1469 results about "Vehicle identification" patented technology

The invention provides an unmanned aerial vehicle identification early warning method and system. The method comprises the following steps: acquiring RGB image data, thermal radiation data and spectral data of an unmanned aerial vehicle no-fly zone through a visible light camera, an infrared thermal imager and a multispectral imager; performing transmission preprocessing on the RGB image data, the thermal radiation data and the spectral data, and adaptively adjusting multi-level fusion of a fusion weight based on real-time environmental parameters to generate target fusion data; based on a deep learning model and a tracking prediction algorithm, performing unmanned aerial vehicle identification early warning on the target fusion data, and generating early warning data; and transmitting the target fusion data and the corresponding abnormal event log to a cloud server, and updating the deep learning model by adopting the target fusion data and the abnormal event log. Through cooperative work of a multi-mode sensor, visible light, infrared, multispectral and other wave bands are covered, all-weather and full-scene unmanned aerial vehicle detection is achieved, the fusion weight is adjusted in real time based on real-time environment parameters, and the accuracy of the recognition result under the complex air situation is ensured.

Systems and methods for automated unmanned aerial vehicle recognition. A multiplicity of receivers captures RF data and transmits the RF data to at least one node device. The at least one node device comprises a signal processing engine, a detection engine, a classification engine, and a direction finding engine. The at least one node device is configured with an artificial intelligence algorithm. The detection engine and classification engine are trained to detect and classify signals from unmanned vehicles and their controllers based on processed data from the signal processing engine. The direction finding engine is operable to provide lines of bearing for detected unmanned vehicles.

The invention relates to the technical field of intelligent traffic monitoring, in particular to a vehicle identification and traffic incident detection method and system based on the view angle of an unmanned aerial vehicle, and the method comprises the steps: obtaining the aerial traffic video data of the unmanned aerial vehicle and the flight control data of the unmanned aerial vehicle; performing target identification on the image in the video data by adopting a target detection algorithm to obtain vehicle information; tracking the vehicle in the video data by using a target tracking algorithm, and mapping the identified vehicle to a unified coordinate system in combination with the flight control data to generate vehicle trajectory data; constructing a comprehensive road description model based on the vehicle trajectory data and the vehicle information; and forming a traffic incident recognition module based on the comprehensive road description model, and recognizing the input aerial video according to a preset traffic incident recognition rule. According to the invention, by optimizing the vehicle identification algorithm, constructing the road description model and designing the efficient traffic event identification module, the application efficiency of the unmanned aerial vehicle aerial video in the traffic monitoring field is effectively improved.

The invention belongs to the technical field of automatic driving vehicle identification, and discloses an automatic driving vehicle point cloud identification method and system based on a laser radar. According to the method, an automatic driving vehicle point cloud recognition model is built, modular dynamic edge convolution based on a feature sensitivity layer number selection strategy is provided in the model, local geometric information is better captured by dynamically learning a local geometric structure, and the extraction capability of geometric feature information is enhanced. In order to prevent the problem of insufficient information extraction caused by layer number simplification of modular dynamic edge convolution, a selective kernel attention mechanism is introduced, a feature fusion mode is adjusted, residual connection is added, more comprehensive statistical information is captured while original information is reserved, and the multi-scale feature capture capability is improved, so that the classification performance of the model is improved. Especially in the face of noise, sparse data and complex object shapes, high classification accuracy can still be kept, and a more accurate environment perception capability is provided for an automatic driving system.

An independent cart system includes multiple track segments, multiple movers, multiple position sensors, and a controller. Each track segment includes multiple coils along a length of the track segment and a segment controller operative to selectively energize the coils to generate an electromagnetic field. Each mover includes a magnet array having multiple magnets, where the magnet array generates a magnetic field that interacts with the electromagnetic field generated by the coils to propel the mover along the track segments. The position sensors are spaced apart along the length of each track segment and generate a position feedback signal with a waveform as a function of the magnetic field generated by the magnet array on each mover. The controller is operative to receive the position feedback signal from each position sensor and to determine a unique identifier for each mover as a function of the waveform of the position feedback signal.

