Abstract: The present disclosure provides a data acquisition method, apparatus, device and computer readable storage medium. According to the embodiments of the present disclosure, determination is made as to whether a preset stable driving condition is met based on acquired driving scene data of the driving scene where the vehicle is currently located, and acquired driving behavior data; if the preset stable driving condition is met, the driving scene data and the driving behavior data are acquired at a frequency lower than a preset sampling frequency. As a result, it is possible to reduce data redundancy in similar scenes and similar diving modes, reduce the amount of data, reduce occupation of the storage resources and transmission resources and facilitate subsequent analysis, and it is possible to implement the dynamic acquisition of driving scene data and driving behavior data by scenes and modes.

Abstract: According to one embodiment, in response to determining that an obstacle blocks at least a portion of a current lane in which an ADV is driving, an obstacle boundary of the obstacle is determined based on the size and shape of the obstacle. A lane configuration is determined based on map data of a map corresponding to a road associated with the lanes. A passing lane boundary that can be utilized by the ADV is determined based on the lane configuration of the road and the obstacle boundary of the obstacle. A passable area is calculated within the passing lane boundary based on a size of the ADV. The passable area is utilized by the ADV to pass the obstacle without collision. Thereafter, a trajectory is planned within the passable area boundary to control the ADV to pass the obstacle.

Abstract: In one embodiment, it is determined that a speed of an autonomous driving vehicle (ADV) is below a predetermined speed threshold during a current turn section of a three-point turn, where the three-point turn includes at least three turn sections. In response, detecting an obstacle within a predetermined proximity of the ADV, determining a type of obstacle. An amount of time during which the speed of the ADV remains below the predetermined speed threshold is determined. It is determined whether the amount of time is greater than a time threshold corresponding to the type of the obstacle. If the amount of time is greater than the time threshold, the current turn section is ended and a next turn section is started.

Abstract: Embodiments of the present disclosure disclose a method for controlling an autonomous vehicle to pass through a curve, a device and a medium, and relate to the field of autonomous driving technologies. At least one implementation of the method for controlling an autonomous vehicle to pass through a curve includes: determining a curve boundary within a sensing area in a current driving direction of the autonomous vehicle based on a current position of the autonomous vehicle on the curve; determining a current safe stopping distance of the autonomous vehicle on the curve based on current driving parameters of the autonomous vehicle and the curve boundary; determining a speed threshold of the autonomous vehicle based on the current safe stopping distance, braking parameters of the autonomous vehicle and a curve curvature corresponding to the current position; and controlling a speed of the autonomous vehicle not to exceed the speed threshold.

Abstract: Embodiments of the present disclosure provide a method and an apparatus for controlling a vehicle, a device and a storage medium. The method includes: in response to determining that a target vehicle is located in a historical takeover area, obtaining historical control information associated with passing through a historical path in the historical takeover area, the historical takeover area indicating an area through which a vehicle in a manual control state passed in a previous period; determining a planned path for the target vehicle to pass through the historical takeover area based on the historical control information; and controlling the target vehicle to pass through the historical takeover area in an automatic control state based on the planned path.

Abstract: A method and apparatus for determining a turn-round path of a vehicle, a device and a storage medium are provided. An embodiment of the method includes: determining a starting position and a target position for the vehicle to turn round on a road; determining, based at least partially on road information associated with the road and vehicle information associated with the vehicle, a candidate turn-round path between the starting position and the target position; evaluating the feasibility of the candidate turn-round path; and determining, based on the evaluation on the feasibility, a turn-round path by which the vehicle is to turn round on the road.

Abstract: Embodiments of the present disclosure disclose a vehicle fault processing method, a device and a medium, and relate to the field of autonomous driving technologies. The vehicle fault processing method includes: obtaining operating index data of a target vehicle and driving environment information of the target vehicle, the operating index data being configured to determine an operating condition of the target vehicle; determining whether a fault occurs in the target vehicle based on the operating index data; and when a fault occurs in the target vehicle, controlling the target vehicle to place a warning sign at a preset position based on the driving environment information.

Abstract: The present disclosure provides a positioning method for head and neck assessment comprising positioning a probe to the head and neck structures of a subject according to reference planes defined by light beams.

Abstract: Techniques are disclosed for neural network based interpolation of image textures. A methodology implementing the techniques according to an embodiment includes training a global encoder network to generate global latent vectors based on training texture images, and training a local encoder network to generate local latent tensors based on the training texture images. The method further includes interpolating between the global latent vectors associated with each set of training images, and interpolating between the local latent tensors associated with each set of training images. The method further includes training a decoder network to generate reconstructions of the training texture images and to generate an interpolated texture based on the interpolated global latent vectors and the interpolated local latent tensors. The training of the encoder and decoder networks is based on a minimization of a loss function of the reconstructions and a minimization of a loss function of the interpolated texture.

Abstract: An example method for neural network based interpolation of image textures includes training a global encoder network to generate global latent vectors based on training texture images, and training a local encoder network to generate local latent tensors based on the training texture images. The example method further includes interpolating between the global latent vectors associated with each set of training images, and interpolating between the local latent tensors associated with each set of training images. The example method further includes training a decoder network to generate reconstructions of the training texture images and to generate an interpolated texture based on the interpolated global latent vectors and the interpolated local latent tensors. The training of the encoder and decoder networks is based on a minimization of a loss function of the reconstructions and a minimization of a loss function of the interpolated texture.

