3364 results about "Vehicle type" patented technology

The invention belongs to the technical field of charging station planning, and discloses a charging station planning method and system based on an automobile charging demand, and the method comprises the steps: building a space-time charging demand distribution map through multi-source data collection and fusion processing, carrying out the seasonal change analysis and future demand prediction, and obtaining a dynamic charging demand prediction model; site layout optimization under a multi-constraint condition is performed by combining an urban road network structure and traffic flow data to form a preliminary charging station layout scheme, and power grid load capacity evaluation and renewable energy access analysis are implemented to establish an energy collaborative supply guarantee system. And designing a peak-valley period charging price dynamic adjustment and appointment queuing mechanism to form an intelligent scheduling control strategy, and finally obtaining a diversified charging facility configuration scheme through different charging power requirements and vehicle type suitability evaluation. According to the method, the problems of inaccurate demand prediction, unreasonable layout, uneven resource allocation and the like in traditional planning are solved, and accurate matching of charging resources and user demands is realized.

The invention provides a charging pile intelligent scheduling processing method and system. Wherein the dynamic charging demand network topology is constructed by fusing the remaining electric quantity of the vehicle, the occupation state of the charging pile and the vehicle type priority label. And updating vehicle position and charging pile state data in real time, predicting a vehicle motion trend and generating a preemption conflict early warning signal. And based on a multi-main-body coordination distribution mechanism, a matching degree score of the vehicle and the charging pile is calculated in combination with a residual electric quantity attenuation rate and a departure time constraint, and a distribution strategy is dynamically optimized. And finally, the power distribution gradient is adjusted according to the score sequence, so that the high-electric-quantity attenuation vehicle obtains higher power improvement, and efficient resource matching and conflict relief are realized. According to the technical scheme, through dynamic prediction and priority matching, charging pile distribution and power adjustment are intelligently optimized, the charging efficiency of the parking lot is remarkably improved, and vehicle waiting conflicts are reduced.

The invention provides a toll vehicle type identification method and system based on expressway passing images, and the method comprises the steps: obtaining an image set of passing vehicles, carrying out the multi-scale brightness compensation processing of a vehicle side contour image, generating a side feature enhancement image, carrying out the contour sharpening processing of a vehicle top contour image, and obtaining a vehicle top contour image; and generating a top feature enhanced image. And performing dual-channel feature fusion on the side feature enhanced image and the top feature enhanced image, generating a vehicle multi-dimensional fusion feature map, inputting the vehicle multi-dimensional fusion feature map into a pre-trained vehicle type matching model, and obtaining a vehicle type feature matching result output by the vehicle type matching model. And performing similarity comparison according to a vehicle model feature matching result and a pre-stored vehicle model feature library, determining a charging vehicle model category corresponding to the passing vehicle, and generating a vehicle model identification mark. According to the method, the robustness of vehicle type recognition in a complex illumination environment can be improved, and meanwhile, the accuracy of vehicle type classification and the charging and passing efficiency are improved by fusing dual-view feature enhancement and a multi-dimensional feature matching mechanism.

Examples relate to vehicle battery systems comprising a base unit connectable to a vehicle's electrical system, a removable modular replaceable battery, and a secondary battery within the base unit. The base unit is configured to interchangeably use modular replaceable batteries of different chemistries. Control circuits optimize charging and discharging parameters based on battery chemistries. The secondary battery provides power when the modular battery is removed. The system includes thermal management, compatibility with lead-acid charging systems, and mobile base unit functionality. The modular battery can power external devices and can foreseeably store energy up to and beyond 1 kWh. The system operates at various voltages suitable for different vehicle types.

