Methods for providing a location of an object or objects of interest that may be performed by a processor of a computing device may include generating a map of an environment around the computing device by a first simultaneous location and mapping (SLAM) operation using information received from the optical sensor, identifying an object of interest in the environment of the computing device by an object recognition operation using information received from the optical sensor, determining a location of the object or objects of interest in the environment, and presenting the determined location of the object or objects of interest in the environment in response to a trigger event correlated to the object or objects of interest.

Provided are a method and apparatus for correcting positioning information, an electronic device and a storage medium, which relate to the field of artificial intelligence, in particular, to intelligent transportation technology and image processing technology. The specific implementation solution includes steps described below. A to-be-corrected image and positioning information of the to-be-corrected image are acquired. A target candidate reference image matching the to-be-corrected image is selected from candidate reference images having accurate positioning information. The positioning information of the to-be-corrected image is corrected according to accurate positioning information of the target candidate reference image. According to the solution of the present disclosure, the positioning information of the to-be-corrected image can be quickly corrected, and a guarantee is provided for accurately updating a navigation map by using the to-be-corrected image subsequently.

The present disclosure provides a method and an apparatus for monitoring road data, an electronic device and a storage medium, and relates to the field of intelligent transportation. A specific implementation solution involves: acquiring a feature expression of a road; predicting, according to the feature expression of the road and a pre-trained change prediction model, a probability that road data of the road will change; and collecting the road data of the road based on the probability that the road data will change.

Methods and systems for improved positioning accuracy relative to a digital map are disclosed, and which are preferably used for highly and fully automated driving applications, and which may use localisation reference data associated with a digital map. The invention further extends to methods and systems for the generation of localisation reference data associated with a digital map.

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.

Various embodiments include a sensor data system for a vehicle, the system comprising: a sensor in the vehicle, the sensor configured to collect environment data; a communication device configured to receive second environment model data from another vehicle; and a processing unit. The processing unit is configured to: calculate a first localization position of at least one of the vehicle or an object in the environment of the vehicle; generate first environment model data based at least in part on the environment data collected, wherein the first environmental model data and the second environment model data both contain localization data; compare the first localization data with the second localization data; and from the comparison determine a correction to the first localization position.

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.

A map generating system for autonomous movement of a robot includes a three-dimensional (3D) point cloud map input device, a 3D point cloud map regeneration device that extracts only 3D point cloud data corresponding to an arbitrary reference height from the 3D point cloud map to regenerate the 3D point cloud map, an unoccupied area generation device that extracts ground point cloud data from the 3D point cloud map to set an unoccupied area into which the robot is able to move, an occupied area generation device that extracts obstacle point cloud data from the 3D point cloud map to set an occupied area into which the robot is unable to move, and a two-dimensional (2D) map generation device that adds the unoccupied area and the occupied area to an area on the 3D point cloud map to generate a 2D map.

Methods for mapping safe and traversable routes for robot vehicles comprise using a deviation to create an operating range threshold that is to be included in a stored path and used by a robot vehicle as a threshold for normal operating conditions when the robot vehicle traverses a safe and traversable route. Methods for responding to unsafe conditions detected by a robot vehicle during operations comprise executing a contingency plan when a variance exceeds an operating delta and does not exceed an intervention delta. Methods for responding to anomalous conditions encountered by a robot vehicle comprise contacting a human controller when a variance exceeds an operating delta and an intervention delta.

A drive assist apparatus according to the present disclosure includes: a dynamic information acquisition unit acquiring, as dynamic information, at least one of subject vehicle dynamic information, the other vehicle dynamic information, and obstacle dynamic information from each of a plurality of vehicles; a map data acquisition unit acquiring map data including a shape of a traffic lane; a dynamic information management unit associating the dynamic information and the map data with each other and managing them; an interference prediction unit predicting whether or not the subject vehicle interferes with the other vehicle traveling in front of the subject vehicle along a traffic lane adjacent to a traffic lane along which the subject vehicle travels when the other vehicle avoids the obstacle based on the dynamic information and the map data; and a drive assist information generation unit generating drive assist information based on the predicted result.