Embodiments of the present disclosure relate to a method and apparatus for generating information. The method can include: acquiring first driving environment data of a target road segment; comparing the first driving environment data with pre-stored second driving environment data of the target road segment, and determining a difference between the first driving environment data and the second driving environment data; and generating, in response to determining the difference satisfying a preset condition, road abnormality information.

An apparatus, method and computer program product provide a route using a map layer of one or more sound events. For example, the apparatus receives a user preference indicating a sound type, identifies a road segment associated with the sound type from a plurality of road segments, selects a subset from the plurality of road segments as a route based on association of the subset with respect to the road segment, and outputs the route.

An approach is provided for maintaining map accuracy considering tectonic plate movements. The approach, for example, involves processing data to determine a tectonic plate on which a map feature of a geographic database is located. A stored position of the map feature is associated with a time epoch assigned to the stored position. The approach also involves determining a velocity of a movement of the tectonic plate. The approach further involves monitoring an estimated position of the map feature over time based on the stored position and the velocity of the movement of the tectonic plate. The approach further involves automatically flagging or updating the stored position of the map feature based on determining that the estimated position differs from the stored position by more than a distance threshold.

The system assigns a plurality of drivers to one or more identified routes. An onboard computing system of a vehicle collects or generates a plurality of vehicle events, which are received at a server via a network. The onboard computing system includes a plurality of sensors and subsystems that are connected to monitor various vehicle components, such as braking, steering, and radar, as well as one or more cameras. A difficulty of each of the identified routes is characterized based on the plurality of vehicle events. A performance of each driver is characterized based on a subset of the plurality of vehicle events collected or received from the vehicle of each driver.

Systems, methods, and non-transitory computer-readable media can determine map information defining a map, wherein the map comprises a plurality of regions. A quality level is assigned to each region of the plurality of regions based on map information available for that region. The quality level is associated with at least one of: a resolution metric, a volume metric, a recency metric, a verification metric, or an elegance metric associated with the map information available for that region. A first region of the plurality of regions is identified that is at risk of being downgraded to a lower quality level. Instructions are issued to one or more vehicles that cause the one or more vehicles to traverse the first region and capture sensor data within the first region.

A map processing system includes a feature quantity calculating section configured to calculate a feature quantity of a feature point included in a map, an integrated feature point generating section configured to integrate a plurality of feature points to generate an integrated feature point, an integrated feature point matching section configured to match integrated feature points between a plurality of maps, a feature point matching section configured to match individual feature points between the plurality of maps, and a map processing section configured to process a map based on a matching result of the integrated feature points and a matching result of the individual feature points.

A vehicle control method according to an embodiment includes selecting a target vehicle to share low-level sensor information with based on the low-level sensor information obtained by a host vehicle and information on a structure included in high-definition map information, and receiving low-level sensor information of the structure from the target vehicle to update a sensor data map of the host vehicle, and performing map matching with the high-definition map information through feature points of a road extracted based on the updated sensor data map.

The present disclosure provides an intersection vertex height value acquisition method and apparatus, an electronic device and a storage medium, and relates to the field of artificial intelligence such as intelligent transportation. The method may include: determining roads with starting points or ending points located in a to-be-processed intersection; and performing, for any vertex in a polygonal intersection region corresponding to the intersection, the following processing: determining a road corresponding to the vertex, taking the determined road as a matching road of the vertex, and determining a height value of the vertex according to height values of endpoints on a road center line of the matching road.

In one embodiment, a system identifies a road to be navigated by an ADV, the road being captured by one or more point clouds from one or more LIDAR sensors. The system extracts road marking information of the identified road from the point clouds, the road marking information describing one or more road markings of the identified road. The system partitions the road into one or more road partitions based on the road markings. The system generates a point cloud map based on the road partitions, where the point cloud map is utilized to perceive a driving environment surrounding the ADV.

This disclosure relates to a distributed data center that includes resources carried by a fleet of vehicles. The system includes sensors configured to generate output signals conveying information related to the vehicles and/or the surroundings of vehicles. The system includes a remote computing server configured to maintain map data and distribute it to the fleet, including local map data to individual vehicles pertaining to their surroundings. Individual vehicles may compare the local map data with the information related to their individual surroundings. Based on such comparisons, individual vehicles may detect discrepancies between the local map data and the information related to their individual surroundings. The remote computing server may modify and/or update the map data based on the detected discrepancies.