An image enhancement system and method based on a generative adversarial network (GAN) model. The image enhancement system includes an acquiring unit, a training unit and an enhancement unit. The acquiring unit is configured to acquire a first image of a driving environment captured by a camera of a first vehicle and a second image of the driving environment captured by a camera of a second vehicle. The training unit is configured to train a GAN by using the first training image to obtain an image enhancement model. The enhancement unit is configured to enhance the second image by inputting the second image into the image enhancement model.

Methods and systems for managing information about fuel stations. A system includes an imaging system and a profile management system. The imaging system is used for generating imaging data for a fuel station onboard a vehicle. The profile management system receives the imaging data. The profile management system identifies selected information for the fuel station based on the imaging data received. The selected information includes an identification of the fuel station, pricing information, and at least one of fuel inventory information, or business hours information for the fuel station. The profile management system updates a profile for the fuel station based on the selected information. The profile management system sends at least a portion of the profile for the fuel station to a target system.

A method of automatic labeling of images for supervised machine learning includes obtaining images of roadside objects with a camera mounted to a vehicle, recording a position and orientation of the vehicle within a defined coordinate system while obtaining the images recording position information for each roadside object with the same defined coordinates system as used while recording the position and orientation of the vehicle, and correlating a position of each of the obtained images of the roadside objects with the position information of each roadside object in view of the recorded position and orientation of the vehicle. The images are labeled to identify the roadside objects in view of the correlated position of each of the obtained images of the roadside objects.

Systems, apparatus, methods, and articles of manufacture for Artificial Intelligence (AI) driving analysis and incentives, by utilizing on-board image object analysis to classify driving events and provide driving-based awards.

A host vehicle-based feature harvester is disclosed. In one implementation, the feature harvester includes memory and a processor configured to receive a plurality of images captured by a camera onboard the host vehicle, the plurality of images being representative of an environment of the host vehicle; analyze at least one image from the plurality of images to identify a representation of a lane mark; select at least one sample area of the representation of the lane mark, wherein the at least one sample area is associated with an image location of at least a portion of the representation of lane mark; determine a location identifier of the at least one sample area; determine a surface quality indicator associated with the at least one sample area; and cause transmission of the location identifier and the surface quality indicator to an entity remotely-located relative to the host vehicle.

An autonomous vehicle (AV) includes features that allows the AV to comply with applicable regulations and statues for performing safe driving operation. An example method for operating an AV includes receiving, from a sensor located on the AV, sensor data that captures a road sign located at a distance from the AV that is operating on a roadway; obtaining, from the sensor data, roadway information indicated by the road sign that corresponds to a segment of the roadway associated with the road sign that is ahead of a current position of the AV on the roadway; determining trajectory-related information for the AV for the distance that is based on the roadway information obtained from the sensor data; and causing the AV to travel in accordance with the trajectory-related information until a determination that the AV has arrived within the segment of the roadway associated with the road sign.

An evacuation running assistance system includes a peripheral environment recognizer to recognize at least a space of a road shoulder and a free space as a non-traffic portion, a time limit setter to set a time limit on the own vehicle for continuing evacuation running, and an evacuation place setter to determine at least one of the road shoulder space and the free space recognized by the peripheral environment recognizer as an evacuation place where the own vehicle is evacuated. A situation determiner determines a situation of an own vehicle as being in one of situations in which evacuation running is to be continued, running of an own vehicle is to be stopped on a lane, and road shoulder evacuation is to be performed based on the time limit. A controller controls the own vehicle based on the situation of the own vehicle determined by the situation determiner.

Systems and methods are provided for processing reports received from vehicles. A processing device perform operations comprising receiving a first report from a first vehicle, the first report generated by the first vehicle for a first hazard detected by the first vehicle; receiving a second report from a second vehicle, the second report generated by the second vehicle for a second hazard detected by the second vehicle; analyzing the first report and the second report to make a determination that the first report and the second report identify a related hazard; aggregating the first report and the second report into a consolidated report based on the determination that the first report and the second report identify a related hazard; and processing the consolidated report to identify a contributing factor to the related hazard.

The present invention relates to a method and system for automatic localisation of static objects in an urban environment. More particularly, the present invention relates to the use of noisy 2-Dimensional (2D) image data to identify and determine 3-Dimensional (3D) positions of objects in large scale urban or city environments. Aspects and/or embodiments seek to provide a method, system, and vehicle for automatically locating static 3D objects in urban environments by using a voting-based triangulation technique. Aspects and/or embodiments also provide a method for updating map data after automatically new 3D static objects in an environment.

Techniques for encoding image data are discussed. Image data containing multiple image frames can be received from an image sensor associated with a vehicle. A first image frame and a second image frame may be associated with a first data chunk. A first metadata associated with the first image frame can be identified. Additionally, a second metadata associated with the second image frame can be identified. The first data chunk can be created that includes the first image frame, the second image frame, the first metadata, and the second metadata. The first data chunk can be stored in a video container, where the video container can be indexed to access the first image frame with the first metadata or the second image frame with the second metadata.