The invention provides a vehicle and goods matching method, electronic equipment and a readable storage medium. The vehicle and goods matching method comprises the following steps: in response to a goods source matching request of a target driver on a logistics transportation platform, determining a target departure place, a target destination, a target vehicle length and a target vehicle type; generating a target screening condition based on the target departure place, the target destination, the target vehicle length and the target vehicle type; according to the target screening condition and the candidate goods sources, determining a first goods source set; based on the feature information of the target driver and the attribute information of the first goods sources, carrying out screening and first sorting processing on the M first goods sources to obtain a second goods source set; based on the target weight, performing second sorting processing on N second goods sources in a second goods source set to obtain a target goods source set; wherein the target weight is at least one of a behavior distance weight, a behavior time weight, a vehicle length consistency weight and a vehicle type consistency weight; and responding to the goods source matching request based on the target goods source set.

The invention discloses a portable unmanned aerial vehicle sound wave countering system and method, and relates to the field of radio countermeasure and sound wave interference, and the system comprises a multi-source sensing module which is used for obtaining a multi-modal feature data set of a target unmanned aerial vehicle; the signal processing module is used for carrying out space-time correlation analysis on the multi-modal characteristic data set and generating a threat evaluation parameter set containing an unmanned aerial vehicle type identification result, communication protocol analysis characteristics and navigation mode discrimination indexes; the countering strategy generation module is used for generating a sound wave modulation parameter set, a radio frequency interference waveform sequence and a positioning deception signal template; the cooperative interference module is used for generating a multiband joint countering instruction set according to the improved phase cooperative control strategy; and the execution monitoring module is used for executing the multiband joint countering instruction set in the preset airspace range. According to the portable unmanned aerial vehicle sound wave countering system and method provided by the invention, the multi-level countering capability for different threat levels is improved.

The invention relates to the field of new energy charging, in particular to a new energy charging management method for a smart city. The method comprises the steps that a municipal vehicle operation feature data set is acquired, on the basis, municipal vehicles are subjected to type division through a vehicle type classification strategy, and a municipal vehicle driving type set is determined; acquiring a municipal vehicle task set, and on the basis, tracking a dynamic task variable set of a task execution vehicle in a municipal vehicle driving type set in a task execution process; according to the dynamic task variable set, analyzing an association relationship between the dynamic task variable set and the real-time power consumption data through a multi-modal fusion strategy, and determining dynamic power change information; and acquiring a charging station information set, dynamically optimizing a charging decision in combination with the municipal vehicle task set and the dynamic electric quantity change information, and determining and outputting an optimal charging strategy. In the municipal vehicle charging decision-making process, the utilization rate of charging resources is improved, and the continuity of municipal task execution is ensured.

The invention discloses a vehicle illegal parking detection system based on unmanned aerial vehicle inspection shooting, and relates to the technical field of vehicle illegal parking inspection. The vehicle illegal parking detection platform is in communication connection with an unmanned aerial vehicle image acquisition module, an image preprocessing module, a vehicle detection analysis module, an illegal parking judgment module, a license plate recognition module and an early warning notification module, and all the modules are in electric signal connection. According to the method, a large area can be rapidly covered through unmanned aerial vehicle inspection shooting, real-time monitoring and detection of vehicle illegal parking behaviors are achieved, compared with a traditional manual inspection mode, unmanned aerial vehicle inspection greatly shortens the detection period, the detection accuracy is remarkably improved, and the detection efficiency is improved through a high-precision image recognition algorithm. The vehicle illegal parking detection system can accurately recognize the license plate number, the vehicle type and the illegal parking position, and subjectivity and errors of manual judgment are effectively avoided.

The invention belongs to the technical field of intelligent traffic, and discloses a space-time fitting-based whole vehicle type traffic feature accurate recognition algorithm, which comprises the steps of obtaining driver behavior data, vehicle dynamic data and road supervision data; preprocessing the driver behavior data, the vehicle dynamic data and the road supervision data to obtain a driver behavior feature data set, a vehicle dynamic feature data set and a road supervision feature data set; according to the vehicle dynamic feature data set and the road supervision feature data set, obtaining a vehicle initial driving track; optimizing the initial driving track of the vehicle through an improved space-time interpolation algorithm to obtain a complete driving track of the vehicle; according to the complete driving track of the vehicle, the vehicle driving speed of each road section is calculated and compared with the limiting speed of the corresponding road section, and whether the vehicle is overspeed is judged; and traffic accidents caused by overspeed, fatigue driving and the like are effectively avoided.