The invention discloses an intelligent parking space guiding system for a hospital parking lot, relates to the technical field of intelligent traffic, and is used for solving the problems that the hospital parking lot is seriously jammed in peak hours and special vehicles lack exclusive passing authority. The system comprises a parking space monitoring module, a vehicle identification module, an intelligent guiding module and a user interaction module. The parking space state is monitored in real time through the multi-mode sensor array; recognizing entering and exiting vehicles through a deep learning recognition algorithm based on cameras deployed at entrances and exits, and obtaining corresponding vehicle information; based on parking space state information and vehicle identification information, a route optimal value is dynamically calculated, and a whole-course navigation service from an entrance to a target parking space is provided for a driver, so that accurate detection of the parking space state, special vehicle priority distribution, high-low peak dynamic route planning and whole-process intelligent guidance are realized, the hospital parking efficiency is remarkably improved, and the hospital parking experience is improved. And the congestion condition in the peak period is relieved.

Systems and methods for automated unmanned aerial vehicle recognition. A multiplicity of receivers captures RF data and transmits the RF data to at least one node device. The at least one node device comprises a signal processing engine, a detection engine, a classification engine, and a direction finding engine. The at least one node device is configured with an artificial intelligence algorithm. The detection engine and classification engine are trained to detect and classify signals from unmanned vehicles and their controllers based on processed data from the signal processing engine. The direction finding engine is operable to provide lines of bearing for detected unmanned vehicles.

The invention relates to the technical field of unmanned aerial vehicle monitoring, and discloses an unmanned aerial vehicle locking monitoring method and system based on a cloud control platform, and the method comprises the following steps: collecting feature data of an unmanned aerial vehicle, and transmitting the feature data to the cloud control platform; the cloud control platform preprocesses the feature data; based on the preprocessed feature data, behavior feature parameters of the unmanned aerial vehicle are extracted; performing abnormal behavior detection on the unmanned aerial vehicle based on the behavior characteristic parameters; acquiring environment data, and performing behavior pattern matching on the unmanned aerial vehicle with the abnormal behavior; illegal unmanned aerial vehicle response and management measures are taken for the unmanned aerial vehicles with illegal matching results; according to the method, the illegal unmanned aerial vehicle identification efficiency and effect are remarkably improved, a systematic and intelligent solution is provided for the unmanned aerial vehicle supervision field, and the method has an important value for promoting standardized development of the unmanned aerial vehicle industry.

The invention relates to the technical field of vehicle image recognition, and discloses a multi-modal data enhanced vehicle recognition method and system based on a generative adversarial network, and the method comprises the steps: collecting vehicle multi-modal data (a visible light image, a thermal infrared image and three-dimensional laser point cloud data), and constructing a vehicle multi-modal data set; performing preprocessing and feature alignment on the data set to obtain standardized multi-modal data; constructing a cross-modal generator based on an adaptive attention mechanism, learning inter-modal feature association through a dynamic weight distribution module, and generating vehicle feature fusion data; designing a dual discriminator structure consisting of a perception consistency discriminator and a semantic fidelity discriminator, and optimizing the generator by adopting an alternate adversarial training strategy; generating a supplementary data sample for the complex scene by using the optimized generator, and constructing an enhanced data set; and constructing a multi-mode cooperative vehicle identification model based on the enhanced data set, and realizing high-precision vehicle attribute identification.

The invention discloses an unmanned aerial vehicle monitoring system which is designed on an embedded software architecture layer, adopts a hierarchical data processing architecture and comprises a peripheral driving layer, a protocol analysis layer, a data management layer, a tracking and capturing layer and an information reporting layer so as to ensure high efficiency and expandability of the system. After unmanned aerial vehicle identification, the system has an unmanned aerial vehicle capturing and tracking function, and combines GNSS positioning information and a dynamic trajectory prediction algorithm to calculate the real-time position and flight trajectory of the unmanned aerial vehicle, so that effective monitoring of the unmanned aerial vehicle is realized, and the real-time safety of a low-altitude airspace is ensured.

