A computer-implemented method for predicting properties of a plurality of objects in a vicinity of a vehicle includes multiple steps that can be carried out by computer hardware components. The method includes determining a grid map representation of road-users perception data, with the road-users perception data including tracked perception results and/or untracked sensor intermediate detections. The method also includes determining a grid map representation of static environment data based on data obtained from a perception system and/or a pre-determined map. The method further includes determining the properties of the plurality of objects based on the grid map representation of road-users perception data and the grid map representation of static environment data.

Provided are methods for semantic annotation of sensor data using unreliable map annotation inputs, which can include training a machine learning model to accept inputs including images representing sensor data for a geographic area and unreliable semantic annotations for the geographic area. The machine learning model can be trained against validated semantic annotations for the geographic area, such that subsequent to training, additional images representing sensor data and additional unreliable semantic annotations can be passed through the neural network to provide predicted semantic annotations for the additional images. Systems and computer program products are also provided.

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 computer-implemented method for reliably identifying objects in a sequence of input images received with the aid of an imaging sensor, positions of light sources in the respective input image being ascertained from the input images in each case with the aid of a first machine learning system, in particular, an artificial neural network, and objects from the sequence of input images being identified from the resulting sequence of positions of light sources, in particular, with the aid of a second machine learning system, in particular, with the aid of an artificial neural network.

Systems and methods for navigating a host vehicle. The system may perform operations including receiving, from an image capture device, at least one image representative of an environment of the host vehicle; analyzing the at least one image to identify an object in the environment of the host vehicle; determining a location of the host vehicle; receiving map information associated with the determined location of the host vehicle, wherein the map information includes elevation information associated with the environment of the host vehicle; determining a distance from the host vehicle to the object based on at least the elevation information; and determining a navigational action for the host vehicle based on the determined distance.

A device can receive parking metadata that includes location data indicating that a portion of a vehicle is located outside of a designated parking area (DPA). The device can process the parking metadata to identify values that are to be used when determining actions to perform. The device can obtain supplemental events data associated with events occurring near the DPA. The device can determine the actions to perform based on the parking metadata and the supplemental events data. The device can provide, as one of the actions and to one or more other devices or to the vehicle, a message indicating that the portion of the vehicle is located outside of the DPA. This can cause the one or more other devices or the vehicle to: move the vehicle from the DPA, reposition the vehicle within the DPA, or penalize an owner of the vehicle.

Certain aspects of the present disclosure provide techniques for performing piecewise pointwise convolution, comprising: performing a first piecewise pointwise convolution on a first subset of data received via a first branch input at a piecewise pointwise convolution layer of a convolutional neural network (CNN) model; performing a second piecewise pointwise convolution on a second subset of data received via a second branch input at the piecewise pointwise convolution layer; determining a piecewise pointwise convolution output by summing a result of the first piecewise pointwise convolution and a result of the second piecewise pointwise convolution; and providing the piecewise pointwise convolution output to a second layer of the CNN model.

Included are: an information acquiring unit to acquire information; a periphery recognizing unit to acquire peripheral environment information regarding a state of a peripheral environment based on the information acquired by the information acquiring unit and a first machine learning model and to acquire calculation process information indicating a calculation process when the peripheral environment information has been acquired; an explanatory information generating unit to generate explanatory information indicating information having a large influence on the peripheral environment information in the calculation process among the information acquired by the information acquiring unit based on the calculation process information acquired by the periphery recognizing unit; and an evaluation information generating unit to generate evaluation information indicating adequacy of the peripheral environment information acquired by the periphery recognizing unit based on the information acquired by the information acquiring unit and the explanatory information generated by the explanatory information generating unit.

Provided are methods for scalable and realistic camera blockage dataset generation, which can include generating synthetic images depicting a blockage on or near an imaging sensor. The synthetic images may be created by combining one or more chroma key-extracted partial blockage image with one or more background images, the combination of which can provide a scalable blockage dataset. Metadata for each synthetic image can be generated along with the synthetic image, by annotating the portion of the synthetic image represented by the chroma key-extracted partial blockage image as constituting blockage. The synthetic images can be used to increase the accuracy of machine learning models trained to identify blockage by increasing the volume of data available for such training.

A method for providing assistance in driving includes capturing an image of a second moving vehicle when a first moving vehicle is moving and obtaining basic information of the second moving vehicle according to the image thereof, the basic information of the second moving vehicle comprising weight information of the second moving vehicle. Driving information of the first moving vehicle is obtained, and a safe distance between the first moving vehicle and the second moving vehicle is determined according to the driving information of the first moving vehicle and the basic information of the second moving vehicle. The current distance between the first moving vehicle and the second moving vehicle is detected, and a warning is output if the distance between the first moving vehicle and the second moving vehicle is less than the safe distance.