The present invention relates to methods and systems that utilize the production vehicles to develop new perception features related to new sensor hardware as well as new algorithms for existing sensors by using self-supervised continuous training. To achieve this the production vehicle's own perception output is fused with other sensors in order to generate a bird's eye view of the road scenario over time. The bird's eye view is synchronized with buffered sensor data that was recorded when the road scenario took place and subsequently used to train a new perception model to output the bird's eye view directly.

The invention supports creation of models for recognizing attributes in an image with high accuracy. An image recognition support apparatus includes an image input unit configured to acquire an image, a pseudo label generation unit configured to recognize the acquired image based on a plurality of types of image recognition models and output recognition information, and generate pseudo labels indicating attributes of the acquired image based on the output recognition information, and a new label generation unit configured to generate new labels based on the generated pseudo labels.

A method for automatically panning a view for a commercial vehicle includes determining a plurality of estimated trailer angles. Each estimated trailer angle is determined using a distinct estimation method, and method assigns a confidence value to each estimated trailer angle in the plurality of estimated trailer angles. The method determines a weighted sum of the plurality of estimate trailer angles, and automatically pans the view based at least in part on the weighted sum and a current vehicle operation.

Provided are methods for enhanced semantic labeling in mapping with a semantic labeling system, which can include receiving, from a LiDAR sensor of a vehicle, LiDAR point cloud information including at least one raw point feature for a point, receiving, from a camera of the vehicle, image data associated with an image captured using the camera, generating at least one rich point feature for the point based on the image data, predicting, using a LiDAR segmentation neural network and based on the at least one raw point feature and the at least one rich point feature, a point-level semantic label for the point, and providing the point-level semantic label to a mapping engine to generate a map based on the point-level semantic label Systems and computer program products are also provided.

The present disclosure relates to an image processing method and apparatus, an electronic device and a storage medium. The method includes performing feature extraction on an image to be processed to obtain a first feature map of the image to be processed and performing weight prediction on the first feature map to obtain a weight feature map of the first feature map. The weight feature map includes weight values of feature points in the first feature map. The method further includes performing feature value adjustment on the feature points in the first feature map based on the weight feature map to obtain a second feature map and determining a processing result of the image to be processed according to the second feature map. Embodiments of the present disclosure may improve the image processing accuracy.

This application provides an image obtaining method and apparatus. The image obtaining method according to this application includes: obtaining first original image data, where the first original image data is captured by an image sensor based on an initial visible light exposure parameter and luminous intensity of an infrared illuminator; obtaining a luminance of a visible light image based on the first original image data; adjusting the visible light exposure parameter based on a first difference, where the first difference is a difference between the luminance of the visible light image and preset target luminance of the visible light image; obtaining a luminance of an infrared image based on the first original image data; adjusting the luminous intensity of the infrared illuminator based on a second difference, where the second difference is a difference between the luminance of the infrared image and preset target luminance of the infrared image.

Embodiments of the present disclosure provide a method and apparatus for training an image recognition model based on a semantic enhancement, a method and apparatus for recognizing an image, an electronic device, and a computer readable storage medium. The method for training an image recognition model based on a semantic enhancement comprises: extracting, from an inputted first image being unannotated and having no textual description, a first feature representation of the first image; calculating a first loss function based on the first feature representation; extracting, from an inputted second image being unannotated and having an original textual description, a second feature representation of the second image; calculating a second loss function based on the second feature representation, and training an image recognition model based on a fusion of the first loss function and the second loss function.

Systems and methods herein provide for improving visibility in a scene. In one embodiment, a system includes a first camera device operable to capture images of a scene at a first band of wavelengths, and a second camera device operable to capture images of the scene at a second band of wavelengths. The first and second bands are different. The system also includes a processor communicatively coupled to the first and second camera devices, the processor being operable to detect an object in the scene based on a first of the images from the first camera device and based on a first of the images from the second camera device that was captured at substantially a same time as the first image from the first camera device, to estimate an obscurant in the scene based on the first images, and to estimate a visibility parameter of the scene based on the object and the estimated obscurant.

A method includes receiving, from a sensing system of an autonomous vehicle (AV), image data including first image data and second image data. The method further includes determining, for a frame, whether an amount of image data matching between the first image data and the second image data satisfies a first threshold condition, in response to determining that the amount of image data matching satisfies a first threshold condition, identifying the frame as invalid, determining whether a number of consecutive frames determined to be invalid satisfies a second threshold condition, and in response to determining that the number of consecutive frames determined to be invalid satisfies the second threshold condition, generating a notification that the sensing system is outputting invalid data.

Techniques are disclosed for systems and methods for water non-water segmentation of navigational imagery to assist in the autonomous navigation of mobile structures. An imagery based navigation system includes a logic device configured to communicate with an imaging module coupled to a mobile structure and/or configured to capture images of an environment about the mobile structure. The logic device may be configured to receive at least one image from the imaging module; determine a water/non-water segmented image based, at least in part, on the received at least one image, and generate a range chart corresponding to the environment about the mobile structure based, at least in part, on the determined water/non-water segmented image and/or the received at least one image.