The invention provides a vehicle enterprise APP intelligent assistant navigation method and system based on a large model. The method relates to collection of user navigation requirements and identification of key entities and user intentions by using a large language model. And when the navigation intention is identified, generating a navigation route through map data and a path planning algorithm in combination with the current position and the target position. And when the vehicle control intention is identified, remotely controlling the vehicle through a vehicle control interface. And when the intention is identified as the information query intention, relevant information is retrieved and displayed from the vehicle information base. And collecting user feedback, an optimization model and a navigation strategy. The navigation demand information comprises text and voice information. The navigation requirement understanding of the user is improved through the large language model, tedious operation is avoided, and the access efficiency and the user experience are improved. Personalized and real-time recommendation is realized, real-time information is provided according to requirements and environments, and travel route planning is optimized. Dialogue management supports multiple rounds of dialogues and deep interaction, complex requirements are effectively processed, and the ability to deal with complex scenes is improved.

The invention belongs to the field of computer vision, and particularly relates to a high-robustness vehicle identification method based on target area constraint, which comprises the following steps of: acquiring original vehicle image data, and obtaining a training image data set with a vehicle target area mask; constructing an improved convolutional neural network as a classification backbone network, and inputting the training image data set into the network for feature extraction to obtain a feature map; constructing an attention region constraint module, and forming attention region constraint loss by calculating Dice loss between the target vehicle mask and the attention thermodynamic diagram; the feature map is input to a target guide segmentation module, a segmentation prediction map of the target vehicle is generated, and segmentation loss is calculated; and updating parameters of the improved convolutional neural network by using a total loss function obtained by combining constraint loss and segmentation loss, thereby realizing high-robustness vehicle identification. According to the method, the robustness and accuracy of vehicle identification under the foreground vehicle shielding condition are effectively improved while high-efficiency calculation of the model is kept.

A method for creating high-resolution environment maps for a vehicle having an autonomous driving function. The method includes: providing environment image data of a recognition system of the vehicle, wherein the recognition system includes an environmental sensor for detecting a vehicle environment while the vehicle is traveling; providing domain knowledge of the vehicle environment in the form of a trained knowledge graph; and creating the high-resolution environment maps of the vehicle environment by supplementing the environment image data by means of the provided domain knowledge of the trained knowledge graph.

The invention relates to the field of unmanned aerial vehicle identification, and discloses an interference method for a slight unmanned aerial vehicle identification mark, which comprises the following steps of: pre-embedding a secondary disturbance primitive at the periphery of the identification mark by constructing a pseudo-structure surface compatible with a multi-mode reflection level; performing pseudo-static layer fusion on a region with abnormal view angle focusing offset in the disturbance sensitivity map, and introducing an adjustable gray scale transmission domain mask and a multi-frequency interference texture in the fusion process to form a disturbance reconstruction domain which is subjected to boundary folding with the edge contour of the original mark; on the basis of the disturbance reconstruction domain, pseudo-random disturbance iteration aligned with the flight path prediction model is executed; mapping the interference identification probability reverse offset curved surface into the visible light disturbance superposition graph; and performing time sequence flexible interference control on the target area based on the light interference stability enhancement unit, and performing dynamic reconstruction on the pseudo-primitive layout density, the shading fine tuning node and the polarization interference structure. The method has the advantage that stable slight interference on the identification mark under the condition of a specific visual angle is improved.

An unmanned aerial vehicle (UAV) identification method based on blind source separation and deep learning is disclosed, in the method, firstly, the one-dimensional radar cross section millimeter wave data set of the UAV is acquired, and the mixed signal is obtained by mixing, and the improved FastICA algorithm is used to separate it. Secondly, the separated signal is converted into a two-dimensional image by data transformation, and the two-dimensional image is augmented, the obtained data set is divided into training set, validation set and test set. Thirdly, establish a UAV classification model based on Improved ResNet18 and train this model on the training set to achieve UAV classification. In the present invention, the training time of the network is not greatly increased while the network identification accuracy is improved, so that it can well complete the UAV type identification, and the design is more reasonable and effective.

