A system and method for large-scale lane marking detection using multimodal sensor data are disclosed. A particular embodiment includes: receiving image data from an image generating device mounted on a vehicle; receiving point cloud data from a distance and intensity measuring device mounted on the vehicle; fusing the image data and the point cloud data to produce a set of lane marking points in three-dimensional (3D) space that correlate to the image data and the point cloud data; and generating a lane marking map from the set of lane marking points.

A rover or semi-autonomous or autonomous vehicle may use an image classifier to determine a terrain class of regions of an image of the terrain ahead of the rover or vehicle. The regions of the images are used to estimate the slope of the terrain for the different regions. The terrain class and slope are used to predict an amount of slip the rover will experience when traversing the terrain of the different regions. A heuristic mapping for the terrain class may be applied to the predicted slip amount to determine a hazard level for the rover or vehicle traversing the terrain.

One or more information maps are obtained by an agricultural work machine. The one or more information maps map one or more agricultural characteristic values at different geographic locations of a field. An in-situ sensor on the agricultural work machine senses an agricultural characteristic as the agricultural work machine moves through the field. A predictive map generator generates a predictive map that predicts a predictive agricultural characteristic at different locations in the field based on a relationship between the values in the one or more information maps and the agricultural characteristic sensed by the in-situ sensor. The predictive map can be output and used in automated machine control.

One or more information maps are obtained by an agricultural work machine. The one or more information maps map one or more agricultural characteristic values at different geographic locations of a field. An in-situ sensor on the agricultural work machine senses an agricultural characteristic as the agricultural work machine moves through the field. A predictive map generator generates a predictive map that predicts a predictive agricultural characteristic at different locations in the field based on a relationship between the values in the one or more information maps and the agricultural characteristic sensed by the in-situ sensor. The predictive map can be output and used in automated machine control.

Route planning is provided to a vehicle (or vehicle driver) based at least in part on the type of vehicle and the terrain conditions between the originating location and destination. The vehicle may employ a terrain monitoring system including a surface-penetrating radar (SPR) system for obtaining SPR signals as the vehicle travels along a route; the obtained SPR signals may be used for navigation against reference images associated with the route. In some embodiments, a navigation server bases route selection in part on the terrain associated with various routes and characteristics of the vehicle.

This invention relates to an information management system of lawn profile data. It comprises a lawn profile information collecting tool for collecting information any pieces of lawns that need mowing jobs; wherein the lawn profile information collecting tool includes a data converter for converting such information to lawn profile data, and data processer for processing the lawn profile data locally into suitable formats and categories for uploading; a mobile device of a user being in communication with the lawn profile information collecting tool to receive the processed lawn profile data; a remote information processing center being in communication with the mobile device and the lawn profile information collecting tool to receive and process requests from the mobile device to upload the lawn profile data; wherein the remote information processing center includes a data storage unit for storing the lawn profile data for usage thereof by a designated lawn mower to perform mowing job, that is, using the stored lawn profile data associated with the particular piece of lawn as requested.

A soil measure, such as a soil cone index, and a vehicle index indicating the amount of force the vehicle exerts on the ground as it travels over the ground, are obtained and compared to identify a soil damage score. The soil damage score can be mapped over a field and an agricultural vehicle can be controlled based upon the soil damage score. In another example, a detector detects a fill level of a material storage compartment on an agricultural vehicle. The inflation pressure of tires on the agricultural vehicle is controlled based upon the detected fill level.

An apparatus for collecting feature data includes a memory configured to store, for each of vehicles, a mileage of the vehicle or the amount of feature data transmitted by the vehicle and representing a feature related to travel of the vehicles; and one or more processors configured to set a transmission condition for permitting transmission of the feature data for each of the vehicles so that, of the vehicles, the transmission condition for a vehicle with a lower mileage or a vehicle having transmitted a smaller amount of the feature data is more relaxed, and notify each of the vehicles via a communication circuit of a collection instruction to instruct the vehicle to collect the feature data under the transmission condition set for the vehicle.

Disclosed embodiments include systems, vehicles, and methods for tracking off-road travel. In an illustrative embodiment, a system includes a computing device having computer-readable media storing computer-executable instructions configured to cause the computing device to monitor a vehicle location. The vehicle location is identified on map data for an area encompassing the location and including road data for recognized roads in the area. Off-road travel is detected in response to determining that the vehicle location is outside of the recognized roads. Travel data is recorded representing the off-road travel of the vehicle.

Implementations are directed to assigning corresponding semantic identifiers to a plurality of rows of an agricultural field, generating a local mapping of the agricultural field that includes the plurality of rows of the agricultural field, and subsequently utilizing the local mapping in performance of one or more agricultural operations. In some implementations, the local mapping can be generated based on overhead vision data that captures at least a portion of the agricultural field. In these implementations, the local mapping can be generated based on GPS data associated with the portion of the agricultural field captured in the overhead vision data. In other implementations, the local mapping can be generated based on driving data generated during an episode of locomotion of a vehicle through the agricultural field. In these implementations, the local mapping can be generated based on GPS data associated with the vehicle traversing through the agricultural field.