4937results about "Vehicle infrastructure communication" patented technology

The invention discloses a traffic transportation operation monitoring early warning and decision analysis method based on vehicle infrastructure cooperation, and relates to the technical field of intelligent traffic. According to the method, real-time collection of dynamic traffic elements is realized through a multi-dimensional sensing network of a vehicle end, a road side and an environment and V2X communication, time-space reference unification of multi-source data is ensured, a road-vehicle-environment-event semantic network is constructed, multi-dimensional recognition of abnormal events such as accidents, congestion and severe weather is realized in combination with hierarchical feature extraction, and the method has the advantages of being high in practicability and high in practicability. The method is advantaged in that identification accuracy is improved, abnormal event propagation paths can be predicted, global road network situation prediction capability is realized, differential early warning is generated based on a comprehensive risk index, multi-level responses such as traffic signal adjustment and path planning are triggered, response time is greatly shortened, emergency response efficiency is optimized, a decision execution effect real-time feedback mechanism is established, and the method is suitable for popularization and application. The system performance is continuously optimized along with data accumulation, and the defect that a big data platform lacks an intelligent decision closed loop is avoided.

The invention provides a data transmission method and transmission device applied to a vehicle-road cloud collaborative road network system, and the method comprises the steps: collecting vehicle operation state data through a vehicle-mounted terminal, collecting real-time road network topology data through road side equipment, and transmitting the data to a cloud server for data preprocessing; performing feature extraction on the preprocessed data based on a preset time-space correlation model, generating dynamic road network feature data, calling a cooperative transmission model to perform transmission path optimization on the dynamic road network feature data, generating a path optimization parameter set, and performing dynamic adjustment on a target transmission path according to the path optimization parameter set. Generating a self-adaptive transmission path adaptive to the current road network state; and distributing the target vehicle data packet to the roadside node and the cloud node through the self-adaptive transmission path, and carrying out retransmission scheduling on the data packet which is not successfully transmitted based on a feedback confirmation mechanism. According to the invention, the communication efficiency and the system robustness in a vehicle-road cloud collaborative scene can be improved.

The invention discloses a low-altitude integrated management system based on a grid digital twin model and an intelligent algorithm, and relates to the technical field of low-altitude aircrafts. The method comprises the following steps: multi-source sensing data fusion and grid coding are carried out, and an airspace environment space-time database is constructed; constructing an airspace environment space-time database to carry out three-dimensional subdivision on the airspace; generating a multi-scale grid twinborn body, and associating a corresponding attribute for each level of grid according to a management requirement; generating an airspace risk thermodynamic diagram in the future 5-30 minutes; establishing a grid state change triggering rule; and generating a multi-target optimal path set by taking the grid navigation cost as a weight. According to the invention, by combining the real-time operation data of the low-altitude aircraft, the meteorological environment and other data with the artificial intelligence algorithm and the navigation rule base, the airspace traffic flow is analyzed, the flight plan is optimized, the flight route is intelligently distributed, the automatic route setting of any two points is realized, and the response speed of the low-altitude flight service system and the processing capability of various flight data are improved.

The invention relates to the technical field of signal devices, and discloses a traffic signal control method and system based on vehicle-road cloud multi-modal data fusion, and the method comprises the steps: collecting multi-modal traffic data synchronously in real time through a vehicle-end sensor, road-side sensing equipment and a cloud Internet platform; fusing the heterogeneous data by adopting a space-time alignment algorithm, and constructing a standardized space-time feature matrix; traffic flow prediction is carried out based on a multi-layer space-time diagram neural network trained by a federated learning mechanism, and a signal control instruction is generated through reinforcement learning and a multi-objective optimization model; and issuing the green wave parameter, the dynamic timing scheme and the cross-domain coordination strategy to a roadside signal machine through the cloud edge coordination architecture to execute control. The problems that in the prior art, low-delay private network communication cannot be achieved, the data fusion efficiency is low, unmanned driving is not supported, and the deployment cost is high are solved, and the purposes of low-delay communication, high reliability and low risk are achieved.

The invention relates to the technical field of ship communication, in particular to a multi-link converged communication method and system for a ship. The method comprises the following steps: receiving navigation information broadcasted by other ships in a preset range, evaluating the availability of a communication frequency band, and generating sea area communication channel state data; acquiring geographic position data of each ship; space communication link potential analysis is carried out according to the geographic position data of each ship and the sea area communication channel state data, and cross-chain space communication potential data is generated; constructing a multi-link ad hoc network topological structure according to the cross-chain space communication potential data; and task adaptation communication link allocation is carried out based on a multi-link ad hoc network topological structure, and a multi-link fusion transmission strategy is constructed, so that real-time communication link safe switching management is realized. According to the invention, on the basis of communication service adaptation and a multi-link fusion transmission strategy, real-time communication safe switching and high-reliability transmission of the marine formation are realized.

