Disclosed herein is a technique for obtaining information relating to a navigable network from devices (12) that are associated with users travelling within the navigable network. For example, a central server can issue requests to the devices (12) for automatically obtaining sensor data, with a request including a set of instructions for obtaining sensor data from one or more sensor(s) (13) accessible by the device (12). The request also includes a location-specific trigger. Thus, when it is determined that the device (12) has reached the location associated with the trigger, the device is able to automatically action the instructions in order to obtain the requested sensor data, which can then be reported back to the server.

A method of generating a scenic rating for segments of an electronic map involves obtaining probe data relating to the movement of a plurality of devices with respect to time in the area, and, for each one of a plurality of segments of the electronic map; identifying a set of positional data relating to the movement of devices along the navigable element represented by the segment, filtering the identified set of positional data relating to the movement of devices along the navigable element represented by the segment based on mode of transport to obtain one or more subset of the identified positional data relating to the movement of devices along the element represented by the segment which may be expected to relate to traversals of the navigable element for recreational purposes, using the or each obtained subset of the positional data to obtain one or more scenicity parameter which may be used in determining a scenic rating for the segment indicative of a scenicity of the navigable element represented by the segment, and using the one or more obtained scenicity parameter to determine a scenic rating for the segment.

The disclosure relates to a method of predicting one or more road attributes. The method may include providing trajectory data of a geographical area. The method may further include providing map data, wherein the map data may include image data of the geographical area. The method may further include extracting trajectory features from the trajectory data and extracting map features from the map data. The method may further include using at least one processor to predict road attributes by inputting the trajectory features and the map features in a neural network and by classifying an output of the neural network into prediction probabilities of the road attributes. The disclosure also relates to a data processing system; to a non-transitory computer-readable medium storing computer executable code; and to a method of training an automated predictor.

Methods, systems, and computer-readable media are disclosed herein that generate computer-executable instructions that are executed by an autonomous vehicle and cause the autonomous vehicle to follow a specific route to deliver or pickup of an item. Using an inference model, historical data of off-street terrain used for prior deliveries and on-street terrain in map data are leveraged to generate candidate routes for the “last 10 feet” of a delivery. One of the candidate routes is selected by the inference model. Then, computer-executable instructions are generated that, when executed by an autonomous vehicle, cause the autonomous vehicle to follow the selected route and perform the last 10 feet of delivery.

A system for generating a costmap for use in planning a path through an environment, the system including one or more suitably programmed processing devices configured to obtain a map of the environment, acquire traversal data indicative of an ability to traverse parts of the environment, for each part of the environment, calculate a traversal indicator indicative of the ability to traverse the part, the traversal indicator being calculated in a colour space, and colourise the map using the traversal indicators to thereby generate a colourised costmap indicative of an ability to traverse the environment, the colourised costmap allowing path planning to be performed.

A navigation system for a host vehicle may include a processor programmed to: receive, from an entity remotely located relative to the host vehicle, a sparse map associated with at least one road segment to be traversed by the host vehicle; receive point cloud information from a LIDAR system onboard the host vehicle, the point cloud information being representative of distances to various objects in an environment of the host vehicle; compare the received point cloud information with at least one of the plurality of mapped navigational landmarks in the sparse map to provide a LIDAR-based localization of the host vehicle relative to at least one target trajectory; determine an navigational action for the host vehicle based on the LIDAR-based localization of the host vehicle relative to the at least one target trajectory; and cause the at least one navigational action to be taken by the host vehicle.

A vehicle data collection system includes a vehicle-mounted sensor; a non-transitory computer readable medium configured to store instructions; and a processor connected to the non-transitory computer readable medium. The processor is configured to execute the instructions for generating an invariant feature map, using a first neural network; and comparing the invariant feature map to template data to determine a similarity between the invariant feature map and the template data, wherein the template data is received from a server. The processor is further configured to execute the instructions for determining whether the determined similarity is exceeds a predetermined threshold; and instructing a transmitter to send the sensor data to the server in response to a determination that the determined similarity exceeds the predetermined threshold.

An approach is provided for mapping a parking facility using multi-modal trajectories collected using mobile device(s). The approach, for example, involves collecting sensor data from sensor(s) of mobile device(s). The sensor data represents the multi-modal trajectories comprising (1) vehicle trajectory segments during which the device(s) traveling into/out of a parking facility, and (2) pedestrian trajectory segments during which the device(s) traveling to/from pedestrian entry/exit point(s) of the parking facility. The approach also involves processing the sensor data to determine semantic event(s) associated with parking in the parking facility based on the multi-modal trajectories. The approach further involves determining parking spot location(s) and/or orientation(s) of the parking facility based on the semantic event(s). The approach further involves generating map data indicating the parking spot location(s) and/or orientation(s). The approach further involves providing the map data as an output.

The present disclosure provides a device and method for generating lane information. The method includes obtaining information on a structure located around a vehicle and driving information of a surround vehicle based on information collected by a sensor and prestored navigation information; and generating geometric information on a travel lane of the vehicle in a road area based on the information on the structure and the driving information of the surround vehicle.

Technologies for managing interoperable high definition (HD) maps for autonomous vehicles includes a HD map server to distribute interoperable HD map tiles to various autonomous vehicles, each of which may be configured to utilize proprietary HD map tiles of different propriety formats. As such, each of the autonomous vehicles is configured to translate the interoperable HD map tile to the proprietary format used by that particular vehicle. Additionally, each autonomous vehicle may submit crowd-sourced sensor data to the interoperable HD map server using a universal data set by converting the sensor data to a universal data structure.