Disclosed are techniques for improving an advanced driver-assistance system (ADAS) by pre-processing image data. In one embodiment, a method is disclosed comprising receiving one or more image frames captured by an image sensor installed on a vehicle; identifying a position of a skyline in the one or more image frames, the position comprising a horizontal position of the skyline; cropping one or more future image frames based on the position of the skyline, the cropping generating cropped images comprising a subset of the corresponding future image frames; and processing the cropped images at an advanced driver-assistance system (ADAS).

A remotely-controlled (RC) and/or autonomously operated inspection device, such as a ground vehicle or drone, may capture one or more sets of imaging data indicative of at least a portion of an automotive vehicle, such as all or a portion of the undercarriage. The one or more sets of imaging data may be analyzed based upon data indicative of at least one of vehicle damage or a vehicle defect being shown in the one or more sets of imaging data. Based upon the analyzing of the one or more sets of imaging data, damage to the vehicle or a defect of the vehicle may be identified. The identified damage or defect may be compared to a claimed damage or defect to determine whether the claimed damage or defect occurred.

The present disclosure provides an electronic apparatus and an object detection method. The electronic apparatus includes a storage device and a processor. The storage device stores an estimation module. The processor is coupled to the storage device and configured to execute the estimation module. The processor acquires a sensed image provided by an image sensor, and inputs the sensed image to the estimation module so that the estimation module outputs a plurality of estimated parameters. The processor calculates two-dimensional image center coordinates of an object image in the sensed image based on the plurality of estimated parameters, and calculates three-dimensional center coordinates corresponding to the object image based on the two-dimensional image center coordinates and an offset parameter in the plurality of estimated parameters. Thus, the location of the object image in the sensed image can be determined accurately.

Systems and methods that allow a smartphone to be used as an imaging device for undercarriage inspection of a moving vehicle are provided. The method may include locating the smartphone on the ground via one or more sensors of the vehicle. The vehicle may generate a path for the vehicle to drive over the smartphone based on the location of the smartphone, and optionally display the path to facilitate manual driving of the vehicle by the driver over the smartphone. Alternatively, the vehicle may self-drive to follow the path. The smartphone may capture image data indicative of the undercarriage of the vehicle, inspect and analyze the image data to identify one or more issues of the undercarriage of the vehicle, and transmit the analyzed image data to the vehicle for display. The driver may confirm the one or more issues and transmit the data to an inspection professional for additional assistance if needed.

An image error identification system retrieves an image associated with a vehicle listing and uses various machine learning models to classify the image and generate identification data that may include a vehicle make, model, trim level, and/or various features of the vehicle present in the image. The identification data is compared to the rest of the vehicle listing to detect a mismatch between the image and the vehicle listing. An alert is generated, when a mismatch is detected, indicating the one of the image or the data in the vehicle listing is incorrect.

The present invention relates to the determination of damage to portions of a vehicle. More particularly, the present invention relates to determining whether each part of a vehicle should be classified as damaged or undamaged and optionally the severity of the damage to each part of the damaged vehicle including preserving the quality of the input images of the damage to the vehicle. Aspects and/or embodiments seek to provide a computer-implemented method for determining damage states of each part of a damaged vehicle, indicating whether each part of the vehicle is damaged or undamaged and optionally the severity of the damage to each part of the damaged vehicle, using images of the damage to the vehicle and trained models to assess the damage indicated in the images of the damaged vehicle, including preserving the quality and/or resolution of the images of the damaged vehicle.

Techniques for improved camera calibration are disclosed. An image is analyzed to identify a first set of key points for an object. A virtual object is generated. The virtual object has a second set of key points. A reprojected version of the second set is fitted to the first set in 2D space until a fitting threshold is satisfied. To do so, a 3D alignment of the second set is generated in an attempt to fit (e.g., in 2D space) the second set to the first set. Another operation includes reprojecting the second set into 2D space. In response to comparing the reprojected second set to the first set, another operation includes determining whether a fitting error between those sets satisfies the fitting threshold. A specific 3D alignment of the second set is selected. The camera is calibrated based on resulting reprojection parameters.

A method for assessing possible trajectories of road users in a traffic environment includes capturing the traffic environment with static and dynamic features, identifying at least one traffic user, determining at least one possible trajectory for at least one road user in the traffic environment, and assessing the at least one determined possible trajectory for the at least one road user with an adapted/trained recommendation service and the captured traffic environment.

A method recognizes an object of a mobile unit in a digital image that shows at least one partition of the mobile unit, especially in motion, by using a method for machine learning. To provide an accurate and reliable recognition the method includes using machine learning in a categorization step for categorizing the digital image, which shows the partition of the mobile unit, with a category. By using the machine learning in a detection step the object of the mobile unit in the categorized digital image and a location of the object in the categorized digital image are determined. By using machine learning in a segmentation step positions in the categorized digital image are classified such that it is determined whether at a respective position of the categorized digital image a part of the object is present or not.

The present disclosure is directed to automatically generating a 360 Virtual Photographic Representation (“spin”) of an object using multiple images of the object. The system uses machine learning to automatically differentiate between images of the object taken from different angles. A user supplies multiple images and/or videos of an object and the system automatically analyzes and classifies the images into the proper order before incorporating the images into an interactive spin. The system automatically classifies the images using features identified in the images. The classifications are based on predetermined classifications associated with the object to facilitate proper ordering of the images in the resulting spin.