Methods and systems are provided for classification of the type and weight of a trailer being towed by a vehicle. The classification is based on a comparison between a real-time road gradient as determined by the vehicle system and an expected road gradient as determined from an off-board or an onboard map. Vehicle operations may be adjusted based on the classification of the attached trailer.

A system for identifying features of a roadway traversed by a host vehicle may include at least one processor programmed to: receive a plurality of images representative of an environment of the host vehicle; recognize in the plurality of images a presence of a road feature associated with the roadway; determine a location of a first point associated with the road feature relative to a curve representative of a path of travel of the host vehicle; determine a location of a second point associated with the road feature relative to the curve; and cause transmission to a server remotely located from the host vehicle of a representation of a series of points associated with locations along the road feature. The second point may be spaced apart from the first point, and the series of points may include at least the first point and the second point.

Provided is an autonomous vehicle including a storage configured to store a map including two-dimensionally represented road surface information and three-dimensionally represented structure information, a camera configured to obtain a two-dimensional (2D) image of a road surface in a vicinity of the vehicle, a light detection and ranging (LiDAR) unit configured to obtain three-dimensional (3D) spatial information regarding structures in a vicinity of the vehicle, and a controller comprising processing circuitry configured to determine at least one of the camera or the LiDAR unit as a position sensor, based on whether it is possible to obtain information regarding the road surface and/or the structures in the vicinity of the vehicle, to identify a position of the vehicle on the map corresponding to a current position of the vehicle using the position sensor, and performing autonomous driving based on the identified position on the map.

The present application discloses a road event detection method, an apparatus, a device and a storage medium, and relates to artificial intelligence, big data, intelligent transportation, automatic driving, and cloud computing technologies in data processing. A specific implementation is that: by obtaining road images collected and returned by multiple vehicles within a historical period, and performing image processing on single-frame road images to determine road events included in the road images, types and/or locations of the road events can be located accurately; further, by performing clustering on road event data of multiple frames of road images including the road events to obtain all road events occurring within the historical period and road event information, all road events occurring within the historical period can be determined accurately, and the types and/or the locations of the road events can be determined accurately, thereby improving the accuracy of road event detection.

Disclosed are systems and methods for determining a geographical area occupied by an intersection.

Systems and methods are provided for verifying the accuracy of a map. Information regarding objects, such as roadway or roadway-related elements may be encoded into a map to be used for navigation and/or control of a vehicle, such as an autonomous vehicle. In order to verify that the map accurately reflects the roadway or roadway-related elements making up a section of roadway, the vehicle may be driven/ridden along the same section of roadway. A camera feed can be captured of this subsequent traversal of the section of roadway to check if the map includes the requisite roadway or roadway-related elements. The known roadway or roadway-related elements can be occluded from view in the camera feed. Accordingly, if any roadway or roadway-related elements do appear in the camera feed, they can be more easily detected by a verifier, and the processor verification is simplified.

Aspects of the disclosure relate to generating scouting objectives in order to update map information used to control a fleet of vehicles in an autonomous driving mode. For instance, a notification from a vehicle of the fleet identifying a feature and a location of the feature may be received. A first bound for a scouting area may be identified based on the location of the feature. A second bound for the scouting area may be identified based on a lane closest to the feature. A scouting objective may be generated for the feature based on the first bound and the second bound.

A system, a method, and a computer program product for generating classification data of a sign. The method comprises obtaining map data of the sign, obtaining a map matched link and at least one parallel link associated with the sign based on the map data, determining presence data of at least one blocking link between the map-matched link and the at least one parallel link, wherein the at least one blocking link satisfies one or more of a heading criterion and a distance criterion. The method further comprises generating the classification data of the sign based on the presence data of the at least one blocking link.

The invention relates to a communication system for a vehicle, which device includes a sensor device, wherein the sensor device is arranged to capture sensor data when the sensor device moves. A receiving device receives reference data from an external management system and a processing device determines a difference between the captured sensor data and the corresponding reference data, wherein the determined difference between the captured sensor and the corresponding reference data is transmitted to the external management system.

A method for detecting a bumpy region of a road surface, an electronic device, a storage medium and a vehicle, are provided, and relate to the field of computer technology. The method includes: acquiring driving state data of a vehicle and an orientation of the vehicle; determining, according to the driving state data, whether the vehicle passes through the bumpy region of the road surface; and in a case where the vehicle passes through the bumpy region of the road surface, determining a location of the bumpy region of the road surface according to the driving state data and the orientation of the vehicle.