According to one aspect, a method is provided to determine whether an autonomous system is ready to be deployed or is otherwise ready for use, scene-based metrics, or metrics based on instances of scenarios. Scene-based metrics are mapped, or otherwise translated, to distance-based metrics such that substantially standard distance-based metrics may be used to gate the readiness of an autonomy system for deployment.

According to one aspect, a method is provided to determine whether an autonomous system is ready to be deployed or is otherwise ready for use, scene-based metrics, or metrics based on instances of scenarios. Scene-based metrics are mapped, or otherwise translated, to distance-based metrics such that substantially standard distance-based metrics may be used to gate the readiness of an autonomy system for deployment.

Included is a surface cleaning service system including: one or more robotic surface cleaning devices, each including: a chassis; a set of wheels; one or more motors to drive the wheels; one or more processors; one or more sensors; and a network interface card, wherein the one or more processors of each of the one or more robotic surface cleaning devices determine respective usage data. A control system or the one or more processors of each of the one or more robotic surface cleaning devices is configured to associate each usage data with a particular corresponding robotic surface cleaning device of the one or more robotic surface cleaning devices.

Disclosed are autonomous vehicles that may autonomously navigate at least a portion of a route defined by a service request allocator. The autonomous vehicle may, at a certain portion of the route, request remote assistance. In response to the request, an operator may provide input to a console that indicates control positions for one or more vehicle controls such as steering position, brake position, and/or accelerator position. A command is sent to the autonomous vehicle indicating how the vehicle should proceed along the route. When the vehicle reaches a location where remote assistance is no longer required, the autonomous vehicle is released from manual control and may then continue executing the route under autonomous control.

System for monitoring stability of autonomous robot, including a GNSS navigation receiver including antenna, analog front end, plurality of channels, inertial measurement unit (IMU) and a processor, all generating navigation and orientation data for the robot; based on the navigation and orientation data, calculating position and direction of movement for the robot; calculating spatial and orientation coordinates z.sub.1, z.sub.2 of the robot, relating to the position and direction of movement; continuing with programmed path for the robot for any spatial and orientation coordinates z.sub.1, z.sub.2 within an attraction domain, where a measure V(z) of distance from zero in z.sub.1, z.sub.2 plane are defined by Lurie-Postnikov functions and is less than 1; for spatial and orientation coordinates outside the attraction domain with V(z)>1, terminating the programmed path and generating notification.

System for monitoring stability of autonomous robot, including a GNSS navigation receiver including antenna, analog front end, plurality of channels, inertial measurement unit (IMU) and a processor, all generating navigation and orientation data for the robot; based on the navigation and orientation data, calculating position and direction of movement for the robot; calculating spatial and orientation coordinates z.sub.1, z.sub.2 of the robot, relating to the position and direction of movement; continuing with programmed path for the robot for any spatial and orientation coordinates z.sub.1, z.sub.2 within an attraction domain, where a measure V(z) of distance from zero in z.sub.1, z.sub.2 plane are defined by Lurie-Postnikov functions and is less than 1; for spatial and orientation coordinates outside the attraction domain with V(z)>1, terminating the programmed path and generating notification.

An autonomous vehicle receives geographic location data defined via a mobile computing device operated by a user. The geographic location data is indicative of a device position of the mobile computing device. The autonomous vehicle also receives image data generated by the mobile computing device. The image data is indicative of a surrounding position nearby the device position. The surrounding position is selected from an image captured by a camera of the mobile computing device. A requested vehicle position (e.g., a pick-up or drop-off location) is set for a trip of the user in the autonomous vehicle based on the geographic location data and the image data. A route from a current vehicle position of the autonomous vehicle to the requested vehicle position for the trip of the user in the autonomous vehicle is generated. Moreover, the autonomous vehicle can follow the route to the requested vehicle position.

A method includes generating a parameter of a trajectory associated with a scenario using a path planner. The parameter is generated based on a training dataset. The method includes comparing the parameter of the trajectory against a validation parameter associated with a validation dataset. The validation parameter is based on human-based vehicle driving trajectory data associated with scenarios that satisfy a level of similarity with the scenario. The method further includes determining a level of similarity between the parameter associated with the scenario and the validation parameter associated with the scenarios, and, subsequent to determining that the level of similarity fails to satisfy a similarity threshold, the method concludes with providing training data associated with the scenario to the training dataset so that a subsequent parameter of a subsequent trajectory generated by the path planner and associated with the scenario satisfies the level of similarity against the validation parameter.

The technology relates to controlling a vehicle in an autonomous driving mode, the method. For instance, a vehicle may be maneuvered in the autonomous driving mode using pre-stored map information identifying traffic flow directions. Data may be received from a perception system of the vehicle identifying objects in an external environment of the vehicle related to a traffic redirection not identified the map information. The received data may be used to identify one or more corridors of a traffic redirection. One of the one or more corridors may be selected based on a direction of traffic flow through the selected corridor. The vehicle may then be controlled in the autonomous driving mode to enter and follow the selected one of the one or more corridors based on the determined direction of flow of traffic through each of the one or more corridors.

The present application discloses is a scheduling method and system for fully autonomous waterborne inter terminal transportation, and belongs to the field of transportation. The method includes: establishing a dynamic scheduling model for waterborne Autonomous guided vessels (wAGVs); quickly inserting the dynamically arriving transportation tasks into all the existing wAGV paths, calculating the insertion cost and selecting the path and position with the lowest insertion cost to obtain updated initial paths; improving the initial path using a heuristic algorithm based on tabu search to obtain quasi-optimal wAGV paths; executing the scheduling wAGV paths.