Beijing Sankuai Online Technology Co., Ltd

Beijing 7invensun Technology Co., Ltd.

Dongchun Ren

Wei Liu

Deheng Qian

Fengmei Nie

Jian Wang

Meng Yang

Huaxia Xia

Xiaohu Gong

Mingyu Fan

Sheng Fu

Yanliang Zhu

Shuguang Ding

Xin Li

Yinian Mao

Zhichao Wang

Qin Han

Xiaoyang Guo

Shuwang Xu

Run Tian

Xuetao Xing

Hongshuai Chen

Zhijiang Lou

Yangyu Zhang

Shitao Yan

Guosheng Chen

Xu Du

Vehicle control is provided, including: obtaining vehicle information of a target vehicle and environmental information of a reference environment in which the target vehicle is located; obtaining a target matrix based on the vehicle information and the environmental information; splitting the target matrix to obtain a plurality of sub-matrices; and obtaining target driving control information of the target vehicle based on matrix elements in the sub-matrices and driving control information of a surrounding vehicle of the target vehicle.

Vehicle control

An obstacle trajectory prediction method and apparatus are provided. In some embodiments, a global interaction feature under joint action of a vehicle and obstacles is determined according to historical status information and current status information of the vehicle, historical status information and current status information of the obstacles, and a future motion trajectory planned by the vehicle; an individual interaction feature of a to-be-predicted obstacle is determined according to the global interaction feature and current status information of the to-be-predicted obstacle; and a future motion trajectory of the to-be-predicted obstacle is predicted through the individual interaction feature and information about an environment around the vehicle.

Obstacle trajectory prediction method and apparatus

A trajectory prediction method and apparatus, a storage medium, and an electronic device are provided. In embodiments of this disclosure, according to a historical trajectory of a designated target and a historical trajectory of each obstacle, a historical interaction feature between the designated target and each obstacle is determined, and a motion trajectory of the designated target is predicted to obtain an initial predicted trajectory. A future interaction feature between each obstacle and the designated target is then determined according to the initial predicted trajectory and a planned trajectory of each obstacle. According to the future interaction feature, a final predicted trajectory of the designated target is obtained.

Trajectory prediction method and apparatus, storage medium, and electronic device

An unmanned device acquires sensing data of surrounding obstacles; determines, for each obstacle, at least one predicted track of the obstacle in a future period of time based on the sensing data; determines, for each moment in the future period of time and according to the predicted track corresponding to the obstacle, a collision probability that a collision with the obstacle occurs at each position in a target region at the moment; and determines a global collision probability that the collision with the obstacle occurs in the entire target region at the moment. According to the global collision probability corresponding to each obstacle at each moment, the unmanned device controls the unmanned device in the future period of time.

Unmanned device control based on future collision risk

A method for trajectory planning, an apparatus, a storage medium, and an electronic device are provided. A constraint set of a space including a target device is determined according to a velocity of an unmanned device and velocities of designated obstacles, so that during optimization of a preliminary reference trajectory, a solution can be obtained with the space in the constraint set as a solution space under the constraint of the constraint set, so as to ensure that the solution space is a convex space, and relatively satisfactory reference trajectory points can be solved.

Method and apparatus for trajectory planning, storage medium, and electronic device

Various embodiments a method and apparatus for unmanned driving behavior decision-making and model training, and an electronic device. The method includes: acquiring sample data, wherein the sample data includes a sample image; extracting a sample feature vector corresponding to the sample data, wherein a feature vector of the sample image is extracted by manifold dimension reduction; and based on the sample feature vector, training by semi-supervised learning to obtain a target decision-making model, wherein the target decision-making model is used for decision-making classification.

Unmanned driving behavior decision-making and model training

The disclosure discloses a method and device for determining parameter for a gaze tracking device. The method includes that: an image to be detected and a characteristic matrix of the image to be detected are acquired; the image to be detected is processed according to a preset model to obtain a first vector and second vector of the image to be detected; and a parameter of the image to be detected is determined according to the first vector and the second vector, the parameter of the image to be detected including at least one of: a position of a glint in the image to be detected and a gaze direction of eyes in the image to be detected.

Method and device for determining parameter for gaze tracking device

The present disclosure discloses a method and apparatus for filtering glints. The method includes that: a first image and a reference image matched with the first image are acquired, and first-type glints are displayed in the first image; second-type glints in the first image are determined according to the reference image, and the second-type glints are glints obtained by estimating glints in the first image on the basis of the reference image; a matching result of the first-type glints and the second-type glints is determined according to first positions of the first-type glints and second positions of the second-type glints; and the first-type glints are filtered according to the matching result.

Method and apparatus for filtering glints

The disclosure discloses a calibration method and device, a storage medium and a processor. The method includes that: a calibration point is controlled to move on a predetermined motion track; a gaze track of a target object is acquired in a movement process of the calibration point; and a specified parameter is calibrated according to the predetermined motion track and the gaze track, the specified parameter is configured to predict a gaze point of the target object.

Calibration method and device, storage medium and processor

Disclosed is a method and apparatus for determining a position of a pupil. The method includes: acquiring an image to be detected including a pupil; acquiring a binary image of a preset area based on a preset model of semi-supervised learning, where the preset area is an area where the pupil is located in the image to be detected; acquiring a centroid of the binary image; and determining a central position of the pupil according to the centroid of the binary image.

Method and apparatus for determining position of pupil

The present disclosure discloses a method and apparatus for determining a pupil position. The method includes that: at least one first parameter of a pupil in an image, an initial edge region of the pupil and at least one second parameter of at least one light spot in the image are acquired; the initial edge region is corrected according to the at least one first parameter and the at least one second parameter to obtain a first edge region; a pupil edge region is determined according to an edge section in the first edge region, and the edge section is a region formed by a point set meeting a first preset condition in the first edge region; and fitting processing is performed on the pupil edge region to obtain a position of the pupil.

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Beijing, China

Dongchun Ren