Abstract: A vehicle decision-making planning method, apparatus, device, and medium comprises generating a base coordinate system, generating a guide line in the base coordinate system to determine an approximate travel trajectory of a vehicle in the future, performing obstacle decision-making under the constraint of the guide line, and generating a drivable area based on the obstacle decision-making.

Abstract: The present disclosure provides a distributed computing network system and a method. The distributed computing network may include: a plurality of nodes, where the plurality of nodes is connected through an n-layer network connection. When the distributed computing network receives a data processing task, a node in an ith layer is configured to: receive a data processing task that is corresponding to the ith layer and that is transmitted from a node in a lower layer; complete a data processing task Ti; transmit, to the node in the lower layer, a data processing result; and transmit, to a node in an upper layer, a remaining data processing task, a process of the data processing task meets a preset condition. The overall feedback time of the data processing task is reduced by distributing calculation tasks among multiple layers of nodes. The technology may be applied in both 4G and 5G network.

Abstract: A map matching method and apparatus, an electronic device and a storage medium are provided. The method includes: acquiring initial positioning information of a vehicle and vehicle sensor data; determining a plurality of pieces of candidate positioning information on the basis of the initial positioning information; determining a plurality of observation semantic features on the basis of the vehicle sensor data; acquiring local map information on the basis of the initial positioning information of the vehicle, the local map information including a plurality of map semantic features; for each piece of candidate positioning information: converting the plurality of map semantic features into a coordinate system of the vehicle sensor; and matching the plurality of observation semantic features with the plurality of candidate map semantic features; and determining optimal candidate positioning information of the vehicle and a matching pair corresponding to the optimal candidate positioning information.

Abstract: A speed planning method and apparatus and a calculating apparatus for automatic driving of a vehicle. The method comprises: using a training sample set to perform machine learning to obtain a machine learning model; partitioning an input space, and obtaining a decision result corresponding to a determined partition based on the obtained machine learning model to form a partition decision table of each partition corresponding to the corresponding decision result; and obtaining each dimensional feature vector of a vehicle while driving in real time as an input feature, determining an input partition to which the input feature belongs, and querying the partition decision table based on the determined partition to obtain the corresponding decision result.

Abstract: A system for controlling an intelligent network vehicle is provided, and the system comprises a sensor group configured to obtain sensor information; a sensing and positioning module configured to obtain sensing information and positioning information based on the sensor information; a planning and control module configured to determine vehicle planning control information based on the sensing information and the positioning information; a safety control module configured to determine safety control information based on the sensing information and the positioning information; a function assessment module configured to determine a vehicle state assessment result; a risk assessment module configured to determine a risk assessment result; a logical arbitration module configured to determine vehicle execution information by arbitrating the vehicle planning control information and the safety control information; and an execution module configured to control the vehicle driving based on the vehicle execution inform

Abstract: Embodiments of the present disclosure relate to methods for updating an autonomous driving system, autonomous driving systems, and on-board apparatuses. In the embodiments of the present disclosure, the autonomous driving system, in a manual driving mode, senses the surrounding environment of a vehicle, performs vehicle positioning, and generates an autonomous control instruction for an underlying vehicle execution system. However, the autonomous driving system does not issue an instruction to control the driving of the vehicle. Instead, it compares the instruction with a control instruction from the driver of the vehicle for the underlying vehicle execution system in the manual driving mode to update a planning and control algorithm of the autonomous driving system. As such, the updated autonomous driving system better caters to the driving habits of the driver and improves the driving experience for the driver without compromising the reliability of planning and decision-making of autonomous driving.

Abstract: The present disclosure provides for a steering control method and a steering control system for self-driving of a vehicle. The method comprises the steps of: obtaining information about an expected steering angle of a vehicle based on an automatic planning control operation; detecting whether an effective torque is applied to a steering wheel by a driver; and when it is detected that the driver has applied the effective torque to the steering wheel, computing a difference between a turning angle of the steering wheel controlled by the driver and the expected steering angle of a vehicle, and determining a self-driving intent prompt torque according to the difference between the two, wherein the self-driving intent prompt torque is to be applied to a steering system.

Abstract: The present disclosure provides a distributed computing network system and a method. The distributed computing network may include: a plurality of nodes, where the plurality of nodes is connected through an n-layer network connection. When the distributed computing network receives a data processing task, a node in an ith layer is configured to: receive a data processing task that is corresponding to the ith layer and that is transmitted from a node in a lower layer; complete a data processing task Ti; transmit, to the node in the lower layer, a data processing result; and transmit, to a node in an upper layer, a remaining data processing task, a process of the data processing task meets a preset condition. The overall feedback time of the data processing task is reduced by distributing calculation tasks among multiple layers of nodes. The technology may be applied in both 4G and 5G network.

Abstract: The present disclosure provides a map updating method, system and a readable storage medium for autonomous driving. The method may include: a vehicle sends a local map request to a server, the local map request includes current location data of the vehicle, the vehicle includes an autonomous driving vehicle; the vehicle receives a first local map of the current location from the server, the first local map covers a first distance on the route of the vehicle; a sensor mounted on the vehicle collects first surrounding environment data of the vehicle during travelling along the first distance; and based on the first local map and the first surrounding environment data, the vehicle generates map update data and sends the data to the server.

Abstract: The present application relates to a method, a device, and a non-transitory computer-readable medium for simultaneously positioning and mapping. The method for simultaneous positioning and mapping includes: obtaining a large field of view image through a large field of view camera; obtaining an undistortion image corresponding to the large field of view image based on a multi-virtual pinhole camera model; and determining the pose of the large field of view camera based on the undistortion image and creating a map. The multiple virtual pinhole camera model includes at least two virtual pinhole cameras with different orientations, and a camera center of the at least two virtual pinhole cameras with different orientations coincides with a camera center of the large field of view camera.

Abstract: The present application relates to a method, a device, and a non-transitory computer-readable medium for simultaneously positioning and mapping. The method for simultaneous positioning and mapping includes: obtaining a large field of view image through a large field of view camera; obtaining an undistortion image corresponding to the large field of view image based on a multi-virtual pinhole camera model; and determining the pose of the large field of view camera based on the undistortion image and creating a map. The multiple virtual pinhole camera model includes at least two virtual pinhole cameras with different orientations, and a camera center of the at least two virtual pinhole cameras with different orientations coincides with a camera center of the large field of view camera.

Abstract: A speed planning method and apparatus and a calculating apparatus for automatic driving of a vehicle. The method comprises: using a training sample set to perform machine learning to obtain a machine learning model (S110); partitioning an input space, and obtaining a decision result corresponding to a determined partition based on the obtained machine learning model to form a partition decision table of each partition corresponding to the corresponding decision result (S120); and obtaining each dimensional feature vector of a vehicle while driving in real time as an input feature, determining an input partition to which the input feature belongs, and querying the partition decision table based on the determined partition to obtain the corresponding decision result (S130).

Abstract: A steering control method and a steering control system for self-driving of a vehicle are disclosed. The method comprises obtaining information about an expected steering angle of a vehicle based on an automatic planning control operation; detecting whether an effective torque is applied to a steering wheel by a driver; and when it is detected that the driver has applied the effective torque to the steering wheel, computing a difference between a turning angle of the steering wheel controlled by the driver and the expected steering angle of a vehicle, and determining a self-driving intent prompt torque according to the difference between the two, wherein the self-driving intent prompt torque is to be applied to a steering system.