The invention discloses a highway traffic operation carbon emission monitoring and calculating method, which comprises the following steps of: classifying vehicles running on a highway according to vehicle types, and establishing a carbon emission correction model of a carbon emission coefficient and a longitudinal slope rate of the vehicle type through a real vehicle test; arranging a monitor in the section of a target highway section, and acquiring basic information and driving information of vehicles passing through the section through the monitor; a carbon emission factor method is adopted to calculate the carbon emission Cm before correction of the vehicle m in the highway section interval; and S3, according to the carbon emission correction model established in the step S1, in combination with the average longitudinal slope ratio of the road section, correcting the carbon emission Cm before correction in the step S3, and obtaining the carbon emission C after correction of the vehicle m in the specified highway road section interval. According to the calculation method, available information is fully utilized from the perspective of an expressway operation management party, and the expressway traffic operation carbon emission monitoring calculation precision is improved.

The invention discloses an unmanned aerial vehicle cluster ground attack task allocation method and system based on an improved particle swarm algorithm, belongs to the technical field of intelligent optimization algorithms, and aims at solving the problem of unmanned aerial vehicle cluster ground attack task allocation. And analyzing two types of constraints including the target type constraint and the unmanned aerial vehicle type constraint, and establishing a mathematical model for maximizing the attack cost-effectiveness ratio. The method comprises the following specific steps: step 1, establishing a target function for distributing and establishing a target attack revenue and a target attack cost to the ground attack task, wherein the target function is used for maximizing the target attack revenue and minimizing the target attack cost; 2, analyzing constraint conditions of ground attack task allocation; 3, integrating the target function and the constraint condition of the ground attack task allocation, and establishing a mathematical model of the ground attack task allocation; and 4, solving the mathematical model established in the step 3 by using an improved particle swarm algorithm to obtain a final distribution result.

The invention provides a vehicle path planning method based on an adaptive optimization algorithm, and relates to the technical field of adaptive algorithm application, and the method comprises the steps: 1, based on a current order demand, a vehicle resource and a preset constraint condition, obtaining a vehicle path planning result; an initial path scheme set meeting the requirements of a time window, the number of splitting times, vehicle type matching and a temperature zone is generated through a step-by-step progressive decision-making process; 2, starting a plurality of independent optimization links according to the initial path scheme set, and dynamically adjusting local path nodes by each link through a greedy strategy to generate an optimization candidate scheme set; and step 3, performing real-time evaluation on the optimized candidate scheme set, determining a final candidate scheme based on the cost saving rate, the demand satisfaction rate and the soft service quality index, and updating the final candidate scheme as the current reference scheme. According to the method, dynamic events are sensed in real time through an adaptive optimization algorithm, influences are evaluated, the path is intelligently adjusted, and the timeliness and accuracy of path planning are improved.

The invention relates to a data-driven intersection vehicle complex interaction behavior strategy simulation method and system. The method comprises the following steps: acquiring real intersection vehicle traffic trajectory data, constructing a plurality of interaction scenes, extracting observation information of each time step, and obtaining real vehicle trajectory data; taking each vehicle as an intelligent agent, based on a Gaussian strategy network pre-constructed according to the vehicle type, calculating a reward by using a reward based on a target guiding gain and a reward estimator from a discriminator, and generating simulated vehicle trajectory data; based on the real vehicle trajectory data and the simulated vehicle trajectory data, respectively extracting observed value-action pair information, updating a discriminator of an intelligent agent by taking distinguishing different attribute information to the greatest extent as a target, and updating a Gaussian strategy network and a value estimator network in a generator by taking confusing the discriminator to the greatest extent as a target, so as to obtain the real vehicle trajectory data and the simulated vehicle trajectory data. And vehicle complex interaction behavior strategy simulation based on multi-agent countermeasure reverse reinforcement learning is realized.