The invention discloses a vehicle positioning method and system based on UWB and vehicle constraint, and relates to the field of vehicle positioning, and the method comprises the steps: deploying a UWB reference station in a well mining environment, and enabling a vehicle to be provided with a UWB tag; obtaining distance measurement data of at least two UWB reference stations, and detecting and compensating the distance measurement data to obtain a vehicle UWB position; collecting vehicle-mounted IMU (Inertial Measurement Unit) data, and obtaining a vehicle attitude quaternion through attitude calculation; collecting vehicle wheel speed meter data, and correcting the vehicle wheel speed meter data according to vehicle motion constraint conditions; according to the vehicle UWB position, the vehicle attitude quaternion and the corrected vehicle wheel speed meter data, the vehicle position is obtained through an adaptive Kalman filtering fusion algorithm. Aiming at low positioning precision caused by easy accumulation of errors and drifting of a traditional inertial navigation system in a well mine closed space in the prior art, the invention reduces the influence of a multipath effect in the traditional inertial navigation system on the positioning precision.

The invention discloses an Internet of Vehicles communication resource allocation method based on federal multi-agent deep reinforcement learning, and belongs to the technical field of deep crossing of intelligent transportation, wireless communication and distributed machine learning. According to the technical scheme, in an Internet of Vehicles scene, a vehicle is taken as an agent, and local training is performed through a multi-agent depth deterministic strategy gradient algorithm: each agent decides spectrum access, power control and bandwidth allocation based on a local state; asynchronous federation learning is adopted, each agent uploads local model parameters to a global server, weights are dynamically adjusted based on model updating quality, communication quality and updating frequency, and a global model is generated through weighted aggregation; and issuing the updated global model to an intelligent agent to realize distributed privacy protection and dynamic optimization of communication resources. The method has the beneficial effects that the spectrum efficiency, the transmission success rate and the system adaptability are remarkably improved on the premise of ensuring the data security, and the method has wide application prospects and commercial values.

The invention discloses an end-cloud cooperative detection method and system for traffic anomalies, and relates to the technical field of intelligent traffic control. The method comprises the following steps: collecting traffic video streams through edge equipment, and identifying abnormal behaviors and generating structured event data by using a lightweight YOLOv3-tiny model; when the confidence exceeds a dynamic threshold and the event type is a high-risk type, uploading a video clip and data to a cloud; the cloud integrates a historical road condition map, meteorological data and a real-time traffic flow state, reconstructs a three-dimensional event scene by adopting a space-time attention pyramid network, and verifies the authenticity of an event in combination with a traffic flow sudden change detection algorithm; and generating a signal lamp forced switching instruction for the risk level overrun event, and issuing the signal lamp forced switching instruction to roadside equipment within 3 seconds to execute emergency response. According to the invention, full-link closed-loop control of traffic accidents from identification to response is realized, and the false alarm probability is greatly reduced while the identification accuracy is guaranteed.

The invention relates to the technical field of traffic communication control, and discloses a vehicle-road cooperative communication optimization system for intelligent traffic. The system comprises a multi-source data sensing module for collecting multi-source heterogeneous data; the communication feature extraction module is used for analyzing the multi-dimensional features; the space-time fusion modeling module is used for constructing a combined space-time feature space; the double-layer resource scheduling library is used for storing an optimization strategy; and the dynamic collaborative decision module is used for generating a real-time scheme and instruction. The method also relates to the functions of abnormal track detection, path re-planning, communication link stability prediction and the like. According to the system, deep fusion and efficient processing of multi-source data are realized, communication resource scheduling and traffic flow regulation and control are optimized, abnormal behaviors of vehicles can be processed in time, the stability of a communication link is guaranteed, the operation efficiency, safety and communication reliability of an intelligent traffic system are effectively improved, and intelligent traffic development is promoted.

