103 results about "Vehicle category" patented technology

A system for forecasting energy consumption by vehicles in a mixed-vehicle fleet. The system includes neural network(s) that generate a predictive function for vehicles in each of a number of classes (e.g., electric vehicles, hybrid vehicles, internal combustion vehicles, vehicle models, model years, etc.). To capture both the generalizable patterns that govern energy consumption across all vehicle classes and the features and relationships that are specific to each class, the neural network(s) include a multi-task learning model that includes shared layers for all of the classes of vehicles and a set of vehicle-specific layers for each class. In some of those embodiments, for example to predict to energy consumption of an additional class with limited data, the neural networks further include an inductive transfer learning model that includes the shared layers transferred from the multi-task learning model and vehicle-specific layers for the additional class.

The invention discloses a Voxel RCNN Complex HD-based road target detection model and detection method, and proposes an improved three-dimensional feature extraction network and a two-dimensional feature extraction network for the problem that the Voxel R-CNN algorithm is insufficient in point cloud feature extraction. In order to solve the problem of low pedestrian detection precision, a mixed detection head with an anchor frame and a mixed detection head without an anchor frame are provided, and the detection precision of a large target and a small target is improved by using the mixed detection head based on an anchor and a mixed detection head without an anchor. The voxel candidate box self-attention pool module is provided for solving the problem that the difference between the detection precision of buses and trucks and the detection precision of trolleys in vehicle categories is large, local and global features of an object are better obtained, and therefore the size, the position and the course angle of a large target can be more accurately refined. And finally, the problem of insufficient detection accuracy of pedestrians, buses and trucks in a complex traffic scene is solved.

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 provides a vehicle detection method fusing adaptive attention in a congestion scene, and relates to the technical field of vehicle detection. For the problems of low vehicle detection precision and high omission ratio in a traffic jam scene, a YOLOv5 network model is constructed, an attention module is constructed based on a CBAM attention module and an ECA attention module, and the attention module is introduced into a backbone network of the YOLOv5 network model; based on the Wise-IoUv3 loss function and the Focal loss function, constructing a joint loss function, and replacing the loss function of the YOLOv5 network model with the joint loss function; respectively embedding ConvMixer hybrid convolution modules into three prediction branches of a head network of the YOLOv5 network model; the method comprises the following steps: detecting congestion vehicles at a monitoring view angle by using a trained model weight, obtaining vehicle category information, enhancing vehicle key feature extraction capability and multi-scale feature expression capability, optimizing bounding box positioning precision through a dynamic weighting mechanism, and promoting attention to a difficult-to-detect target.

The invention relates to an intelligent traffic signal control method and system based on traffic flow prediction, and belongs to the technical field of intelligent traffic. Traffic vehicle video streams and traffic flow data are collected through a high-definition camera and a traffic flow sensor group, different vehicles and speeds, directions and movement tracks thereof are identified through the traffic vehicle video streams, special vehicles are judged, traffic violation behaviors and road abnormal conditions are monitored according to the traffic vehicle video streams and the traffic flow data, and the traffic violation behaviors and the road abnormal conditions are monitored. A traffic prediction model is established according to traffic vehicle video streams and traffic flow historical record data, and traffic flow, vehicle positions, vehicle categories, lane line information, vehicle movement tracks, traffic violation, road abnormal conditions, and traffic flow values and vehicle speeds in future time periods of all lanes at all intersections are obtained from a server in real time. And different signal lamp control schemes are generated according to different vehicles. According to the invention, a signal control scheme is designed in combination with vehicle types, traffic violation, road abnormity and other conditions, and accurate traffic congestion dispersion is realized.

Detecting compatibility mismatch by generative artificial intelligence is described. Compatibility data is obtained (e.g., by accessing a database). The compatibility data is associated with a compatibility between items (e.g., items and categories of vehicles or an item and another item) and includes a list of recommended compatibilities between the items and a user reported compatibility for at least one item. A machine learning model is generated for detecting a compatibility mismatch between a first item and a second item and / or between an item and a category of vehicle. At least a portion of the compatibility data is provided as input to generative artificial intelligence to generate the machine learning model. An update to the list of recommended compatibilities is determined based on the detected compatibility mismatch.

