This application provides an image processing method, a network training method, and a related device, and relates to image processing technologies in the artificial intelligence field. The method includes: inputting a first image including a first vehicle into an image processing network to obtain a first result output by the image processing network, where the first result includes location information of a two-dimensional 2D bounding frame of the first vehicle, coordinates of a wheel of the first vehicle, and a first angle of the first vehicle, and the first angle of the first vehicle indicates an included angle between a side line of the first vehicle and a first axis of the first image; and generating location information of a three-dimensional 3D outer bounding box of the first vehicle based on the first result.

Disclosed is an apparatus and method with object detection. The method may include updating a pre-trained model based on sensing data of an image sensor, performing pseudo labeling using an interim model provided a respective training set, determining plural confidence thresholds based on an evaluation of the interim model, performing multiple trainings using the interim model and the generated pseudo labeled data, by applying the determined plural confidence thresholds to the multiple trainings, respectively, and generating an object detection model dependent on the performance of the multiple trainings, including generating an initial candidate object detection model when the interim model is the updated model.

Disclosed is an apparatus and method with object detection. The method may include updating a pre-trained model based on sensing data of an image sensor, performing pseudo labeling using an interim model provided a respective training set, determining plural confidence thresholds based on an evaluation of the interim model, performing multiple trainings using the interim model and the generated pseudo labeled data, by applying the determined plural confidence thresholds to the multiple trainings, respectively, and generating an object detection model dependent on the performance of the multiple trainings, including generating an initial candidate object detection model when the interim model is the updated model.

A system for determining relevance of a light source to an automobile includes at least one camera adapted to capture images of light sources in proximity to the automobile, a controller in communication with the at least one camera and adapted to receive captured images from the at least one camera, the controller further adapted to estimate an orientation of at least one light source relative to the automobile, classify the at least one light source as one of relevant and irrelevant, and, when the at least one light source is classified as relevant, send information about the at least one light source to a planning module for the automobile.

A system for receiving user input from an internal vehicle component surface includes a flat surface layer of the internal vehicle component that includes a first portion made of an elastic material and a second portion that surrounds the first portion, and a push-button assembly located beneath the first portion of the flat surface layer. The push-button assembly includes a push-button switch that is switched into at least a first switching state by downward pressure, and a vertical movement mechanism that when activated causes the push-button switch to move vertically in a direction of the flat surface layer. Vertical movement of the push-button switch causes a vertical displacement of the first portion of the flat surface layer, and downward pressure on the first portion of the flat surface layer when vertically displaced causes a corresponding downward pressure to the push-button switch, switching the push-button switch into the first switch state.

A system for receiving user input from an internal vehicle component surface includes a flat surface layer of the internal vehicle component that includes a first portion made of an elastic material and a second portion that surrounds the first portion, and a push-button assembly located beneath the first portion of the flat surface layer. The push-button assembly includes a push-button switch that is switched into at least a first switching state by downward pressure, and a vertical movement mechanism that when activated causes the push-button switch to move vertically in a direction of the flat surface layer. Vertical movement of the push-button switch causes a vertical displacement of the first portion of the flat surface layer, and downward pressure on the first portion of the flat surface layer when vertically displaced causes a corresponding downward pressure to the push-button switch, switching the push-button switch into the first switch state.

System and methods enable vehicles to make ethical/empathetic driving decisions by using deep learning aided location intelligence. The systems and methods identify moral islands/complex driving scenarios where a complex ethical decision is required. A Generative Adversarial Network (GAN) is used to generate synthetic training data to capture varied ethically complex driving situations. Embodiments train a deep learning model (ETHNET) that is configured to output one or more driving decisions to be taken when a vehicle comes across an ethically complex driving situations in the real world.

A behavior prediction system predicts human behaviors based on environment-aware information such as camera movement data and geospatial data. The system receives sensor data of a vehicle reflecting a state of the vehicle at a given time and a given location. The system determines a field of concern in images of a video stream and determines one or more portions of images of the video stream that correspond to the field of concern. The system may apply different levels of processing powers to objects in the images based on whether an object is in the field of concern. The system then generates features of objects and identify VRUs from the objects of the video stream. For the identified VRUs, the system inputs a representation of the VRUs and the features into a machine learning model, and outputs from the machine learning model a behavioral risk assessment of the VRUs.

A vehicle computing system may implement techniques to predict behavior of objects detected by a vehicle operating in the environment. The techniques may include determining a feature with respect to a detected objects (e.g., likelihood that the detected object will impact operation of the vehicle) and/or a location of the vehicle and determining based on the feature a model to use to predict behavior (e.g., estimated states) of proximate objects (e.g., the detected object). The model may be configured to use one or more algorithms, classifiers, and/or computational resources to predict the behavior. Different models may be used to predict behavior of different objects and/or regions in the environment. Each model may receive sensor data as an input, and output predicted behavior for the detected object. Based on the predicted behavior of the object, a vehicle computing system may control operation of the vehicle.

A vehicle computing system may implement techniques to predict behavior of objects detected by a vehicle operating in the environment. The techniques may include determining a feature with respect to a detected objects (e.g., likelihood that the detected object will impact operation of the vehicle) and/or a location of the vehicle and determining based on the feature a model to use to predict behavior (e.g., estimated states) of proximate objects (e.g., the detected object). The model may be configured to use one or more algorithms, classifiers, and/or computational resources to predict the behavior. Different models may be used to predict behavior of different objects and/or regions in the environment. Each model may receive sensor data as an input, and output predicted behavior for the detected object. Based on the predicted behavior of the object, a vehicle computing system may control operation of the vehicle.