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: The present disclosure discloses a method for simulating an obstacle in an unmanned simulation scene, an electronic device, and a storage medium, and relates to a field of unmanned driving technologies. The method includes: for obstacle information in a three-dimensional scene map, determining Gaussian distribution information of obstacle position detected by using a perception algorithm to be tested based on actual perception performance of the perception algorithm; adjusting each position of the simulated obstacle in the initial motion trajectory sequence, such that a position deviation between each adjusted position point in the target motion trajectory sequence and the corresponding position point in the initial motion trajectory sequence follows a Gaussian distribution; and adding the target motion trajectory sequence of the simulated obstacle to the three-dimensional scene map.

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: In response to a request to pull over an ADV at a destination point at a side of a lane, a path including a first segment, a second segment and a transition point is planned. The transition point is determined based on at least one of a distance to the destination point or a predetermined distance to a boundary of the side of the lane. The first segment from a start point to the transition point is generated by using a quadratic programming (QP) operation. The second segment from the transition point to the destination is generated based on a shape of the boundary. The ADV is controlled to pull over to the destination point according to the planned path.

Abstract: In response to a request to park an ADV in a parking lot, a set of parking-trajectories associated with a set of predetermined locations near one or more parking spots in the parking lot may be obtained, where the set of parking-trajectories was previously generated based on prior collected planning and control data of the parking lot. Each parking-trajectory of the set of parking-trajectories may correspond to one parking spot of the one or more parking spots in the parking lot. A parking-trajectory may be selected from the set of parking-trajectories based on a current location of the ADV. The ADV may be controlled to park in a corresponding parking spot according to the selected parking-trajectory.

Abstract: In one embodiment, an exemplary method of autonomously charging an autonomous driving vehicle includes receiving, from a sensor in an autonomous driving vehicle (ADV), indication that a batter level of the ADV falls below a threshold; and selecting a charging pile from a plurality of charging piles on a high definition map based on information received from a cloud server. The method further includes generating a first trajectory based on a current location of the ADV and a location of the selected charging pile, the first trajectory connecting a first point representing the current location of the ADV to a second point at the selected charging pile, and including a first segment and a second segment. The method further includes driving forward along the first segment of the first trajectory, and driving backward along the second segment of the first trajectory when the ADV drives towards the selected charging pile along the first trajectory.

Abstract: The present disclosure provides a method, apparatus and storage medium for autonomous operation of an unmanned logistics vehicle. The method comprises: an unmanned logistics vehicle obtains a user's cargo transport task; the unmanned logistics vehicle plans a travel route according to the cargo transport task, and automatically travels to a destination according to the travel route; after reaching the destination, the unmanned logistics vehicle controls a cargo compartment door to open. The solution of the present disclosure may be applied to save man power costs and improve the cargo transport efficiency.

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: A computer-implement method for operating an ADV is disclosed. A starting point and an ending point of a route along which an ADV is to be driven is determined. Whether each of the starting point and the ending point is within a first driving area having a lane boundary or a second driving area as an open space is determined. The route is divided into a first route segment and a second route segment. Dependent upon whether the starting point or the ending point is within the first driving area or the second driving area, the method comprises operating in one of an on-lane mode or an open-space mode to plan a first trajectory for the first route segment and operating in one of the on-lane mode or the open-space mode to plan a second trajectory for the second route segment.

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.