Embodiments of the present disclosure disclose a method and apparatus for avoidance control of a vehicle, an electronic device, and a storage medium, where the method includes: obtaining behavior information of a moving obstacle, where the moving obstacle is located in a direction of movement of the vehicle and a distance between the moving obstacle and the vehicle satisfies a preset avoidance distance; determining an avoidance strategy for the vehicle based on the behavior information of the moving obstacle; and controlling movement of the vehicle based on the avoidance strategy. The method in the embodiments of the present disclosure considers the behavior information of the moving obstacle in the process of avoidance control of the vehicle, and the vehicle can be controlled to travel when the moving obstacle takes an avoidance action, thereby improving the accuracy of avoidance for the vehicle and the practicality of intelligent driving.

In one embodiment, a number of speed control rate candidates are determined for a speed control command of operating an autonomous vehicle. For each of the speed control rate candidates, a number of individual costs are calculated for the speed control rate candidate by applying a plurality of cost functions, each cost function corresponding to one of a plurality of cost categories. A total cost for the speed control rate candidate is determined based on the individual costs produced by the cost functions. One of the speed control rate candidates having a lowest total cost is selected as a target speed control rate. A speed control command is generated based on the selected speed control rate candidate to control a speed of the autonomous vehicle.

A method for automated control of a vehicle includes automatically navigating a vehicle within a current lane of travel with a driving assistance system, and predetermining a steering maneuver during the automatic navigation with the driving assistance system. The method also includes, prior to performing the predetermined steering maneuver, biasing the vehicle within the current lane of travel with an offset from a center of the current lane of travel using the driving assistance system, the offset in a direction of the predetermined steering maneuver, and performing the predetermined steering maneuver with the driving assistance system.

A method for calculating running resistance of a vehicle includes: calculating, by a controller, an integrated value obtained by integrating a torque of a driving source of the vehicle before the vehicle reaches a reference speed after the vehicle starts; and calculating, by the controller, the running resistance of the vehicle including rolling resistance based on the integrated value of the torque of the driving source.

The present disclosure relates to a method for driving on the basis of characteristics of a driving surface, and a robot for implementing the same, and a method for driving on the basis of characteristics of a driving surface, according to one embodiment of the present disclosure, comprises the steps in which: a sensing module of the robot senses an adjacent driving surface to generate characteristic information of the driving surface, and a control unit of the robot stores position and characteristic information of the driving surface in a map storage of the robot; the controller of the robot sets a function to be applied to the driving surface in response to the characteristic information of the driving surface, or generates a movement path selectively including the driving surface corresponding to start and end points of the robot; and the controller controls a moving unit and a functional unit of the robot according to the set function or the movement path.

A method using unsupervised velocity prediction and correction for urban driving from sequences of noisy position estimates includes: performing a vehicle velocity prediction for one or more other vehicles in a vicinity of a host automobile vehicle; calculating a first heuristic based on a uniformity test; calculating a second heuristic based on a vehicle speed of the one or more other vehicles; combining the first heuristic and the second heuristic using a weighted sum; determining an uncertainty mask applying the combined first heuristic and the second heuristic and a heuristic threshold; and applying the uncertainty mask to identify a velocity correction for use by the host automobile vehicle.

A work vehicle includes: a body; a traveling apparatus capable of a turning travel; a speed detector capable of detecting a vehicle speed; a steering tool manually operable to steer the traveling apparatus; a notification apparatus; and a controller. The controller is configured or programmed to control the traveling apparatus in response to a manual operation, with use of a travel control module; determine, based on a relationship between the vehicle speed and a steering angle of the steering tool, whether at least one of the vehicle speed and the steering angle needs to be reduced, with use of a determination module; and control the notification apparatus to give a notification of the determination by the determination module, with use of a notification module.

Performance anomalies in autonomous vehicle can be difficult to identify, and the impact of such anomalies on systems within the autonomous vehicle may be difficult to understand. In examples, systems of the autonomous vehicle are modeled as nodes in a probabilistic graphical network. Probabilities of data generated at each of the nodes is determined. The probabilities are used to determine capabilities associated with higher level functions of the autonomous vehicle.

A planning system for an autonomous work vehicle system includes a controller having a memory and a processor. The controller is configured to provide an indication of a work area for operation of the autonomous work vehicle system, identify a plurality of zones of the work area, and identify one or more rules for each respective zone of the plurality of zones. The one or more rules define operation of the autonomous work vehicle system in the respective zone. The controller is configured to generate a plan for operation of the autonomous work vehicle system based on the plurality of zones and the one or more rules for each respective zone of the plurality of zones.

Systems, methods, and other embodiments described herein relate to controlling a vehicle system when the vehicle system is under control of a user. In one embodiment, a method includes predicting a start of a user sneezing episode. The method includes identifying a plurality of phases in the user sneezing episode, and controlling the vehicle system based on which one of the plurality of phases is active.