The embodiment of the invention provides an unmanned aerial vehicle task scheduling method and device for intelligent road network patrol, and precise planning of an unmanned aerial vehicle patrol route is realized by constructing a three-dimensional road network topology model and an obstacle distribution map. And designing an unmanned aerial vehicle scheduling strategy based on task fitness, and extracting patrol rules from the task template library to perform intelligent scheduling in combination with the real-time position, the endurance mileage and the load state. A grading early warning mechanism is introduced, and the event influence range is analyzed through target detection and scene recognition, so that differentiated early warning of the roadside equipment and the vehicle-mounted terminal is realized. According to the method, the defects of the traditional technology in the aspects of task planning, unmanned aerial vehicle scheduling, early warning linkage and the like are effectively overcome, and the intelligent level and the emergency disposal effect of road network patrol are remarkably improved.

The invention provides an intelligent barn automatic inspection system and method, and relates to the technical field of intelligent inspection, the system comprises an inspection robot, a 5G communication transmission module and a remote computer, the inspection robot comprises an environment sensing module for generating and updating an environment map and identifying obstacle information; the autonomous navigation control module comprises a path planning unit for generating an inspection path and performing local path adjustment; the obstacle avoidance unit is used for generating an obstacle avoidance strategy; the motion control unit controls the movement of the inspection robot; the data acquisition module is used for acquiring information in the granary; the data processing and analyzing module is used for processing and analyzing the collected multi-source data and outputting a monitoring report and early warning information; wherein the 5G communication transmission module is connected with the inspection robot and the remote computer, and is used for transmitting data output by each module in the inspection robot and receiving an instruction sent by the remote computer. According to the invention, real-time sensing, intelligent obstacle avoidance and autonomous inspection of the granary environment are realized.

An unmanned aerial vehicle (UAV) agent for forming a two-tier hybrid smart agriculture network architecture is provided. The UAV agent is configured to support two communication protocols, a short-range communication protocol and a long-range communication protocol. The agriculture sensor gathers agriculture data such as soil temperature and participates in a first-tier short range wireless communication network to send the collected data to at least one UAV agent. A UAV agent participates in one or more first-tier short-range communication networks to pick up sensor data from agriculture sensors in one or more clusters and also participates in a second-tier long-range communication network to route the collected sensor data to at least one cloud server. The tasks in both tier networks are formulated as optimization problems to achieve optimal data delivery and solved by using multi-agent reinforcement learning (MARL), which is implemented by the invented Focus Coordination Multi-Agent Deep Deterministic Policy Gradient (FC-MADDPG) algorithm.

The invention discloses an unmanned sweeper and cloud platform data interaction system based on hybrid encryption, and particularly relates to the technical field of communication interaction information security, which comprises an authority authentication module, a data encryption module, an information processing module, a master-slave authentication control module, a transverse trust management module and a communication scheduling module, an encryption strategy and a key template are dynamically matched through a task score and a mapping model, off-line identity authentication, transverse trust signature and a channel switching mechanism are supported, and the security of data transmission, the fault tolerance of the system and the cooperative reliability are improved under the scenes of task switching, network abnormity and the like; according to the method, a task-driven dynamic encryption matching mechanism is realized, and the data security and the resource efficiency are improved; a multi-level authentication and role signature map is constructed, and the collaborative credibility and fault-tolerant capability of the equipment are enhanced; and a priority index scheduling strategy is introduced to ensure that key data is preferentially transmitted in a high-fluctuation network environment, so that the overall stability and safety of the system are improved.

The invention provides an anonymization processing method and system for emotion data in a vehicle. The anonymization processing method comprises the steps of collecting multi-mode emotion data; correspondingly carrying out local preprocessing and multi-modal alignment processing on the multi-modal emotion data; corresponding sensitive emotion feature information in the preprocessed multi-modal emotion data is extracted through a lightweight recognition algorithm, and the sensitive emotion feature information at the recognized position is packaged in a unified mode; performing desensitization processing on various types of emotion modal data, and encapsulating and synchronizing desensitization results with labels; deep emotional feature extraction is performed on the image, the voice and the physiological signal through a multi-modal fusion model, and the extracted three types of deep features are fused to obtain an emotional representation vector; and inputting the emotion representation vector into a pre-trained emotion recognition model, and outputting the current emotion state of the passenger, including the specific emotion category and the corresponding confidence coefficient. According to the method, emotion analysis and transmission are performed after data anonymization is realized, and accurate judgment of the system on the emotion state is not influenced while privacy security of the user is ensured.

