Systems and method are provided for controlling a vehicle. In one embodiment, a method includes: initiating, by a controller onboard the vehicle, a first laser pulse from a first laser device; initiating, by a controller onboard the vehicle, a second laser pulse from a second laser device, wherein the initiating the second laser pulse is based on a phase shift angle; receiving, by the controller onboard the vehicle, first return data and second return data as a result of the first laser pulse and the second laser pulse; interleaving, by the controller onboard the vehicle, the first return pulse and the second return pulse to form a point cloud; and controlling, by the controller onboard the vehicle, the vehicle based on the point cloud.

A utility vehicle includes: a travel structure including a front wheel, a rear wheel, a steering structure mounted to the front wheel, and a drive source that drives the front wheel and/or the rear wheel; circuitry that controls the travel structure to effect autonomous travel without manned operation in a given travel area; a route setter that sets a travel route for the autonomous travel; a vehicle location detector that detects a location of the utility vehicle; and a target detector that detects a monitoring target in the travel area. In case that the monitoring target is detected at a location during the autonomous travel, the circuitry stores the location of the monitoring target as history information. The route setter sets a reference point at the location where the monitoring target was detected and sets the travel route based on the reference point.

The present invention relates to a system, method, infrastructure, and vehicle for performing automated valet parking. The present disclosure enables an unmanned vehicle to autonomously move to and park at an empty parking space by communicating with a parking infrastructure. The present disclosure enables an unmanned vehicle to autonomously move from a parking space to a pickup zone by communicating with a parking infrastructure.

An autonomous moving apparatus includes a moving unit moving the apparatus, a detector detecting distances from surrounding objects and shapes of the objects, and a controller. When a route of the apparatus is adjusted to a route of a follow target under control of the moving unit, the controller controls the moving unit so that the apparatus continues to move without changing the route of the apparatus if the follow target has changed the route but an obstacle having a possibility of causing a movement abnormality when the apparatus moves over the obstacle is present between the route of the follow target and the route of the apparatus. When the obstacle is no longer present between the route of the follow target and the route of the apparatus, the controller controls the moving unit so that the route of the apparatus is adjusted to the route of the follow target.

A method may include receiving a request for drone-assisted media capture from a vehicle located at a first location, the request specifying one or more user preferences, selecting an unmanned aerial vehicle that is able to perform the drone-assisted media capture based on the user preferences, causing the selected unmanned aerial vehicle to travel to the first location, and causing the selected unmanned aerial vehicle to capture media at the first location based on the user preferences.

Vehicles can be equipped to operate in both autonomous and occupant piloted mode. Refueling stations can be equipped to refuel autonomous vehicles without occupant assistance. Refueling stations can be equipped with a fueling control computer that communicates with vehicles via wireless networks to move vehicles between waiting zones, service zones and served zones. Refueling stations can include liquid fuel, compressed gas and electric charging.

The technology relates to developing a highly accurate understanding of a vehicle's sensor fields of view in relation to the vehicle itself. A training phase is employed to gather sensor data in various situations and scenarios, and a modeling phase takes such information and identifies self-returns and other signals that should either be excluded from analysis during real-time driving or accounted for to avoid false positives. The result is a sensor field of view model for a particular vehicle, which can be extended to other similar makes and models of that vehicle. This approach enables a vehicle to determine when sensor data is of the vehicle or something else. As a result, the detailed modeling allowing the on-board computing system to make driving decisions and take other actions based on accurate sensor information.

Systems and methods are directed to determining autonomous vehicle scenarios based on autonomous vehicle operation data. In one example, a computer-implemented method for determining operating scenarios for an autonomous vehicle includes obtaining, by a computing system comprising one or more computing devices, log data representing autonomous vehicle operations. The method further includes extracting, by the computing system, a plurality of attributes from the log data. The method further includes determining, by the computing system, one or more scenarios based on a combination of the attributes, wherein each scenario includes multiple scenario variations and each scenario variation comprises multiple features. The method further includes providing, by the computing system, the one or more scenarios for generating autonomous vehicle operation analytics.

A system and method for controlling an autonomous vehicle to affect a view seen by an occupant of the autonomous vehicle is described. In one embodiment, a method for controlling an autonomous vehicle to affect a view seen by an occupant of the autonomous vehicle includes determining a navigation route, determining content associated with the navigation route, monitoring current conditions of the autonomous vehicle and the occupant, determining, based on the current conditions, whether to change a position of the vehicle to affect the view seen by the occupant, and when the current conditions permit, moving the autonomous vehicle to affect the view seen by the occupant.

A method includes identifying sensor data associated with corresponding distal ends of one or more axles of an autonomous vehicle (AV). The method further includes determining, based on the sensor data, mass distribution data of the AV. The mass distribution data is associated with a first load proximate a first distal end of a first axle of the AV and a second load proximate a second distal end of the first axle of the AV. The method further includes causing, based on the mass distribution data, performance of a corrective action associated with the AV.