Abstract: A method, a device and a system for cooperatively constructing a point cloud map, relating to the field of point cloud map construction. The method includes acquiring point cloud data of corresponding road sections respectively acquired by a plurality of vehicles when moving on different road sections, an overlap region being provided between any first road section and second road section which are adjacent; determining, by using the point cloud data of the overlap region, a transformation matrix for transforming the point cloud data of the second road section into the point cloud data of the first road section; using the transformation matrix to transform the point cloud data of the second road section; and splicing the point cloud data of the first road section and the transformed point cloud data of the second road section to construct a point cloud map of the preset route.

Abstract: The present disclosure provides a method and an apparatus for identifying a data point, and a computer readable storage medium, and relates to the field of computer technologies. The method for identifying a data point includes acquiring real-time point cloud data of a current position of a vehicle; converting a coordinate of a data point in the real-time point cloud data into a first coordinate under a coordinate system of a preset reference point cloud map, wherein the reference point cloud map includes static data points; matching the first coordinate of the data point in the real-time point cloud data with the reference point cloud map; and determining the data point in the real-time point cloud data as a static data point under a condition that the first coordinate of the data point in the real-time point cloud data matches the reference point cloud map.

Abstract: The present invention relates to the technical field of model establishment, and provides a dimethylarsinous acid-induced malignantly transformed cell line of human keratinocytes and application thereof. In the present invention, human keratinocytes are persistently exposed to and incubated with a low dose of dimethylarsinous acid, to construct an inorganic arsenic metabolite dimethylarsinous acid (DMAIII)-induced malignantly transformed cell model of human keratinocytes. The malignantly transformed cell model of the present invention promotes the identification of carcinogenicity of arsenic methylated metabolites, and indicates that long-term exposure to low-dose arsenic metabolite dimethylarsinous acid (DMAIII) causes malignant transformation of skin cells, thus providing a new cell model basis and new research idea for the study of carcinogenic mechanism of arsenic.

Abstract: A system and a method for determining an intermediate parking goal of an autonomous vehicle. The autonomous vehicle includes a controller, the controller has a processor and a storage device storing computer executable code. The computer executable code, when executed at the processor, is configured to: prepare a driving plan toward a final parking goal and a parking plan to park at the final parking goal; drive the autonomous vehicle toward the final parking goal according to the driving plan; switch from the driving plan to the parking plan when the autonomous vehicle is within a predetermined distance to the final parking goal; and when the final parking goal is inaccessible: determine the intermediate parking goal, drive and park the autonomous vehicle at the intermediate parking goal.

Abstract: This disclosure relates to a position determination method, device, system, and computer-readable storage medium, and relates to the field of computer technologies. The method of the present disclosure includes acquiring laser point cloud data measured at a current position point of a vehicle as reference point cloud data, and acquiring point cloud data corresponding to a preset starting point of the vehicle in a point cloud map as target point cloud data; matching the target point cloud data with the reference point cloud data to determine a transformation matrix between the target point cloud data and the reference point cloud data; and determining coordinate information of the current position point according to coordinate information of the preset starting point and the transformation matrix.

Abstract: A method and apparatus for determining a location of a vehicle, relating to the technical field of autonomous driving. The method includes determining the current location point of a vehicle according to a positioning state of a GPS of the vehicle; obtaining laser point cloud data measured by the vehicle at the current location point and using same as first point cloud data; obtaining point cloud data, in a point cloud map, corresponding to a preset starting point of the vehicle, and using same as second point cloud data; matching the first point cloud data with the second point cloud data to determine a transformation matrix between the first point cloud data and the second point cloud data; and determining coordinate information of the current location point according to coordinate information of the preset starting point and the transformation matrix.

