Methods and systems for jointly estimating a pose and a shape of an object perceived by an autonomous vehicle are described. The system includes data and program code collectively defining a neural network which has been trained to jointly estimate a pose and a shape of a plurality of objects from incomplete point cloud data. The neural network includes a trained shared encoder neural network, a trained pose decoder neural network, and a trained shape decoder neural network. The method includes receiving an incomplete point cloud representation of an object, inputting the point cloud data into the trained shared encoder, outputting a code representative of the point cloud data. The method also includes generating an estimated pose and shape of the object based on the code. The pose includes at least a heading or a translation and the shape includes a denser point cloud representation of the object.

Devices, systems, and methods for remote control of autonomous vehicles are disclosed herein. A method may include receiving, by a device, first data indicative of an autonomous vehicle in a parking area, and determining, based on the first data, a location of the autonomous vehicle. The method may include determining, based on a the location, first image data including a representation of an object. The method may include generating second image data based on the first data and the first image data, and presenting the second image data. The method may include receiving an input associated with controlling operation of the autonomous vehicle, and controlling, based on the input, the operation of the autonomous vehicle.

A system for identifying desired information from collected sensor data includes a collection device and a processing module. The collection device collects sensor data, coarsely filters the sensor data according to predefined rules to generate filter matched data, and securely transmits the filter matched data to the processing module. The processing module finely filters the filter matched data to generate desired information, provides the desired information to an authorized actor, and deletes the filter matched data.

A method for recognizing an object which is turning off includes detecting a vehicle in front by way of a surroundings sensor system of an ego vehicle; determining a probability of the vehicle in front turning off; and marking the vehicle in front as an object which is turning off when the probability of turning off is equal to or greater than a predetermined threshold value.

A method and an apparatus for obtaining 3D information of a vehicle are provided. The method includes: first determining a body boundary line of a first vehicle, and then determining an observation angle and/or an orientation angle of the first vehicle based on the body boundary line.

Included are an object identification unit that identifies an identified object in an image; a mapping unit that generates a superimposed image by superimposing target points corresponding to ranging points and superimposing a rectangle surrounding the identified object to the image; an identical-object determination unit that specifies, in the superimposed image, two target points closest to the left and right line segments of the rectangle inside the rectangle; a depth addition unit that specifies, in a space, the positions of two edge points indicating the left and right edges of the identified object based on two ranging points corresponding to the two specified target points, and calculates two depth positions of two predetermined corresponding points different from the two edge points; and an overhead-view generation unit that generates an overhead view of the identified object from the positions of the two edge points and the two depth positions.

Described herein are systems and methods for determining if a vehicle is parked. In one example, a system includes a processor, a sensor system, and a memory. Both the sensor system and the memory are in communication with the processor. The memory includes a parking determination module having instructions that, when executed by the processor, cause the processor to determine, using a random forest model, when the vehicle is parked based on vehicle estimated features, vehicle learned features, and vehicle taillight features of the vehicle that are based on sensor data from the sensor system.

A method for re-recognizing an object image is provided based on multi-feature information capture and correlation analysis weights of an input feature map by using a convolutional layer with a spatial attention mechanism and a channel attention mechanism, causing channel and spatial information to effectively combined, which not only focus on an important feature and suppress an unnecessary feature, but also improve a representation of a feature. A multi-head attention mechanism is used to process a feature after an image is divided into blocks to capture abundant feature information and determine a correlation between features to improve performance and efficiency of object image retrieval. The convolutional layer with the channel attention mechanism and the spatial attention mechanism is combined with a transformer having the multi-head attention mechanism to focus on globally important features and capture fine-grained features, thereby improving performance of re-recognition.

A method of detecting occupants in a vehicle includes detecting an oncoming vehicle and acquiring a plurality of images of occupants in the vehicle in response to detection of the vehicle. The method includes performing automated facial detection on the plurality of images and adding a facial image for each face detected to a gallery of facial images for the occupants of the vehicle. The method includes performing automated facial recognition on the gallery of facial images to group the facial images into groups based on which occupant is in the respective facial images, and counting the final group of unique facial images to determine how many occupants are in the vehicle.

A traffic information processing equipment, system and method. The traffic information processing equipment includes an image recognition and decision device and a warning device. The image recognition and decision device is configured to process a received traffic route image to identify a scene, and determine whether to perform a warning operation according to the scene to obtain a determination result. The warning device is configured to generate warning information according to the determination result for sending prompt information to vehicles in a traffic route.