According to the present disclosure, a map matching apparatus includes a map information acquisition unit configured to acquire map information expressed by a plurality of links, a movement information acquisition unit configured to acquire movement information of a moving body on a predetermined route, and a matching unit configured to specify a link string, which is a string of the links corresponding to the route, based on the map information and the movement information, in which the matching unit is configured to generate a physical network, which is a road network, based on the connection relationship and the map information, generate a hierarchical logical network in which the physical network is duplicated in a plurality of layers and configured in a hierarchical manner, and specify the link string indicating a minimized cost in the hierarchical logical network.

A system for generating linear feature data is provided. The system may determine, from sensor data, detection data associated with at least one link. The at least one link comprises a plurality of sub links. The system may further determine, using map data, one or more linear feature clusters for each of the plurality of sub links, based on the detection data. Furthermore, the system may determine a plurality of linear feature groups for the at least one link, based on at least one set of feature matched distances and the determined linear feature clusters, where a given linear feature group respectively comprises at least one first linear feature cluster associated with a first sub link and at least one second linear feature cluster associated with a second sub link. Furthermore, the system may generate the linear feature data, based on the plurality of linear feature groups.

A method and control system for generating and updating digital maps using a plurality of passages along a road portion by at least one road vehicle is provided. The method comprises obtaining positioning data and sensor data of each passage from the at least one road vehicle. Further, the method comprises forming a sub-map representation of the surrounding environment at each obtained longitudinal position based on the obtained sensor data, and estimating a longitudinal error for each obtained longitudinal position within each segment. Furthermore, the method comprises determining a new plurality of longitudinal positions of each road vehicle for each passage by applying the estimated longitudinal error on each corresponding obtained longitudinal position, and applying the determined new plurality of longitudinal positions on associated sensor data in order to generate a first layer of a map representation of the surrounding environment along the road portion.

The present disclosure provides a method and apparatus for automatically producing map data. The method includes: performing track rectification on crowdsourcing tracks based on corresponding standard tracks, and locating each map element included, based on depth information of track point images included in the rectified crowdsourcing tracks; comparing a latest map element obtained based on the rectified crowdsourcing tracks locating and an old map element at a corresponding locating position using a pre-built entity semantic map; determining, in response to a change in the latest map element compared to the old map element, a target processing method according to a processing standard of a changed map element pre-abstracted from a map element update specification; and processing the latest map element according to the target processing method to obtain a processed latest map.

An apparatus and method for controlling an automatic lane change of a vehicle in consideration of a speed limit are configured to obtain information about the speed limit of a road from map information including information about the speed limit of the road, and calculate a speed at which an automatic lane change function is operable based on the speed limit of the road. As a result, it is possible to control the automatic lane change of the vehicle while automatically complying with laws and/or regulations in consideration of the speed limit of the road.

An apparatus, method and computer program product are provided for providing signature reduction for a vehicle. For example, the apparatus receives a destination for a vehicle as input, selects a subset from a plurality of road segments as a route from a location to the destination, and outputs the route or a portion thereof. The subset is selected based on association of each of the plurality of road segments with respect to a source, and the source is capable of acquiring vehicle signature information.

System, methods, and other embodiments described herein relate to training a depth model for joint depth completion and prediction. In one arrangement, a method includes generating depth features from sparse depth data according to a sparse auxiliary network (SAN) of a depth model. The method includes generating a first depth map from a monocular image and a second depth map from the monocular image and the depth features using the depth model. The method includes generating a depth loss from the second depth map and the sparse depth data and an image loss from the first depth map and the sparse depth data. The method includes updating the depth model including the SAN using the depth loss and the image loss.

In a computer-implemented method of assessing driving performance using route scoring, driving data indicative of operation of a vehicle while the vehicle was driven on a driving route may be received. Road infrastructure data indicative of one or more features of the driving route may also be received. A route score for the driving route may be calculated using the road infrastructure data, and a driving performance score for a driver of the vehicle may be calculated using the driving data and the route score for the driving route. Data may be sent to a client device via a network to cause the client device to display the driving performance score and/or a ranking based on the driving performance score, and/or the driving performance score may be used to determine a risk rating for the driver of the vehicle.

Techniques are disclosed for collaborative map construction using multiple vehicles. Such a system may include a ground vehicle including a first computing device and a first scanning sensor, and an aerial vehicle including a second computing device and a second scanning sensor. The ground vehicle can obtain a first real-time map based on first scanning data using the first scanning sensor, and transmit a first real-time map and position information to the aerial vehicle. The aerial vehicle can receive the first real-time map and the position information from the first computing device, obtain a second real-time map based on second scanning data collected using the second scanning sensor, and obtain a third real-time map based on the first real-time map and the second real-time map.

A system, a method and a device for planning a driving path for a vehicle are described. In one example aspect, the device is configured to: analyze sense data to obtain positioning data of vehicles; assign vehicle transportation tasks to an unmanned vehicle and a manned vehicle in the predetermined area in accordance with a predetermined transportation task, each vehicle transportation task including a transportation start point and a transportation end point; plan driving paths for the unmanned vehicle and the manned vehicle based on the assigned vehicle transportation tasks, the vehicle positioning data and map data; transmit the assigned transportation task and the planned driving path for the unmanned vehicle to the unmanned vehicle; and transmit the assigned transportation task and the planned driving path for the manned vehicle to a mobile device corresponding to the manned vehicle.