Abstract: The present invention relates to the technical field of model establishment, and provides a dimethylmonothioarsinic acid-induced malignantly transformed cell line of human keratinocytes and use thereof. In the present invention, human keratinocytes are persistently exposed to and incubated with dimethylmonothioarsinic acid, to construct an inorganic arsenic metabolite dimethylmonothioarsinic acid (DMMTAv)-induced malignantly transformed cell model of human keratinocytes. The malignantly transformed cell model of the present invention promotes the identification of carcinogenicity of arsenic methylated metabolites, and indicates that long-term exposure to low-dose arsenic metabolite dimethylmonothioarsinic acid (DMMTAv) causes malignant transformation of skin cells, thus providing a new cell model basis and new research idea for the study of carcinogenic mechanism of arsenic.

Abstract: Provided are a model generation method and apparatus, an object detection method and apparatus, a device and a storage medium. The method includes acquiring multiple scaling coefficients of a batch normalization layer in an initially-trained intermediate detection model, where the intermediate detection model is obtained by training an original detection model based on multiple training samples, and each training sample includes a sample image and a sample annotation result of a known object in the sample image; screening a to-be-pruned coefficient from the multiple scaling coefficients according to values of the multiple scaling coefficients; and screening a to-be-pruned channel corresponding to the to-be-pruned coefficient from multiple channels of the intermediate detection model and performing channel pruning on the to-be-pruned channel to generate an object detection model.

Abstract: In one embodiment, a lateral drifting error is determined based on at least a current location of an ADV. The lateral drifting error is segmented into a first drifting error and a second drifting error using a predetermined segmentation algorithm. A planning module plans a path or trajectory for a current driving cycle (e.g., planning cycle) to drive the ADV from the current location for a predetermined period of time. The planning module performs a first drifting error correction on the trajectory by modifying at least a starting point of the trajectory based on the first drifting error to generate a modified trajectory. A control module controls the ADV to drive according to the modified trajectory, including performing a second drifting error correction based on the second drifting error.

Abstract: In response to a search query received from a client, a content item is identified from a content database based on one or more keywords associated with the search query. In addition, a search is performed in an image store to identify a list of image candidates based on one or more keywords associated with the search query. An image is selected from the list of image candidates using an image selection algorithm. Metadata associated with the content provider is inscribed onto the selected image to generate a customized image. The customized image is integrated with the content item to generate a composite content item such as a poster. As a result, the content represented by the composite content item is closely tied to the content provider. The composite content item is then transmitted to the client.

Abstract: A system and a method for controlling an autonomous driving vehicle. The system includes vehicle sensors and a controller. The controller has a processor and a storage device storing computer executable code. The computer executable code, when executed at the processor, is configured to: receive vehicle parameters from the vehicle sensors; obtain a vehicle dynamic model by adding a dynamics error bound to a state space model, wherein the dynamics error bound is estimated using linear least square; minimize a linear quadratic regulator cost function based on the vehicle dynamic model; and control the vehicle using control input obtained from the minimized cost function.

Abstract: Embodiments of the present disclosure disclose a method for determining a vehicle control parameter, an apparatus for the same, a vehicle on-board controller, and an autonomous vehicle. An embodiment of the method comprises: obtaining a lateral offset sequence of a vehicle and a control input sequence of a controller for controlling a lateral output of the vehicle, wherein a lateral offset in the lateral offset sequence is for characterizing an offset between an actual lateral output of the vehicle and a desired lateral output; executing a step of determining a vehicle control parameter; wherein the executing the step of determining the vehicle control parameter includes: with the lateral offset sequence as an input and the control input sequence as the desired output, training a pre-established vehicle dynamic model to obtain a trained vehicle dynamic model; and determining the vehicle control parameter from the trained vehicle dynamic model.

Abstract: In one embodiment, during a planning stage of autonomous driving of an autonomous driving vehicle (ADV), it is determined that the ADV needs to change lanes from a source lane to a target lane. A first trajectory is generated from a current location of the ADV in the source lane to the target lane such as a center line of the target lane. A lane shifting correction is then calculated based on the lane configuration of at least the source lane and/or target lane, as well as the current state of the ADV. Based on the lane shifting correction, at least the starting point of the first trajectory is modified, which in turn generates a second trajectory. In one embodiment, the starting point of the first trajectory is shifted laterally with respect to a heading direction of the source lane based on the lane shifting correction.