Abstract: Digital image defect identification and correction techniques are described. In one example, a digital medium environment is configured to identify and correct a digital image defect through identification of a defect in a digital image using machine learning. The identification includes generating a plurality of defect type scores using a plurality of defect type identification models, as part of machine learning, for a plurality of different defect types and determining the digital image includes the defect based on the generated plurality of defect type scores. A correction is generated for the identified defect and the digital image is output as included the generated correction.

Abstract: The present invention relates to a tellurium-oxide-based glass composition for forming a glass-to-metal seal to alloys or metals having a coefficient of thermal expansion higher than 16 ppm/° C., said composition comprising TeO2, ZnO, TiO2 and optionally K2O and being essentially free of lead oxide, sodium oxide and vanadium oxide. In addition it relates to the use of the glass composition according to the invention to form a glass-to-metal seal between copper or a copper alloy and an alloy or a metal having a coefficient of thermal expansion higher than 16 ppm/° C., in particular aluminum alloys. It furthermore relates to a connector comprising a contact made of copper or of copper alloy, an insert and/or shell made of a metal or alloy having a coefficient of thermal expansion higher than 16 ppm/° C. and, by way of glass-to-metal sealant between the contact and the insert and/or shell, a tellurium-oxide-based glass having the composition according to the invention.

Abstract: The present disclosure is to provide an adjustable vehicle wheel brake space inspection tool. The adjustable vehicle wheel brake space inspection tool is configured to detect the brake space of the vehicle wheel during usage and is configured to adapt to wheels of any size; and meanwhile, the adjustable vehicle wheel brake space inspection tool has the characteristics of high universality and detection accuracy, simple structure, low manufacturing cost, convenient operation, and the like.

Abstract: A method, an apparatus, a system and a storage medium for authorizing an autonomous driving function are proposed. The method comprises: sending, by a control system in an autonomous vehicle, an authorization request to a cloud server, when the autonomous driving function needs to be activated; and activating, by the control system, the autonomous driving function, in response to receiving authorization approval instruction information returned by the cloud server after the cloud server verifies authorization for the autonomous vehicle and approves authorization. The technical solution may improve safety of the vehicle and the user.

Abstract: Provided are a pass route planning method and apparatus, a device and a readable storage medium. The method includes: obtaining current road environment information in front of a vehicle; determining a current passable area according to the current road environment information; setting pass candidate points in the current passable area; and determining a current optimal passable route according to the pass candidate points. Firstly, the passable area is determined, and then the pass candidate points are set in the passable area, and thus the current optimal passable route is calculated according to the pass candidate points in the passable area, which can effectively reduce a calculation amount of unreachable pass routes in the prior art in which candidate points are set in all areas directly in front of an unmanned vehicle, thereby reducing a calculation amount for planning a pass route and improving a planning speed of the pass route.

Abstract: A method, an apparatus and a medium for enabling a self-driving vending machine to cope with a weather environment are proposed. The method comprises: obtaining information about a weather environment of a place where the self-driving vending machine is located; in response to determining that a protective measure needs to be taken according to the information about the weather environment, performing protection for the self-driving vending machine according to a preset protective measure. The technical solution may be applied to avoid the impacts caused by weather factors to the recognition capability of the sensors of the self-driving vending machine and the user's purchasing behaviors, and thereby ensuring normal use of the self-driving vending machine.

Abstract: A replenishment method, apparatus and storage medium of a self-driving vending machine are proposed. The replenishment method include: obtaining information about remaining goods by a self-driving system in the self-driving vending machine; controlling the self-driving vending machine to drive automatically to a replenishment station for replenishment when determining, according to the information about remaining goods, that replenishment needs to be performed. The technical solution of the present disclosure may be applied to implement timely replenishment for the self-driving vending machine and ensure normal work of the self-driving vending machine.

Abstract: A method, an apparatus, a computer device and a storage medium for autonomous driving determination are proposed. The method includes: obtaining information about a driving scenario of an autonomous vehicle when a preset trigger condition is met; comparing the information about the driving scenario with an autonomous driving capability of the autonomous vehicle; determining, according to a comparison result, whether the autonomous vehicle is adapted to employ an autonomous driving mode in the driving scenario. The technical solution of the present disclosure may be applied to improve the safety of the vehicle and the user.

Abstract: A method, an apparatus and a storage medium for evacuating a person around an autonomous vehicle are proposed. The method includes: obtaining data about surrounding of the autonomous vehicle during the driving of the autonomous vehicle; recognizing a person with onlooker intention within a predetermined range around the autonomous vehicle according to the data obtained; evacuating the person with onlooker intention in a predetermined manner. The technical solution may be applied to ensure normal driving of the autonomous vehicle.

Abstract: A method and an apparatus for controlling an autonomous vehicle are provided according to the embodiments of the disclosure. The method includes: sending, in response to determining that a pedestrian is in a first target area, behavior prompt information representing prompting the pedestrian to make a corresponding behavior; determining whether a deceleration condition matching the behavior prompt information is satisfied based on acquired behavior information of the pedestrian; and sending control information for reducing a moving speed of the autonomous vehicle, in response to determining that the deceleration condition is satisfied and determining that a speed of the autonomous vehicle is greater than a preset deceleration threshold. According to the embodiments, deceleration control of the autonomous vehicle is achieved based on the response of the pedestrian to the behavior prompt information.