The invention relates to the technical field of signal processing, in particular to an unmanned aerial vehicle identification method and system based on a random forest, and the method comprises the steps: transmitting a radar signal, receiving an echo signal reflected by an unmanned aerial vehicle, carrying out the signal processing and target detection of the echo signal, and extracting a target trace point set; performing track association and filtering processing based on the target trace point set to generate a target track set; based on a target track state in a preset time window, extracting a multi-dimensional motion feature vector of the target track; and inputting the feature vector into a pre-trained random forest model, and outputting an unmanned aerial vehicle identification result. The objective of the invention is to realize low-cost and high-precision unmanned aerial vehicle identification.

One variation of a preferred system for rendering content on a vehicle includes a display, a memory, a communication module, and a processor. The display is configured to mount adjacent an exterior surface of a vehicle. The memory is configured to store a plurality of executable applications including a vehicle identification application and an application elected by a user. The communication module is configured to download the user-elected application to the memory. The processor is configured to selectively execute applications to control visual content rendered on the display, wherein the display renders vehicle identification information when the vehicle identification application executes, and wherein the display renders additional visual content when the user-elected application executes.

The invention provides a multi-modal feature vector fused vehicle cross-lens ReID matching method and system, and relates to the technical field of vehicle identification, and the method comprises the steps: collecting a vehicle image through a camera, and extracting triple features, including a bottom-layer visual feature, a middle-layer semantic feature and a high-layer character feature; in combination with vehicle space-time metadata at adjacent acquisition cameras, generating space-time trajectory feature vectors, and capturing a time sequence rule of a vehicle driving path; the missing features are complemented by adopting an AI generation technology, and the complementation precision is optimized through adaptive learning; inputting the triple feature, the time sequence rule and the complementation feature into a Transform fusion network to generate a fusion feature vector; calculating the cosine similarity of the to-be-matched vehicle according to the fusion feature vector, and carrying out cross-border head matching; according to the method, the problem of insufficient distinction degree of single-modal features is solved, the track feature vector is generated by combining the space-time metadata of the camera, the driving time sequence rule of the vehicle between different lenses is captured, and the space-time constraint of cross-scene matching is enhanced.

The invention discloses a control method and device of a vehicle cloud cooperative system and a vehicle, and relates to the technical field of vehicles. The method comprises the steps that a vehicle twinning request is acquired, and the vehicle twinning request carries a vehicle identifier and operation state data of a vehicle; based on the vehicle twinning request, virtual twinning and a channel key are generated, the virtual twinning is a virtual mirror image matched with the running state of the vehicle, and the virtual twinning is used for simulating the running state of the vehicle and predicting future behaviors of the vehicle; establishing a communication channel based on the channel key, and transmitting the current operation state data and the target demand of the vehicle based on the communication channel; analyzing the current operation state data and the target demand based on the virtual twinning to obtain an analysis result; and determining a target decision based on the analysis result, and controlling vehicle driving based on the target decision. According to the invention, the technical problems of unstable network connection, delayed state synchronization and limitation of an intelligent decision-making mechanism of a vehicle cloud cooperative system in the related technology are solved.

The invention relates to the technical field of traffic monitoring, in particular to a multivariate information fusion speed measurement method and system, and the method comprises the following steps: installing an image collection device and a three-dimensional laser radar, obtaining an image of a monitoring area through the image collection device, and obtaining the point cloud data of the monitoring area through the three-dimensional laser radar; constructing a vehicle intelligent identification model, and identifying a vehicle in the monitoring area image by using the vehicle intelligent identification model to obtain a first vehicle identification result; preprocessing the monitoring area point cloud data, and identifying vehicles in the monitoring area point cloud data based on point cloud segmentation and point cloud clustering to obtain a second vehicle identification result; and determining the vehicle position according to the first vehicle identification result and the second vehicle identification result, and further obtaining the vehicle speed by tracking and monitoring the vehicle. According to the multi-information fusion speed measurement method provided by the invention, the speed measurement distance and the speed measurement precision in a complex environment can be improved, and optimization of traffic flow management and improvement of road traffic efficiency are facilitated.