The invention discloses an intelligent cleaning method and system for an engineering vehicle, and the method comprises the steps: capturing a vehicle surface image of a current to-be-cleaned engineering vehicle, carrying out the preprocessing of the vehicle surface image, and obtaining vehicle image data; according to the vehicle image data and the trained machine learning model, obtaining a stain identification result and vehicle type information of the engineering vehicle; based on a stain identification result and vehicle type information, an optimal cleaning scheme is comprehensively calculated, and cleaning parameters are intelligently adjusted; the cleaning state is monitored in real time, abnormal conditions are detected, and when water source insufficiency and machine faults occur, an alarm is given out immediately, and corresponding treatment measures are taken. The cleaning efficiency of the engineering vehicle is remarkably improved, the cleaning time is shortened, and the labor cost is reduced; through the synergistic effect of stain analysis and intelligent cleaning, targeted cleaning and comprehensive deep cleaning are achieved, and the cleaning quality is improved.

The invention belongs to the field of road vehicle control systems, and relates to a method for predicting the risk of collision between an electric bicycle and a vehicle based on the multi-modal behavior of the electric bicycle, which utilizes physical modeling and takes the influence of the braking distance and the transverse and longitudinal stability of the electric bicycle on the vehicle as a basic risk; automobile risks caused by the environment are further considered, and multi-dimensional environment risk assessment modeling is carried out by integrating four dimensions of dynamic distance-speed, adjacent vehicle types, traffic flow density and weather conditions (precipitation, temperature, wind speed and visibility); the risk of electric bicycle operation behaviors (acceleration change and steering angle and frequency) to the automobile is combined; the complex relation between the environment change and the whole vehicle risk is captured in real time by using the environment sensitivity coefficient, and finally a set of comprehensive, accurate and real-time multi-dimensional comprehensive risk assessment method is provided for the vehicle for the multi-mode riding behavior of the electric bicycle, so that the collision risk in the complex traffic flow environment is reduced.

The invention provides a road inspection method and device in severe weather, electronic equipment and a storage medium, and the method comprises the steps: carrying out the recognition processing of a scene image through a road inspection model, and obtaining a road inspection result of a target road; the training process of the road inspection model comprises the following steps: acquiring a data set, wherein the data set comprises a severe weather image; performing feature extraction on the data set by adopting a pyramid network to obtain a plurality of levels of first feature extraction results; when information fusion is carried out on the first feature extraction results of the multiple levels, feature adaptation processing is carried out according to the first feature extraction results, and a second feature extraction result is obtained; performing feature enhancement modulation on the second feature extraction result by adopting double branches, and merging feature enhancement modulation results to obtain a modulation feature map; and predicting the modulation feature map to obtain a prediction result. The method has the beneficial effect that the vehicle type identification accuracy during road inspection in severe weather is improved.

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.

The invention discloses a traffic signal timing optimization method and system, a storage medium and a program product, and relates to the field of traffic management data processing systems.The method comprises the steps that a plurality of adjacent intersection nodes are determined based on position identification information of local intersection nodes; collecting vehicle passing state data and vehicle characteristic data of a local intersection node, and generating traffic flow characteristic parameters; identifying a characteristic vehicle type and vehicle position information of the target characteristic vehicle; calculating the traffic influence score of the target characteristic vehicle on the traffic flow of each intersection; constructing a traffic signal optimization model, and calculating based on the traffic signal optimization model to obtain an initial timing scheme; sending the traffic flow output data to the adjacent intersection node, and receiving traffic flow input data sent by the adjacent intersection node; and updating the traffic signal optimization model, generating a final timing scheme, and adjusting signal control parameters of local intersection nodes. According to the invention, the accuracy of traffic signal timing can be improved.

