An autonomous vehicle includes an array of sensors, a processor, and a switch. The array of sensors generate sensor data related to one or more objects in an external environment of the autonomous vehicle and the processor determines an environmental context. The switch transfers the sensor data from the array of sensors to the processor, where the switch is configured to: (a) receive first sensor data from a first sensor group of the array of sensors; (b) receive second sensor data from a second sensor group of the array of sensors; (c) determine an order of transmission of the first sensor data over the second sensor data in response to the environmental context; and (d) transmit the first sensor data to the processor prior to transmitting the second sensor data based on the order of transmission.

A supervisory control system is disclosed that provides an operator situational awareness interface use with monitoring a plurality of automated vehicles (AVs). The system is configured to: generate a map of a geographical area of interest; obtain location data and perceived risk data for a plurality of AVs in the geographical area; generate a vehicle icon corresponding to each AV; position the vehicle icon for each AV on the map based on the location data for a corresponding AV; apply a color coding to each vehicle icon based on a perceived risk level for a corresponding AV; and signal a display device to display an AV fleet map graphic that includes the color coded vehicle icons positioned on the map. The controller may be further configured to: generate an AV servicing queue graphic that displays vehicle icons in an order based on a determined servicing priority.

An autonomous robotic vehicle is capable of detecting, identifying, and locating the source of gas leaks such as methane. Because of the number of operating components within the vehicle, it may also be considered a robotic system. The robotic vehicle can be remotely operated or can move autonomously within a jobsite. The vehicle selectively deploys a source detection device that precisely locates the source of a leak. The vehicle relays data to stakeholders and remains powered that enables operation of the vehicle over an extended period. Monitoring and control of the vehicle is enabled through a software interface viewable to a user on a mobile communications device or personal computer.

An autonomous guided vehicle includes a frame, a drive section, a payload handler, a sensor system, and a supplemental sensor system. The sensor system has electro-magnetic sensors, each responsive to interaction or interface of a sensor emitted or generated electro-magnetic beam or field with a physical characteristic, the electro-magnetic beam or field being disturbed by interaction or interface with the physical characteristic, and which disturbance is detected by and effects sensing of the physical characteristic. The sensor system generates sensor data embodying at least one of a vehicle navigation pose or location information and payload pose or location information. The supplemental sensor system supplements the sensor system, and is, at least in part, a vision system with cameras disposed to capture image data informing the at least one of a vehicle navigation pose or location and payload pose or location supplement to the information of the sensor system.

Disclosed herein are a deep network learning method using an autonomous vehicle and an apparatus for the same. The deep network learning apparatus includes a processor configured to select a deep network model requiring an update in consideration of performance, assign learning amounts for respective vehicles in consideration of respective operation patterns of multiple autonomous vehicles registered through user authentication, distribute the deep network model and the learning data to the multiple autonomous vehicles based on the learning amounts for respective vehicles, and receive learning results from the multiple autonomous vehicles, and memory configured to store the deep network model and the learning data.

A robotic cleaning system may include a robotic cleaner configured to generate a map of an environment and a mobile device configured to communicatively couple to the robotic cleaner, the robotic cleaner configured to communicate the map to the mobile device. The mobile device may include a camera configured to generate an image of the environment, the image comprising a plurality of pixels, a display configured to display the image and to receive a user input while displaying the image, the user input being associated with one or more of the plurality of pixels, a depth sensor configured to generate depth data that is associated with each pixel of the image, an orientation sensor configured to generate orientation data that is associated with each pixel of the image, and a mobile controller configured to localize the mobile device within the map using the depth data and the orientation data.

A marine drone system utilizing an unmanned aerial vehicle to provide visual feedback for conditions including temperature, depth, and conditions which may suggest favorable fishing conditions, such as weed lines, flotsam, breaks, and objects, such as birds or fish. The system utilizes a plurality of sensors, including, but not limited to, cameras, laser, GPS, radar, and LIDAR. The visual feedback may be shown as a video fees or a map, wherein the feedback is shown as a visual backgrounds, wherein an overlay of interactive functions provides information regarding the conditions. The system also includes method steps for implementing, obtaining, and displaying the information. The system hardware includes the unmanned aerial vehicle, a base station, and a hardwired tether between the unmanned aerial vehicle and the base station providing power and bi-directional data transfer.

Provided is a navigation system for a leader vehicle leading follower vehicles, including: the leader vehicle, configured to transmit, real-time movement data to follower vehicles; and, the follower vehicles, each comprising: a signal receiver for receiving the data from the leader vehicle; sensors configured to detect at least one maneuverability condition; a memory; a vehicle maneuver controller; a distance sensor; and a processor configured to: determine a route for navigating the local follower vehicle from an initial location; determine a preferred range of distances from the vehicle in front of the respective follower vehicle that the respective follower vehicle should stay within; determine a set of active maneuvering instructions for the respective follower vehicle based on at least a portion of the data received from the guiding vehicle; determine a lag in control commands; and, execute the set of active maneuvering instructions in the respective follower vehicle.

Systems and methods for vehicle lane change control are described. Some implementations may include determining a kinematic state of a vehicle moving in an origin lane; detecting, based on data from one or more sensors of the vehicle, objects that are moving in a target lane of the road; determining a headway constraint in terms of a preparation time, a preparation acceleration to be applied to the vehicle during the preparation time, and an execution time during which the vehicle is to transition from the origin lane to the target lane; determining values of the preparation time, the execution time, and the preparation acceleration subject to a set of constraints including the headway constraint; and determining a motion plan that will transition the vehicle from the origin lane to the target lane based at least in part on the preparation time, the execution time, and the preparation acceleration.

A population density map of a region is generated by dividing the region into cells and allocating a population of the region only to the cells that are accessible to people, or are believed to be populated. Each of the cells is classified based on one or more ground features of the cells, and an adjustment factor for each of the cells is determined based at least in part on the classifications. Equal shares of the population of the region are allocated to each of the cells that is accessible or populated, and the equal shares are multiplied by the adjustment factors determined for the respective ones of the cells to calculate a population for each of such cells.