The invention provides a new energy vehicle parking charging scheduling management method and system, and relates to the technical field of new energy charging management, and the method comprises the steps: receiving a charging reservation request sent by a new energy vehicle, including a vehicle identification code, a battery remaining capacity and an expected charging time period, obtaining a vehicle charging characteristic parameter, and combining with environment temperature correction; and the actual required charging time is accurately calculated. And on the basis of the charging duration, the expected charging time period and the charging pile operation state, a dynamic scoring method is adopted, a comprehensive score is calculated through duration matching score and reliability score weighting, and an optimal charging pile is selected for distribution according to a scoring result. According to the method, a weighted dynamic scoring method of the duration matching score and the reliability score is adopted, the user demand, the operation state of the charging pile and the environmental influence are comprehensively considered, the optimal distribution of the charging pile resources is ensured, and the overall operation efficiency and the service quality of the charging station are remarkably improved.

The invention discloses a highway hazardous chemical substance liquid leakage accident monitoring method and a highway hazardous chemical substance liquid leakage accident monitoring system based on computer vision. The method comprises the following steps: arranging and installing an identifier configured for hazardous chemical substance vehicle identification on a vehicle for loading and transporting hazardous chemical substances; the identifier comprises an optical identifier configured to be used for improving the visual identification degree of the dangerous goods vehicle and a digital identifier configured to realize encrypted storage and quick reading of vehicle information; on the basis of an existing road monitoring system, a lightweight identification model configured for detecting the states of an optical identifier and a digital identifier in real time is deployed and loaded in the existing road monitoring system; performing visual positioning on the road surface projection pattern through a camera, and calculating the real-time position of the vehicle by combining map coordinates so as to supplement the positioning blind area of the GPS in the tunnel mountain area; accident probability calculation is carried out based on the identification state to determine whether the vehicle is suspected to have an accident; and when the accident is judged to be suspected, triggering an accident response process. And the accident response efficiency is obviously improved.

The invention provides a vehicle target automatic labeling method and system based on deep learning. The method comprises the steps of obtaining video stream monitoring data collected by a road camera; based on the video stream monitoring data, utilizing a pre-trained vehicle identification model to automatically label a vehicle target in the video stream monitoring data to obtain a preliminary labeling result; performing uncertainty evaluation on the preliminary labeling result through an adversarial sample generation strategy to obtain an uncertainty score; when the uncertainty score does not exceed a preset threshold value, taking the preliminary labeling result as a final labeling result of the vehicle target; according to the invention, by using the pre-trained convolutional neural network model, large-scale video stream data can be quickly processed and preliminary annotation can be completed, so that the dependence on manual annotation is reduced; uncertainty evaluation is carried out on the preliminary labeling result through an adversarial sample generation strategy, errors or low-confidence-coefficient areas possibly existing in the labeling result can be effectively recognized, and the accuracy of the final labeling result can be guaranteed.

The invention provides a vehicle fault diagnosis method, apparatus and device, and a readable storage medium. The method comprises the steps of obtaining fault description information and a vehicle identification identifier input by a user; the fault description information is used for representing a vehicle fault condition; in a preset diagnosis database, determining a target fault code corresponding to the fault description information, and determining a plurality of fault codes associated with the vehicle identification identifier; the diagnosis database stores a corresponding relation between the fault description information and the fault codes; and under the condition that the target fault code accords with the multiple fault codes corresponding to the vehicle identification identifier, generating a fault diagnosis result according to the target fault code so as to complete vehicle fault diagnosis. The vehicle fault diagnosis efficiency can be improved.

The invention provides a vehicle energy management method and system and a vehicle, and belongs to the technical field of vehicles, and the method comprises the steps: transmitting vehicle identification information to a cloud after the vehicle is powered on, enabling the cloud to obtain the historical driving data of the vehicle based on the vehicle identification information to obtain driving route data, and training a preset first energy management model, target model parameters are obtained, and then a target energy management model is obtained; and determining a target driving route from the plurality of historical driving routes through the application core based on the route data of the plurality of historical driving routes and the current working condition data of the vehicle, and obtaining control parameters for controlling the vehicle to drive on the target driving route based on the target energy management model and the feature data corresponding to the target driving route, and the control parameters are sent to the execution core so that the execution assembly can control the vehicle based on the control parameters. Through the method provided by the invention, the inherent defects of a traditional fixed strategy and a single deployment architecture can be overcome.