The invention relates to an AI identification road traffic safety illegal behavior image evidence obtaining method and device based on an unmanned aircraft, equipment and a medium, and the method comprises the steps: calling a no-parking region identification model, so as to determine a no-parking region mask in a to-be-inspected region image, calling a potential violation vehicle detection model to identify potential violation vehicles in the no-parking area mask so as to determine bounding boxes of the potential violation vehicles and vehicle types corresponding to the bounding boxes; and calling a target tracking algorithm to perform target tracking on the potential violation vehicle, and determining the potential violation vehicle as a target violation vehicle when detecting that the intersection-to-union ratio between the bounding box and the no-parking area mask in the plurality of continuous image frames exceeds a preset threshold value and the potential violation vehicle is in a static state. And sending the violation evidence packet corresponding to the target violation vehicle to a traffic management system. According to the invention, the efficiency of the monitoring system is improved, and accurate and verifiable violation evidences are provided for the traffic management system.

The invention discloses a new energy automobile charging pile parking space occupation detection and billing linkage control method, and relates to the technical field of urban public resource optimization distribution and new energy automobile charging pile parking space management. Comprising the steps of multi-source sensing and state fusion: sensing real-time occupation states, vehicle types and charging behavior states of all parking spaces in real time by using a parking space detector, an ALPR visual sensor, a Hall sensor and a charging pile state communication protocol, collecting original data, deploying edge nodes at the parking spaces, and fusing the original data in real time, outputting a unified parking space state object after fusion; original data are collected in real time through the parking space detector, the ALPR visual sensor, the Hall sensor and the charging pile state communication protocol, and through multi-dimensional behavior analysis of the parking space state, the abnormal behaviors that a fuel truck occupies a space for a long time, an electric vehicle occupies the space and is not charged, and the electric vehicle is detained after being charged can be accurately recognized; and an accurate basis is provided for subsequent charging processing and precise management.

The invention discloses an intelligent safety management method and system for a highway service area, and the method comprises the steps: recognizing the number of vehicles in the service area, and obtaining historical flow data and analysis data corresponding to the historical flow data; identifying the types of vehicles entering the service area, obtaining parking space data of parking areas corresponding to different types of vehicles, and guiding the vehicles to parking spots; safety monitoring is carried out on the vehicles of the focus type in the recognized vehicle types; and pushing the data to a manager. Through accurate identification of the number and types of vehicles entering a service area and combination of historical vehicle conditions of the service area, dynamic analysis of vehicle flow data can be realized, optimal distribution of parking resources in advance is realized, congestion caused by too many vehicles is avoided, and meanwhile, real-time monitoring and early warning of key vehicle types are realized, so that the service area is more intelligent. Safety accidents can be found in time at the beginning, and expansion of the accidents is avoided.

A real-time parking management system (100) comprising: a plurality of RFID tags (1) each assigned to a vehicle, wherein the RFID tags (1) are configured to store a unique vehicle identifier; a plurality of RFID readers (2) positioned at the entry and exit points of a parking facility, wherein the RFID readers (2) are configured to detect the RFID tags (1) and read the vehicle identifier; a plurality of visual sensors (3) positioned at parking spaces, entry and exit points, configured to capture images or video feeds to verify vehicle occupancy, detect unauthorized parking, and assist with license plate recognition;a machine learning module (4) configured to analyze historical parking data to predict peak parking times, optimize parking space allocation based on vehicle type, size, and user preferences, and enhance security by detecting suspicious activity based on visual sensor data; a central server (5) configured to receive vehicle identification from the RFID readers (2), process input from the visual sensors (3), confirm vehicle presence, access a database to retrieve and store vehicle information, dynamically allocate parking spaces based on availability, vehicle classification, and user preferences, track parking duration for each vehicle, and control entry and exit gates based on verified credentials and security policies;and a user interface unit (6) configured to allow users to reserve parking spaces, display parking space availability in real time, facilitate payment of parking fees using integrated payment gateways, provide step-by-step navigation support to assigned parking spaces, and inform users about parking status, entry confirmations, and security alerts.