Abstract: In one embodiment, instead of using map data, a relative coordinate system is utilized to assist perception of the driving environment surrounding an ADV for some driving situations. One of such driving situations is driving on a highway. Typically, a highway has fewer intersections and exits. The relative coordinate system is utilized based on the relative lane configuration and relative obstacle information to control the ADV to simply follow the lane and avoid potential collision with any obstacles discovered within the road, without having to use map data. Once the relative lane configuration and obstacle information have been determined, regular path and speed planning and optimization can be performed to generate a trajectory to drive the ADV. Such a perception system is referred to as a relative perception system based on a relative coordinate system.

Abstract: According to an embodiment, a system generates a number of sample trajectories from a trajectory sample space for a driving scenario. The system determines a reward based on a reward model for each of the sample trajectories, where the reward model is generated using a rank based conditional inverse reinforcement learning algorithm. The system ranks the sample trajectories based on the determined rewards. The system determines a highest ranked trajectory based on the ranking. The system selects the highest ranked trajectory to control the ADV autonomously according to the highest ranked trajectory.

Abstract: According to some embodiments, a system receives a first control command and a speed measurement of the ADV. The system determines an expected acceleration of the ADV based on the speed measurement and the first control command. The system receives an acceleration measurement of the ADV. The system determines a feedback error based on the acceleration measurement and the expected acceleration. The system updates a portion of the calibration table based on the determined feedback error. The system generates a second control command to control the ADV based on the calibration table having the updated portion to control the ADV autonomously according to the second control command.

Abstract: In one embodiment, a lateral drifting error is determined based on at least a current location of an ADV. The lateral drifting error is segmented into a first drifting error and a second drifting error using a predetermined segmentation algorithm. A planning module plans a path or trajectory for a current driving cycle (e.g., planning cycle) to drive the ADV from the current location for a predetermined period of time. The planning module performs a first drifting error correction on the trajectory by modifying at least a starting point of the trajectory based on the first drifting error to generate a modified trajectory. A control module controls the ADV to drive according to the modified trajectory, including performing a second drifting error correction based on the second drifting error.

Abstract: According to some embodiments, a system receives a first control command and a speed measurement of the ADV. The system determines an expected acceleration of the ADV based on the speed measurement and the first control command. The system receives an acceleration measurement of the ADV. The system determines a feedback error based on the acceleration measurement and the expected acceleration. The system updates a portion of the calibration table based on the determined feedback error. The system generates a second control command to control the ADV based on the calibration table having the updated portion to control the ADV autonomously according to the second control command.

Abstract: A first localization system performs a first localization using a first set of sensors to track locations of the ADV along the path from a starting point to a destination point. A first localization curve is generated as a result representing the locations of the ADV along the path tracked by the first localization system. Currently, a second localization system performs a second localization using a second set of sensors to track the locations of the ADV along the path. A second localization curve is generated as a result representing the locations of the ADV along the path tracked by the second localization system. A system delay of the second localization system is determined by comparing the second localization curve against the first localization curve as a localization reference. The system delay of the second localization system can then be utilized to compensate path planning of the ADV subsequently.

Abstract: In one embodiment, instead of using map data, a relative coordinate system is utilized to assist perception of the driving environment surrounding an ADV for some driving situations. One of such driving situations is driving on a highway. Typically, a highway has fewer intersections and exits. The relative coordinate system is utilized based on the relative lane configuration and relative obstacle information to control the ADV to simply follow the lane and avoid potential collision with any obstacles discovered within the road, without having to use map data. Once the relative lane configuration and obstacle information have been determined, regular path and speed planning and optimization can be performed to generate a trajectory to drive the ADV. Such a perception system is referred to as a relative perception system based on a relative coordinate system.