obtained using the IMU.

Abstract: A method includes obtaining first and second data captured using different types of sensors. The method also includes obtaining first object detection results based on the first data and generated using a machine learning model, where the first object detection results identify one or more objects detected using the first data. The method further includes obtaining second object detection results based on the second data, where the second object detection results identify one or more objects detected using the second data. The method also includes identifying one or more inconsistencies between the first and second object detection results and generating labeled training data based on the one or more identified inconsistencies. In addition, the method includes retraining the machine learning model or training an additional machine learning model using the labeled training data.

Abstract: An apparatus includes at least one camera configured to capture at least one image of a traffic lane, an inertial measurement unit (IMU) configured to detect motion characteristics, and at least one processor. The at least one processor is configured to obtain a vehicle motion trajectory using the IMU and based on one or more vehicle path prediction parameters, obtain a vehicle vision trajectory based on the at least one image, wherein the vehicle vision trajectory includes at least one lane boundary, determine distances between one or more points on the vehicle and one or more intersection points of the at least one lane boundary based on the obtained vehicle motion trajectory, determine at least one time to line crossing (TTLC) based on the determined distances and a speed of the vehicle, and activate a lane departure warning indicator based on the determined at least one TTLC.

Abstract: An apparatus includes at least one camera configured to capture an image of a traffic lane in front of a vehicle. The apparatus also includes a path tracking controller configured to detect lane boundaries and a path curvature for the traffic lane from the image, determine a lateral offset of the vehicle from a reference path for the traffic lane and a heading offset for the vehicle from the path curvature, determine a yaw rate maintaining the vehicle within the traffic lane using a kinematics control, determine a steering angle maintaining the vehicle within the traffic lane using a dynamics control and the yaw rate determined by the kinematics control, and activate a steering control based on the determined steering angle.

Abstract: A method includes obtaining an image of a scene and identifying one or more labels for one or more objects captured in the image. The method also includes generating one or more domain-specific augmented images by modifying the image, where the one or more domain-specific augmented images are associated with the one or more labels. In addition, the method includes training or retraining a machine learning model using the one or more domain-specific augmented images and the one or more labels. Generating the one or more domain-specific augmented images may include at least one of modifying the image to include a different amount of motion blur, modifying the image to include a different lighting condition, and modifying the image to include a different weather condition.

Abstract: Operation and motion control, by a vehicle's ADAS or AD features, is improved in ways suitable to EVs having higher driving and handling performance. The vehicle dynamic model for high rates of lateral acceleration (e.g., sharp cornering or taking curves having a small radius of curvature as faster speeds) is improved by one or more of optimizing time cornering stiffness with a sigmoid function and/or altering front/rear steering angle to account for roll steer and compliance steer, based on vehicle testing. Indicators for lane departure warning or collision warning, evasive steering, or emergency braking are therefore reliably extended to allow higher performance maneuvers.

Abstract: A method includes obtaining a fisheye image of a scene and identifying multiple regions of interest in the fisheye image. The method also includes applying one or more transformations to transform and rotate one or more of the regions of interest in the fisheye image to produce one or more transformed regions. The method further includes generating a collage image having at least one portion based on the fisheye image and one or more portions containing the one or more transformed regions. In addition, the method includes performing object detection to identify one or more objects captured in the collage image.

Abstract: A method includes identifying, using at least one processor, a first point associated with an uncertain location of an object in a space and a polynomial curve associated with an uncertain location of a feature in the space. The method also includes determining, using the at least one processor, a probabilistic proximity of the object and the feature. The probabilistic proximity is determined by identifying a second point on the polynomial curve, transforming an uncertainty associated with the polynomial curve into an uncertainty associated with the second point, and identifying the probabilistic proximity of the object and the feature using the first and second points and the uncertainty associated with the second point.