The invention discloses a customized order receiving method and system for a designated driver based on a saas platform. The invention relates to the technical field of designated driving services. Receiving demand data of a driver, wherein the demand data comprises order receiving preference, a service area and service time; receiving personal information input by a driver, wherein the personal information comprises basic information and order receiving preference setting; wherein the demand data comprises order receiving preferences, service areas and service time of drivers and users; wherein the basic information comprises a name, a driving age and a vehicle type; according to the invention, the driver is allowed to set personalized order receiving conditions according to own order receiving preference, service area and time, so that the working flexibility of the driver is greatly improved. The driver can autonomously select the order suitable for himself / herself, so that the rejection rate of improper orders is reduced, and the working efficiency and the satisfaction degree are improved. And through an intelligent order receiving matching algorithm, the system can quickly match the most appropriate order for the driver, so that the empty driving time and waiting time of the driver are reduced, and the working efficiency and income are further improved.

The invention belongs to the technical field of virtual automobile exhibition, and particularly relates to a virtual automobile exhibition system based on digital twinning. The system comprises a digital twinning construction module which generates a lightweight model and supports first-person visual angle dynamic updating; the user behavior analysis module is used for mapping data such as a sight track into a weighted directed graph, and predicting vehicle types and configuration requirements by using a multi-head graph attention network; the personalized configuration selection module is used for generating a configuration scheme in combination with digital twinborn parameters; the virtual test driving module is used for providing immersive experience based on the configuration scheme and the scene; and the background data analysis module integrates the data to generate a user portrait so as to optimize the exhibition content. According to the system, dynamic virtual-real mapping, accurate demand prediction and data-driven intelligent exhibition are realized, and the user interaction depth and the car purchase efficiency are improved.

The invention discloses a road shoulder vehicle exclusion method based on an inspection system, and relates to the technical field of intelligent transportation, and the method comprises the steps: obtaining a parking area image shot by an inspection vehicle, recognizing a vehicle target in the image, and extracting the license plate position information; based on vehicle three-dimensional feature reconstruction and space projection analysis, generating a space distribution thermodynamic diagram of the vehicle in the image coordinate system; constructing a dynamic segmentation line model, and adaptively generating a road shoulder region judgment boundary according to the parking space type and the road geometric parameters; and through a multi-target trajectory association algorithm, the berth vehicle and the road shoulder vehicle are distinguished, and the berth occupation state is output. According to the scheme provided by the invention, the spatial positioning precision and behavior judgment reliability in a complex scene can be remarkably improved, the limitations on environmental adaptability, vehicle type compatibility and time sequence continuity are broken through, and a road shoulder violation management and control solution with high robustness and full-time coverage is provided for urban traffic management.

The invention discloses a heavy vehicle axle load identification method based on bridge deflection influence line distribution, and the method comprises the following steps: erecting a monitoring device for vehicle parameter identification on a bridge floor, and arranging a structure displacement detection device at the midspan of a bridge to identify the midspan deflection of the bridge; carrying out on-site vehicle running test to obtain influence lines of small heavy vehicles with different vehicle types and vehicle weights on the midspan deflection of the bridge; performing simulation amplification according to the influence line data of the small heavy vehicle; constructing a heavy vehicle axle load identification model, and performing model training through the obtained influence line data corresponding to the heavy vehicle; and inputting the data of the target heavy vehicle during bridge crossing into the heavy vehicle axle load identification model to obtain axle load data of the target heavy vehicle. The method has the advantages that the vehicle axle load is estimated through on-bridge visual recognition and under-bridge deflection calculation, and the influence of device installation on bridge floor traffic can be avoided; and meanwhile, the recyclable structural displacement detection device can effectively reduce the investment cost of equipment.