A vehicle traveling information analysis device comprises: a reception unit that receives, from a plurality of vehicles, traveling information which includes location information and time information; a traveling information recording unit that stores, in a traveling information storage unit, the traveling information received from the reception unit together with identification information of vehicles; an analysis condition input unit that accepts, from a user, designation of an analysis condition which includes information pertaining to an analysis target road; an extraction region setting unit that sets extraction regions each having a predetermined area to a respective plurality of locations on a road map on the basis of the designated analysis condition; and a vehicle identification information extraction unit that extracts identification information of a vehicle having a record of traveling information in all the extraction regions which have been respectively set to the plurality of locations on the road map.

The invention discloses a vehicle diagnosis method, device and system, and belongs to the technical field of vehicles. The method comprises the steps of obtaining a vehicle identification code of a reference vehicle; according to the first information, second information is generated, and the second information comprises the first information and a first signature value; sending second information to a signature server, and verifying the first signature value by the signature server based on the first information; receiving third information returned by the signature server under the condition that the verification of the first signature value is passed, wherein the third information comprises fourth information and a second signature value; third information is sent to the reference controller of the reference vehicle, the reference controller of the reference vehicle verifies the second signature value based on the fourth information, when the verification of the second signature value is passed, the time corresponding to the reference second number is determined, and when the current time is earlier than the time corresponding to the reference second number, the second signature value is determined. And controlling the access mode of the on-board diagnostic system of the reference vehicle to be a non-secure access mode. The method improves the flexibility and success rate of vehicle diagnosis.

Recent location and control information received from “lead” vehicles that traveled over a segment of land, sea, or air is captured to inform, via aggregated data, subsequent “trailing” vehicles that travel over that same segment of land, sea, or air. The aggregated data may provide the trailing vehicles with annotated road information that identifies obstacles. In some embodiments, at least some sensor control data may be provided to the subsequent vehicles to assist those vehicles in identifying the obstacles and / or performing other tasks. Besides, obstacles, the location and control information may enable determining areas traveled by vehicles that are not included in conventional maps, as well as vehicle actions associated with particular locations, such as places where vehicles park or make other maneuvers.

Systems and methods for automated unmanned aerial vehicle recognition. A multiplicity of receivers captures RF data and transmits the RF data to at least one node device. The at least one node device comprises a signal processing engine, a detection engine, a classification engine, and a direction finding engine. The at least one node device is configured with an artificial intelligence algorithm. The detection engine and classification engine are trained to detect and classify signals from unmanned vehicles and their controllers based on processed data from the signal processing engine. The direction finding engine is operable to provide lines of bearing for detected unmanned vehicles.

The invention discloses an unmanned aerial vehicle identification and tracking system, which can integrate multi-source visual information by calling to-be-identified images shot by a plurality of camera units, thereby determining accurate position information of a target unmanned aerial vehicle. The plurality of camera units can be monitoring camera networks which are arranged all over, the camera networks are arranged and execute tasks such as public security monitoring at ordinary times, and unmanned aerial vehicle identification and tracking can be executed, so that the hardware use cost is reduced, and the camera units can be quickly spread and implemented under the existing hardware condition; the visual field range of the camera shooting unit is matched with the low-altitude flight height of the target unmanned aerial vehicle, the blind area of the flyer detection radar can be effectively covered, and the target unmanned aerial vehicle can still be effectively recognized and tracked under the condition that the target unmanned aerial vehicle flies at a low altitude and the flyer detection radar cannot effectively detect. Supervision of the unmanned aerial vehicle is improved, and use safety of the unmanned aerial vehicle is guaranteed. The method is widely applied to the technical field of unmanned aerial vehicle management.