Beijing Baidu Netcom Science Technology

Provided is a driving information prediction method, apparatus, and autonomous driving vehicle, which relate to the field of autonomous driving, especially to the field of artificial intelligence, and particularly to the technical fields of autonomous driving and intelligent transportation. The method includes: determining (S110) a first leader-follower relationship between a target vehicle and a first obstacle based on a motion parameter of the target vehicle at a current moment, path information of the target vehicle within a first time period, a motion parameter of the first obstacle at the current moment, and predicted path information of the first obstacle within the first time period; obtaining (S120) first predicted driving information based on the first leader-follower relationship; and determining (S130) first optimal driving information based on an evaluation result corresponding to the first predicted driving information.

The present disclosure provides a joint lateral and longitudinal control method and apparatus for an autonomous vehicle, an electronic device, and a medium, and relates to the technical field of autonomous driving, in particular to the technical field of trajectory tracking and model predictive control. A solution for implementation includes: obtaining a lateral-longitudinal coupled state prediction model of an autonomous vehicle and a desired trajectory; obtaining a current state quantity and a current control quantity of the autonomous vehicle; solving for a target control quantity for trajectory tracking using a model predictive control algorithm based on the current state quantity, the current control quantity, the lateral-longitudinal coupled state prediction model, and the desired trajectory, where the model predictive control includes quadratic programming, and a cost function of the quadratic programming includes at least a lateral-longitudinal decoupled target error; and controlling the autonomous vehicle to perform the trajectory tracking based on the target control quantity.

The present disclosure provides a lane-level positioning method and apparatus, a device, a vehicle, and a medium, and relates to the field of computer technologies and specifically to the technical fields such as artificial intelligence, computer vision, intelligent vehicles, and autonomous vehicles. The method includes: obtaining map road information and visual perception road information of surroundings of a vehicle based on positioning information of the vehicle; determining a plurality of candidate lanes based on the positioning information; determining a topological recursion probability of the vehicle being in each of the candidate lanes by utilizing the map road information; determining a perception observation probability of the vehicle being in each of the candidate lanes by utilizing the visual perception road information; determining a positioning probability of the vehicle being in each of the candidate lanes by utilizing the positioning information; and determining a target lane the vehicle is in from the plurality of candidate lanes based on the topological recursion probability, the perception observation probability, and the positioning probability of the vehicle being in each of the candidate lanes.

The present disclosure provides an animal language translation method, an emotion recognition method, and a model training method and apparatus, which relates to the field of artificial intelligence technology, particularly to the field of machine learning technology, deep learning technology, natural language processing technology and the like. A specific implementation solution involves acquiring animal-related multimodal data; preprocessing the multimodal data to obtain fused multimodal data; recognizing current animal emotions based on the fused multimodal data to obtain animal emotion recognition results; performing semantic mapping and language translation on the emotion recognition results to translate animal languages into human languages to obtain language translation results.

Provided is a method and apparatus for automatically annotating an obstacle and related devices, relating to the field of artificial intelligence technology, and in particular, to technologies fields of autonomous driving, neural network, deep learning and the like. The method includes: optimizing (S101) a target parameter in a projection relationship based on a re-projection error, the projection relationship is used to project a target obstacle from a reference frame onto a frame to be optimized, and satisfies a constraint in which positions of the target obstacle in different frames are consistent in an obstacle coordinate system established according to the target obstacle; and determining (S102) a target pose of the target obstacle based on the optimized target parameter.

Provided is a method and an apparatus for controlling quality of service of a virtual machine, relating to the fields of cloud computing, virtualization, and big data. The method includes: storing (S101) an operation request in a target queue corresponding to a layer of a virtual machine cluster when an available token quantity in a token bucket of the layer is unable to meet a target token quantity required for the operation request; wherein tokens of the layer are periodically generated according to an upper limit of quality of service of the layer; deducting (S102) the target token quantity from an updated available token quantity when the available token quantity in the token bucket is updated to meet the target token quantity required for the operation request; and sending (S103) the operation request from the target queue to the layer for processing when deducting the target token quantity successfully.

The invention provides an image generation method and apparatus, an intelligent agent, an intelligent agent system and a storage medium, relates to the field of artificial intelligence technology, in particular to the field of computer vision, deep learning and large language models, and can be applied to an artificial intelligence generated content scene. The specific implementation solution includes: obtaining (101, 201, 301, 501, 601, 701) image generation requirement information; determining (102, 202, 502, 602, 702) a target image generation manner according to the image generation requirement information; querying (103, 305, 503) a first reference image based on the image generation requirement information; and based on the image generation requirement information and the first reference image, generating (104, 204, 306, 605) a target image using the target image generation manner.

The present disclosure provides a sandbox scheduling method and related apparatus based on a code generation large model, relating to the field of computer technologies, and specifically to the field of artificial intelligence technologies such as large language models, sandboxes, human-computer interaction, and low-code development. The method includes: determining personalized requirement information according to a code development request initiated by a target user; in response to the presence of a reusable sandbox matching the personalized requirement information, allocating the reusable sandbox matching the personalized requirement information to the target user, where the reusable sandbox is a sandbox retaining data associated with the target user; in response to the absence of a reusable sandbox matching the personalized requirement information, determining a to-be-activated sandbox corresponding to the personalized requirement information from a pre-constructed sandbox hot pool, and allocating the to-be-activated sandbox to the target user, where different sandboxes respectively provide the same code generation large model in different operating environments. The method improves the resource utilization efficiency of sandbox scheduling.

The invention discloses a method for generating an adaptive program based on artificial intelligence (AI), an agent, and a storage medium, relating to the AI-related fields. The method includes: determining diversity information of a target population, wherein the diversity information indicates program diversity of the target population; determining a temperature parameter according to the diversity information, wherein the temperature parameter is used to adjust a selection pressure; selecting, from the target population according to the temperature parameter, a first parent program and a second parent program corresponding to the first parent program; and obtaining a target program by performing an evolution iteration according to the first parent program and the second parent program. The invention may adaptively select a parent program based on the temperature parameter, enhance a balance between exploration and exploitation in an evolution process, and consequently efficiently generate diversified and high-performance programs.

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