A method for performing an at least partially automatic vehicle function of a vehicle depending on a travel route to be assessed by means of a driver assistance system is disclosed. The method comprises providing a plurality of clusters from route data with respect to at least one known travel route, wherein the clusters group the route data sectionwise according to predefined geometric parameters. The method comprises providing recorded course data that indicate a course of the travel route to be assessed and applying the clusters to the course data in order to divide the travel route to be assessed into route sections corresponding to the clusters. The method comprises determining at least one uncertainty quantity which is characteristic of an uncertainty with respect to the assignment made and determining a control quantity as a function of the uncertainty quantity and providing the control quantity for performing the vehicle function.

An automatic working system comprises a self-moving device moving and working in a working region, a handheld device and a control module. The handheld device is configured to move along a perimeter of the working region with a user and comprises a detecting module, detecting the perimeter information of the working region; and an input module, receiving a command of the user for detecting the perimeter information. The control module comprises a perimeter setting unit, generating virtual data of the perimeter, an area calculation unit calculating the area of the working region and a scheduling unit generating a working schedule. The self-moving device comprises a working module, a driving module and a controller. The controller controls the self-moving device to work according to the working schedule.

Autonomous vehicles are an exciting prospect to the future of driving. However, concerns about the decision-making made by the AI controlling a vehicle has been of concern, particularly in light of high-profile accidents. We can alleviate some concern, introduce better decisions, and also train an AI to make better decisions by introducing a remote viewer's, e.g., a human's, reaction to a possibly complex environment surrounding a vehicle that includes a potential threat to the vehicle. One or more remote viewer may provide a recommended response to the threat that may be incorporated in whole or in part in how the vehicle reacts. Various ways to engage and utilize remote viewers are proposed to improve the likelihood of receiving useful recommendations, including modifying how the environment is presented to a remote viewer to best suit the remote viewer, e.g., perhaps present the threat in a game.

An infrastructure is provided for improving the safety of autonomous systems. An autonomous vehicle management system (AVMS) controls one or more autonomous functions or operations performed by a vehicle or machine such that the autonomous operations are performed in a safe manner. The AVMS is capable of dynamically controlling the behavior of sensors associated with a vehicle. For example, for a sensor, the AVMS can dynamically change and control what sensor data is captured by the sensor and/or communicated from the sensor to the AVMS (e.g., granularity/resolution, field of view, control zoom), when the data is captured by the sensor and/or communicated by the sensor to the AVMS (e.g., on-demand, according to a schedule), and how the data is captured by the sensor and/or communicated from the sensor to the AVMS (e.g., communication format, communication protocol, rate of data communication).

The present disclosure relates generally to techniques for the kinematic estimation and dynamic behavior estimation of autonomous heavy equipment or vehicles to improve navigation, digging and material carrying tasks at various industrial work sites. Particularly, aspects of the present disclosure are directed to obtaining a set of sensor data providing a representation of operation of an autonomous vehicle in a worksite environment, estimating, by a trained model comprising a Gaussian process, a set of output data based on the set of sensor data, controlling an operation of the autonomous vehicle in the worksite environment using input data derived from the set of sensor data and the set of output data, obtaining actual output data from the operation of the autonomous vehicle in the worksite environment, and updating the trained model with the input data and the actual output data.

The present disclosure relates to an indoor monocular navigation method based on cross-sensor transfer learning and a system thereof. Determining an preliminary autonomous navigation model according to simulated laser radar data; acquiring actual single-line laser radar data and monocular camera data of the mobile robot simultaneously in an actual environment; determining the heading angle of the mobile robot according to the actual laser radar data; determining a laser radar monocular vision navigation model, according to the generated heading angle of the mobile robot and the monocular camera data at a the same moment and by using a Resnet18 network and a pre-trained YOLO v3 network; determining a heading angle of the mobile robot at the current moment, according to the acquired monocular camera data and by using the laser radar monocular vision navigation model; performing navigation of the mobile robot.

Systems and methods are provided for learning world graphs to accelerate hierarchical reinforcement learning (HRL) for the training of a machine learning system. The systems and methods employ or implement a two-stage framework or approach that includes (1) unsupervised world graph discovery, and (2) accelerated hierarchical reinforcement learning by integrating the graph.

A method of controlling a vehicle or robot. The method includes the following steps: determining a first control sequence, determining a second control sequence for controlling the vehicle or robot depending on the first control sequence, a current state of the vehicle or robot, and on a model characterizing a dynamic behavior of the vehicle or robot, controlling the vehicle or robot depending on the second control sequence, wherein the determining of the first control sequence is performed depending on a first candidate control sequence and a second candidate control sequence.

A method of operating an autonomous cleaning robot is described. The method includes initiating a training run of the autonomous cleaning robot and receiving, at a mobile device, location data from the autonomous cleaning robot as the autonomous cleaning robot navigates an area. The method also includes presenting, on a display of the mobile device, a training map depicting portions of the area traversed by the autonomous cleaning robot during the training run and presenting, on the display of the mobile device, an interface configured to allow the training map to be stored or deleted. The method also includes initiating additional training runs to produce additional training maps and presenting a master map generated based on a plurality of stored training maps.

Various technologies described herein pertain to generating an occupancy grid movie for utilization in motion planning for the autonomous vehicle. The occupancy grid movie can be generated for a given time and can include time-stepped occupancy grids for future times that are at predefined time intervals from the given time. The time-stepped occupancy grids include cells corresponding to regions in an environment surrounding the autonomous vehicle. Probabilities can be assigned to the cells specifying likelihoods that the regions corresponding to the cells are occupied at the future times. Moreover, cached query objects that respectively specify indices of cells of a grid occupied by a representation of an autonomous vehicle at corresponding orientations are described herein. An occupancy grid for the environment surrounding the autonomous vehicle can be queried to determine whether cells of the occupancy grid are occupied utilizing a cached query object from the cache query objects.