The invention relates to a traffic state perception and control method based on laser vision fusion, and relates to the field of intelligent traffic, and the method comprises the steps: obtaining vehicle images and point cloud data through a traffic monitoring system of a highway entrance ramp, carrying out the vehicle static feature recognition and track reconstruction, and carrying out the analysis to obtain multi-dimensional parameters; according to the method, the traffic flow monitoring means such as license plate attribution distribution, vehicle type distribution and running speed mean value are selected, the congestion condition is predicted, the predicted congestion coefficient is output, and finally a traffic control scheme is formulated and executed according to the predicted congestion coefficient, so that the problems of insufficient data accuracy and reliability caused by single traffic flow monitoring means are solved; the method solves the technical problems of low prediction comprehensiveness and low prediction precision caused by traffic congestion prediction, effectively improves the accuracy and reliability of congestion prediction, provides a scientific basis for traffic management, and is helpful for optimizing traffic flow and reducing congestion.

The invention discloses a non-signalized intersection vehicle right-of-way decision and speed guidance method. The method comprises the following steps: S1, collecting positions, speeds, accelerated speeds, vehicle types and intention information of all vehicles in a non-signalized intersection planning area; s2, determining a motorcade division scheme according to a formation rule combined with the vehicle intention; s3, establishing a road right optimization model of the non-signalized intersection, and determining the moment when each vehicle enters a confluence area of the non-signalized intersection and the allocated vehicle fleet occupation time of the non-signalized intersection; s4, constructing a speed suggestion algorithm, and inputting the moment when the vehicle enters the confluence area of the non-signalized intersection and vehicle state information; and S5, providing an on-line speed suggestion for the vehicle, and realizing conflict-free passing of the vehicle at the non-signalized intersection. The non-signalized intersection vehicle right-of-way decision and speed guidance method provided by the invention has stronger adaptability and can reduce the calculation complexity, and the vehicle passing efficiency and safety are improved.

The invention relates to a millimeter-wave radar global vehicle track splicing method based on track similarity, which comprises the following steps of: sensing a moving vehicle on a traffic road by utilizing millimeter-wave radars arranged on the traffic road at certain intervals, and acquiring vehicle track data detected by the millimeter-wave radars; and according to the vehicle type similarity, the distance similarity, the speed similarity and the behavior similarity, vehicle tracks of the two radar monitoring blind areas and the overlapping area are matched, the matching degree of the two target vehicle tracks is judged, and finally, tracks between frame windows are spliced according to the matching degree to obtain a final target track. The method solves the problem of discontinuous cross-device multi-target trajectory tracking, solves the problem of poor splicing effect of a trajectory splicing algorithm based on a linear prediction model, and has the advantages of being independent of moving target feature information, high in precision, good in adaptability and the like.

The invention provides an unmanned aerial vehicle radio frequency signal identification method and a related device, and relates to the technical field of deep learning. Signal data are loaded from an unmanned aerial vehicle radio frequency signal data set, and the data set comprises different unmanned aerial vehicle types, detection distances and signal fragment labels; dividing the signal data into a plurality of sections of continuous sampling point sequences which are not overlapped with one another; adding Gaussian additive white noise with different powers into the signal data, and constructing a training data set comprising a plurality of signal-to-noise ratios; performing short-time Fourier transform on the signal data to generate a time-frequency graph; performing principal component analysis and dimension reduction on the time-frequency graph, and extracting key time-frequency features; performing hyper-parameter optimization on the deep neural network model by adopting a k-fold cross validation strategy; and the optimized deep neural network model is used to classify and identify the time-frequency graph. The technical effect of accurately identifying the radio frequency signal of the unmanned aerial vehicle under different signal-to-noise ratio conditions is realized.