2094 results about "Vehicle identification" patented technology

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 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.

The invention provides a vehicle fault remote diagnosis and repair method and device and a storage medium. The method comprises the steps of receiving vehicle data of a target vehicle within a preset time period; determining a first fault code corresponding to the fault data; performing fault diagnosis on the first fault code and the operation data through a remote diagnosis module; processing the fault diagnosis information through a fault repair module according to the vehicle identifier to obtain a software repair package of the target vehicle; detecting whether the vehicle-mounted client carries out fault repair or not; after the software repair package is sent to the vehicle-mounted client and fault repair is carried out to obtain a fault repair result, a fault detection instruction is sent to the vehicle-mounted client to carry out fault diagnosis, and a first fault diagnosis result is obtained; determining whether the first fault repair result is completely repaired according to the first fault diagnosis result; if the first fault is not completely repaired, repairing the target vehicle according to the first fault through the remote assistance module; otherwise, it is confirmed that fault repair of the target vehicle is completed.

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 a pedestrian and vehicle detection method based on an improved YOLOv10n. The pedestrian and vehicle detection method comprises the following steps: on the basis of an original model YOLOv10n, replacing a first layer and a third layer in Backbone and a seventeenth layer Conv in Neck with a space-to-depth convolution SPDConv; a focused spatial pyramid pooling module ASPPF is constructed, and semantic fusion of cross-level features is optimized; designing a global grouping coordinate attention module GGCA, fusing the GGCA into a Neck layer, generating attention maps by using global information of the feature maps in the spatial dimension, and weighting the input feature maps through the attention maps to enhance the feature expression ability; a WIou loss function is introduced, the bounding box loss of the network is optimized, and the convergence speed and regression precision of the model are improved. And training a model by using a data set which is made by autonomous labeling to obtain a training result, and completing a pedestrian and vehicle detection task by using an optimal training weight. According to the method, a good detection effect is achieved with a small number of model parameters, and the pedestrian and vehicle recognition accuracy is well improved.

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 relates to the field of unmanned aerial vehicle identification and tracking, and discloses an unmanned aerial vehicle tracking method, tracking device and system and a storage medium, and the method comprises the steps: searching an unmanned aerial vehicle through an unmanned aerial vehicle tracking device, and employing a deep neural network visual template detection mode when the unmanned aerial vehicle is not searched and is in an unmanned aerial vehicle searching state; searching a suspicious target in the view field through a deep neural network; wherein the unmanned aerial vehicle is used as a search target; after the target is searched, adopting an identification result to initialize a machine vision detection mode so as to track the target through machine vision; when a target is lost, a tracking result is inaccurate, a tracked target moves too fast or background interference is serious in the tracking process, the mode is switched to a mixed mode for mutual cooperation so as to realize accurate tracking of the target. The method can achieve the recognition and tracking of the unmanned plane under the edge calculation, is precise in recognition, is high in tracking precision, and is small in loss rate.

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 belongs to the technical field of hazardous chemical substance vehicle identification, and discloses a hazardous chemical substance vehicle identification method considering multi-modal time sequence information, and the method comprises the following steps: S1, data collection; s2, data preprocessing; s3, feature extraction; s4, modeling a time sequence; s5, multi-modal information fusion is carried out; s6, classifying and identifying; s7, performing real-time feedback and early warning; and S8, updating and optimizing the model. According to the dangerous chemical vehicle identification method considering the multi-modal time sequence information, multi-modal data can be effectively integrated, and the accuracy and real-time performance of dangerous chemical vehicle identification are improved; the false alarm rate and the missing report rate can be reduced, and reliable decision support is provided for a traffic management department; the method can adapt to constantly changing traffic conditions, and high accuracy is kept through real-time updating and optimization of the model; the efficiency and effect of traffic safety management can be remarkably improved, and risks and potential hazards in the dangerous chemical transportation process are reduced.

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 provides an intelligent vehicle type identification and dynamic regulation and control loading system, and compared with the prior art, the intelligent vehicle type identification and dynamic regulation and control loading system comprises an arrangement module, an identification module, a loading control module, a dynamic regulation and control module and a real-time monitoring platform. The arrangement module intelligently dispatches and matches vehicles according to transportation requirements; the identification module accurately identifies vehicle information through an image identification algorithm; the loading control module controls the loading device to automatically complete material loading through a PLC. The dynamic regulation and control module adjusts the loading speed according to the real-time loading state, and overload or too slow loading is avoided; and the real-time monitoring platform provides transportation task progress monitoring and abnormity alarming, so that the loading process is ensured to be carried out smoothly. According to the system, the utilization efficiency of transportation resources can be effectively improved, the transportation cost is reduced, and the automation and intelligence level of loading operation is improved. Through intelligent scheduling and dynamic regulation and control, it is ensured that the material loading process is more efficient and accurate.

The invention relates to the technical field of artificial intelligence, in particular to a vehicle target identification and detection method and system. Comprising the following steps: training an experimental data set by using a Yolo v5 target detection algorithm, and introducing a BIFPN bidirectional feature pyramid network and a CBAM-nSAM attention mechanism based on CBAM improvement; a GSConv structure based on depth separable convolution is introduced into a Yolo v5 target detection algorithm, convolution of a Neck part in the Yolo v5 network is replaced, and an EIoU loss function is used to replace a CIoU loss function in the Yolo v5 network; and training the improved Yolo v5 target detection algorithm by using the training set and the verification set to obtain a trained model and a trained weight, testing the trained model and the trained weight by using the test set, and further evaluating a result. According to the invention, based on the improvement of the BIFPN bidirectional feature fusion pyramid network and the CBAM-nSAM attention mechanism, a more accurate and more efficient vehicle identification method can be realized by using the existing hardware basis, and rapid and accurate vehicle identification statistics can be realized.

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.

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 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 an interactive vehicle trajectory prediction and driving intention recognition method based on multi-task learning. The method comprises the following steps: processing vehicle track data set data, and determining target data corresponding to a target vehicle and surrounding traffic participants according to vehicle identifiers contained in the data; a multi-task learning method is invented, a CTG-MTLN model based on multi-task learning is established by adopting processed data so as to realize functions of interactive vehicle track prediction and driving intention recognition, and the model has the characteristics of high precision and high efficiency.

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 provides an unmanned aerial vehicle identification method fusing heading, micro-motion spectrum and transient signal features. The method comprises the following steps: extracting unmanned aerial vehicle target heading features; unmanned aerial vehicle target micro-motion spectrum feature extraction; extracting transient features of a target communication signal of the unmanned aerial vehicle; constructing a fusion feature vector; and classifying and identifying the target. According to the method, the heading features, the micro-motion spectrum features and the transient signal features of the light and small unmanned aerial vehicle are extracted, the unmanned aerial vehicle target is classified and identified by using the random forest classification method through feature normalization fusion, and the robustness and accuracy of unmanned aerial vehicle identification are improved.

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.