The invention provides an AmBC-NOMA communication resource joint optimization method for a high mobility V2X scene, and relates to the technical field of communication resource joint optimization. Aiming at channel dynamic change caused by high-speed motion of a vehicle and the influence of the channel dynamic change on short packet communication reliability and rate performance, the method combines reflection type communication and non-orthogonal multiple access technologies, and on the basis of fully considering channel time-varying characteristics, a reflection coefficient, a power distribution coefficient and block length resource distribution in a system are jointly optimized, so that the reliability and the rate performance of short packet communication are improved. Therefore, the communication rate performance and the resource utilization efficiency under the requirements of short time delay and low error rate are improved. Through the method, the short packet transmission rate of the system can be remarkably improved under the conditions of limited time delay and channel uncertainty, and the actual application requirements of the future Internet of Vehicles system for efficient, low-time-delay and high-reliability short packet communication are met.

The invention discloses a vehicle-road cooperation path planning decision-making system based on deep learning, and belongs to the technical field of intelligent traffic. According to the system, global traffic flow data, vehicle positions and vehicle driving destinations are collected, traffic flow prediction of each road section is carried out in combination with a GNN-LSTM fusion model, and a basis is provided for global path planning; the motion trails of the participants are predicted through a Transform model, and real-time interactive decision making between the vehicles and the participants is achieved through a DQN algorithm; vehicle state data, traffic light time sequence data and intersection geographic parameters are fused based on an MPC algorithm to generate a vehicle intersection passing track, whether a dangerous space-time window exists or not is detected through an IoU algorithm, whether the MPC algorithm with constraints is adopted to correct the vehicle intersection passing track or not is determined, and intersection collaborative decision making is achieved. According to the invention, the dynamic adaptability of vehicle path planning is improved, the safety of vehicle-road cooperation and interaction with surrounding participants is enhanced, and the traffic efficiency is effectively improved.

Methods and systems are disclosed for resource management on Sidelink for vehicle to everything (V2X) scenarios. Example methods and systems for a Resource Structure on Sidelink are disclosed. Control and data channels may be Frequency Division Multiplexed (FDMed) or Time Division Multiplexed (TDMed) with an Orthogonal Frequency Division Multiplexing (OFDM) waveform. Example methods and systems for a Resource Configuration on Sidelink are disclosed. The resource configuration may be statically configured or may be locally configured. Example methods and systems for Assisted Resource Allocation are disclosed. Assisted Sensing may be performed by a group lead or RSU and local scheduling may be performed by a group lead or RSU.

The invention discloses an Internet of Vehicles network selection and switching decision system supporting access of multiple operators. According to the method and the device, the timeliness and the continuity of network switching are remarkably improved through prediction and planning in advance. The module depends on a trajectory-network matching prediction sub-module, combines a vehicle navigation trajectory and a global digital twinborn model, identifies a base station coverage area in a future driving path and the network service quality of each operator in advance, and avoids the lag problem that switching is triggered only after signals are weakened in traditional passive switching. The pre-switching resource reservation sub-module further sends a resource request to a target operator base station and confirms reservation before the vehicle enters a new road section, ensures that resources such as bandwidth and time slot required during switching are in place, reduces switching failure or interruption caused by resource competition, avoids common communication interruption after signal sudden drop in traditional switching, and improves the switching efficiency. And continuous transmission of key businesses such as automatic driving control instructions and real-time road conditions is ensured.

The invention discloses a dynamic beam adaptive optimization method for the Internet of Vehicles, and the method comprises the steps: firstly obtaining the current position, speed and driving direction of a vehicle, and carrying out the prediction of a future trajectory through combining historical trajectory information and employing a Kalman filtering technology and the like; and selecting a candidate beam direction from the predefined beam codebook. The system receives channel quality feedback information of a communication link in real time, and dynamically updates a beam forming weight based on an improved recursive least square algorithm. According to the algorithm, a composite adaptive adjustment mechanism of an error size and an error change trend is introduced, a multi-objective optimization function is constructed, and performance indexes such as link quality maximization, interference power minimization and beam switching frequency minimization are effectively balanced in a dynamic environment, so that an optimal beam direction is obtained. Finally, the real-time switching of the antenna array beams is realized through a low-overhead and low-delay control protocol. According to the method, track perception and a self-adaptive feedback mechanism are fused, and good real-time performance, stability and expandability are achieved.

