An example vehicle can include a sensor platform and a controller that is configured to determine an object that is in front of the vehicle, determine the object as a hazard by at least one of determining, using dead reckoning, that the object is in a path of travel of the vehicle that will cause the object to travel under a restricted zone of the vehicle and/or the object has a height that is higher than a vehicle ride height.

Embodiments are disclosed to calculate and store rock climbing navigational data using a rock climbing watch including a sensor array configured to measure an orientation of the rock climbing watch and a processor configured to calculate an angle between two rock face locations along a rock climbing route based upon an orientation of the rock climbing watch when it is pointed from a current rock face location towards another rock face location. A processor may determine route segments for traversing the rock climbing route. A processor may convert route climbing navigational data into a three-dimensional wireframe rock climbing navigational map including rock face locations, calculated distances and angles between rock face locations, and a rock climbing route. The rock climbing watch may present information to guide the user along a rock climbing route by displaying a graphical directional indicator and a distance to the next rock face location.

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.

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 system and a method are disclosed for determining a range of potential distances between a work machine and an object. The system receives an image captured by a camera on the work machine and identifies an object in the image. The system determines an angle between the camera and the object, a height associated with the object, and an uncertainty associated with the height. Based on the angle, the height, and the uncertainty, the system determines a range of potential distances between the work machine and the object. The shortest distance in the range is compared to a threshold distance for safe operation of the work machine. When the shortest distance in the range is less than the threshold distance, the system causes the work machine to perform a safety action.

Techniques for displaying a 3d map view of a 3d geographical map are provided. A database stores data which defines the 3d geographical map in a layered hierarchy comprising data layers which can be individually rendered for the 3d map view. The database comprises a data layer of a digital terrain model which is modelling a terrain surface and a data layer of a 3d road network comprising 3d road elements. A processor is configured for selecting specific data layers of the database in response to a resolution setting of the 3d map view and to render the 3d map view using the selected specific data layer.

A method of creating a orthomosaic of a corridor area, the corridor area at least partially described by a corridor path, the method comprising flying an aircraft along a primary flight line approximating the corridor path and capturing a sequence of primary images; flying the aircraft along a secondary flight line substantially parallel to the corridor path and capturing a sequence of secondary images; identifying, in the primary images and secondary images, common features corresponding to common ground points; estimating, via bundle adjustment and from the common ground points, an exterior orientation associated with each primary image and a three-dimensional position associated with each ground point; orthorectifying, using at least some of the exterior orientations and at least some of the three-dimensional ground point positions, at least some of the primary images; and merging the orthorectified primary images to create the orthomosaic.

Embodiments include a method for pruning anchor points from a feature map generated from a Light Detections And Ranging (LiDAR) point cloud, the method comprising: receiving, by a navigation system, a LiDAR point cloud from a LiDAR sensor, the LiDAR point cloud comprising data representing one or more objects in physical surroundings detected by the LiDAR sensor; extracting, by the navigation system, a feature map from the LiDAR point cloud, the feature map comprising a plurality of anchor points, each anchor point defined by an anchor box; smoothing, by the navigation system, the extracted feature map; determining, by the navigation system, density of pixels within the anchor box of each anchor point; and pruning, by the navigation system, anchor points from the feature map based on a plurality of factors related to the determined density of pixels within the box of each anchor point.

In an example, the autonomous vehicle (“AV”) can be configured among the other vehicles and railway to communicate with a rider on a peer to peer basis to pick up the rider on demand from a location on a track, like a railway, tram or other track, rather than the rider being held hostage to a fixed railway schedule. The rider can have an application on his/her cell phone, which tracks each of the AVs, and contact them using the application on the cell phone. In an example, the AV is configured for both on-track and off track operation with different operating parameters for on-track and off track, including speed, degree of autonomy, sensors used etc.