The invention relates to the technical field of intelligent traffic and automatic driving, and discloses a multi-link redundancy guarantee method for an intelligent driving vehicle in a highway extreme scene, which comprises the following steps: acquiring vehicle-mounted sensor data, roadside equipment data and cloud traffic data corresponding to a preset highway section, constructing a global traffic situation map according to the vehicle-mounted sensor data, the roadside equipment data and the cloud traffic data; according to the global traffic situation map, evaluating the risk level of each highway section, and determining the takeover control strategy of the intelligent driving vehicle in each highway section according to the risk level; under the condition that the takeover control strategy is a formation cooperation strategy, generating a formation cooperation instruction of intelligent driving vehicles in a corresponding highway section, and sinking the formation cooperation instruction to a roadside edge node; and sending the formation cooperation instruction to the intelligent driving vehicle based on a multi-link redundancy architecture.

The invention relates to the technical field of warehouse-in and warehouse-out management, in particular to a second-hand car warehouse-in and warehouse-out management system and method. The system comprises a vehicle RFID tag module, an intelligent electronic price tag module, a warehouse-in management module, a warehouse-out management module, an inventory monitoring and alarming module, a multi-point RFID positioning network module and an inventory map visualization module, and a unique encrypted RFID tag can be configured for each second-hand vehicle; binding an intelligent electronic price tag for each second-hand vehicle, and receiving verification to generate a verification result; collecting label information when the vehicle enters the parking lot; the label information is verified when the vehicle leaves; receiving the verification result of the intelligent electronic price tag and the vehicle in-library data of the database to lock the position of the abnormal vehicle; dense RFID reader nodes are arranged in the market to collect and output positioning data; and receiving the positioning data and the vehicle state information, generating a real-time inventory distribution map, and providing a scheduling and site planning decision basis for a manager. The scheduling and planning efficiency of managers can be improved.

The invention provides a vehicle identity anonymous authentication method in a space-air-ground fusion vehicle-mounted network. The method comprises the following steps: carrying out multi-source data acquisition; generating a dynamic area identifier based on the vehicle real-time position data, and performing dynamic updating according to a vehicle moving threshold value; vehicle equipment identification information is acquired, a salted hash value is generated in combination with the dynamic region identifier, and an anonymous certificate containing dynamic effective duration is generated; extracting spatio-temporal data fluctuation parameters according to the environmental perception data, and extracting delay trend parameters and channel stability parameters according to the regional network state data; the weight is dynamically adjusted according to the link type and the time period type, and the dynamic trust quantized value of the vehicle is calculated through weighted summation; differential authentication is executed based on the dynamic trust quantized value of the vehicle and the anonymous voucher, and the differential authentication comprises the steps that lightweight verification is adopted when the vehicle is highly trusted, two-factor authentication is started when the vehicle is low trusted, and link timeout redundancy authentication is combined. According to the method, the trust evaluation accuracy can be improved, and the dynamism of a privacy protection mechanism is enhanced.

An example system includes one or more memories for storing grid-free vehicle data and one or more processors configured to determine one or more first features from first vehicle data of the vehicle data, the first vehicle data being from a first vehicle of the plurality of vehicles. The one or more processors are configured to determine one or more second features from second vehicle data of the vehicle data, the second vehicle data being from a second vehicle of the plurality of vehicles. The one or more processors are configured to fuse the one or more first features and the one or more second features to generate fused features. The one or more processors are configured to generate a BEV representation based on the fused features.

The invention discloses a bus and station interactive scheduling method and system based on V2X and deep reinforcement learning decision, a medium and equipment, and the method comprises the steps: collecting bus operation dynamic data, station passenger flow and environment data in real time, and combining V2X network interaction information to construct a full-dimension perception system; a deep reinforcement learning model is adopted for dynamic decision making, intelligent closed-loop control of vehicle scheduling, station service and traffic signal cooperation is achieved, the bus punctuality rate is remarkably increased, the waiting time of passengers is shortened, operation safety is guaranteed through active early warning of abnormal conditions, and intelligent upgrading of a bus system from passive response to active prevention is achieved.

The invention relates to a multi-source sensing fusion and dynamic resource allocation method based on vehicle infrastructure cooperation, and belongs to the technical field of mobile communication. The single-vehicle environment sensing is to complete the detection of the surrounding environment by using the sensing data of the single-vehicle environment sensing. However, the problem that heterogeneous data fusion efficiency is low due to the fact that the sensing range of single-vehicle sensing is limited exists. In order to solve the problem, the method comprises the following steps: firstly, constructing a vehicle-road double-layer cooperative sensing network, and improving the accuracy of shelter detection through a multi-source sensor space-time alignment and confidence weighting mechanism; secondly, designing an improved discrete dogvessel swarm optimization algorithm, balancing global exploration and local development by using a chain collaborative search strategy, and improving the perception fusion quality; and finally, developing a mixed action space depth deterministic strategy gradient algorithm, dynamically coordinating the resources of the intelligent network connection vehicle and the road side unit, and reducing the sensing time delay. The scheme is suitable for environmental perception optimization and edge computing resource scheduling of the vehicle in a complex traffic scene, and aims to improve perception fusion efficiency.

The invention relates to the technical field of intelligent networked vehicles, in particular to an intelligent networked vehicle remote control and monitoring method. The method comprises the following steps: acquiring real-time running state information and roadside sensing data of a vehicle, and generating a remote takeover request; receiving a control instruction sent by the cloud control platform, and feeding back a control execution result; calculating instruction interaction delay and network transmission delay; the stability of the communication environment is evaluated by combining the two, and the vehicle driving mode is dynamically adjusted. Furthermore, time delay is calculated according to heartbeat signals and control instruction timestamps in the communication link, the stability index of the communication environment is evaluated, and the safe driving speed is adjusted according to the stability. And when the communication environment stability is lower than the threshold value or the instruction interaction delay exceeds the threshold value, an emergency stop mechanism is triggered. According to the method, the safety and the stability of the intelligent network connection vehicle under remote control can be improved.

The present disclosure relates to an electronic device, method, and storage medium for wireless communication. Various embodiments for enhancing sidelink (SL) performance are described. In an embodiment, the electronic device comprises a processing circuit, and the processing circuit is configured to: determine a V2X communication policy for a specific region, wherein the V2X communication policy comprises at least one of service control information, communication assistance information, and transmission control information; and transmit the V2X communication policy, enabling a first terminal device to obtain the V2X communication policy.

The invention discloses an unloading strategy and resource scheduling combined method based on the Internet of Vehicles. The method comprises the following steps: constructing a multi-server and multi-user vehicle edge computing system; constructing a communication model according to a vehicle edge computing system; constructing a calculation model; establishing a joint optimization problem model by taking balance of time delay and energy consumption and maximization of task success rate as targets; and solving the joint optimization problem model by using a grey wolf optimization algorithm based on chaotic mapping and differential mutation and a resource allocation algorithm based on delay balance to obtain a task unloading strategy set and a resource allocation strategy set. According to the unloading strategy and resource scheduling combined method based on the Internet of Vehicles, the problems that in the prior art, the task success rate is low due to uneven resource allocation in a multi-server environment, and single target optimization in a dynamic environment is difficult to meet the real-time performance and energy efficiency requirements at the same time are solved.

The invention discloses an elastic resource allocation and barrier gate linkage control system and method for mixed traffic of parking lots, a medium and equipment, and the system comprises a vehicle type recognition module, a dynamic resource allocation module, a cooperative control decision module, an emergency channel management module and a multi-mode communication module. According to the parking lot mixed traffic management system integrating vehicle-road cooperative communication, double-layer game optimization and elastic resource pool division, an independent resource pool and a dynamic quota adjustment mechanism of automatic driving vehicles and manual driving vehicles are constructed, and a charging pile-barrier gate cooperative optimization unit and a multi-stage emergency response protocol are combined; the global optimization of parking lot resource allocation and traffic control is realized, and the problems of prominent resource competition contradiction, separation of barrier gate control and resource allocation, insufficient intelligence of an emergency response mechanism and short delay of multi-mode communication and decision cooperation in a mixed traffic scene are solved.

Disclosed in the present invention is a three-dimensional roadside sensor deployment method for collaborative sensing in the Internet of Vehicles. The method comprises: modeling an RSN model of a three-dimensional road environment; on the basis of the RSN model, constructing sensing detection functions for single sensors, and when there are a plurality of sensors providing coverage, constructing a global sensor detection function for each deployable area on the basis of the sensing detection functions for the single sensors; taking the maximization of the proportion of deployable areas that meets or exceeds a minimum detection probability as a first objective optimization problem function, which maximization is based on the global sensor detection function, taking the minimization of the total number of sensors in each deployable area as a second objective optimization problem function, and constructing a deployment optimization reward function consisting of the first objective optimization problem function and the second objective optimization problem function; and constructing a decision sequence, and on the basis of a decision transformer, solving the decision sequence to obtain a deployment policy, wherein a cumulative reward value is obtained by performing reward reallocation on the deployment optimization reward function by means of a two-layer optimization method. The present invention implements sensor deployment in a three-dimensional road environment.