Detecting compatibility mismatches through generative artificial intelligence is described. Compatibility data is obtained (e.g., by accessing a database). The compatibility data is associated with compatibility between items (e.g., an item with a vehicle category, or an item with another item), and includes a recommended compatibility list between items and user reported compatibility for at least one item. A machine learning model is generated for detecting compatibility mismatches between the first item and the second item and / or between the item and the vehicle category. At least a portion of the compatibility data is provided as input to generative artificial intelligence to generate a machine learning model. An update to the recommended compatibility list is determined based on the detected compatibility mismatch.

The application relates to the field of warehouse management, in particular to a logistics warehouse management system based on big data, which comprises a division unit, a analysis unit and a control unit. The division unit is used for determining effective task points according to task update frequency, determining regional division modes according to the number and distribution coefficient of the effective task points, and determining the vehicle category of the carrying equipment according to the running efficiency and loading proportion. The analysis unit is used for determining analysis processing modes according to the vehicle matching degree of the carrying area. The control unit is used for determining the priority coefficient of each adjustable vehicle, determining the response vehicle according to the priority coefficient, and determining the moving route of the response vehicle according to the radiation demand degree of the movable route. The scheduling unit is used for determining the area state according to the task dispersion degree of the two types of carrying areas and the number of the response vehicles during vehicle scheduling analysis, determining the scheduling mode according to the area state, and determining the vehicle scheduling range according to the task fluctuation coefficient and the obstacle complexity. The logistics warehouse management efficiency is improved.

The invention relates to a vehicle service package management method and device, computer equipment and a storage medium. The method comprises the following steps: acquiring vehicle historical data of a target vehicle category, and performing time change trend analysis on the vehicle historical data to obtain health state parameters of each vehicle part of the target vehicle category; inputting the health state parameter of each vehicle part of the target vehicle category into a preset fault analysis model for data processing to obtain a fault probability of each vehicle part of the target vehicle category, and allocating a corresponding detection weight to each vehicle part based on the fault probability of each vehicle part; and generating detection task configuration information of the target vehicle category based on the detection weight corresponding to each vehicle component, and taking the detection task configuration information as a vehicle service package of the target vehicle category. By adopting the method, the detection efficiency and the detection accuracy of the vehicle part can be improved.

A vehicle management system manages a vehicle in a predetermined area. The vehicle management system has a machine learning model for estimating a position of a vehicle shown in an image. The vehicle management system acquires a target-specialized parameter from a parameter providing apparatus, the target-specialized parameter being a parameter of the machine learning model trained with a focus on a category of a target vehicle. The vehicle management system apples the target-specialized parameter to the machine learning model to acquire a target-specialized machine learning model specialized in the category of the target vehicle. The vehicle management system acquires an image captured by a camera installed in the predetermined area and showing the target vehicle. The vehicle management system estimates a position of the target vehicle based on the image and the target-specialized machine learning model.

The invention provides a multi-modal fusion unmanned aerial vehicle identification method and system, and the method comprises the steps: obtaining a radio signal, an image signal and a sound signal of an unmanned aerial vehicle; performing feature extraction on the radio signal based on a blind source separation algorithm and adaptive filtering to obtain radio features; performing feature extraction on the image signal based on image super-resolution reconstruction and a double-flow feature extraction network to obtain image features; performing feature extraction on the sound signal based on spectral analysis and harmonic feature extraction to obtain an audio feature; fusing the radio feature, the image feature and the audio feature to form a multi-modal fusion feature; and performing identification based on the multi-modal fusion features to obtain an unmanned aerial vehicle category identification result. By adopting the scheme, the accuracy, reliability and environmental adaptability of unmanned aerial vehicle identification can be remarkably improved.

The invention provides an unmanned aerial vehicle identification and tracking method and system based on multi-modal fusion and trajectory modeling, and relates to the technical field of target detection and tracking. Infrared / visible light images and audio signals are synchronously collected in an airport key area, visual and acoustic features are extracted through a deep network after time alignment, and the target detection and tracking accuracy is improved. Outputting respective unmanned aerial vehicle category probabilities and candidate target frames; afterwards, weights are distributed adaptively according to confidence coefficients of audio and visual classification results, category fusion is carried out on a probability level, and unified multi-modal fusion representation is constructed on a feature level, so that relatively high recognition reliability is still kept in a small-target, weak-texture and strong-noise scene; on the basis, the multi-modal fusion feature sequence serves as input, the position and speed of the unmanned aerial vehicle in each time step are constructed into a trajectory state sequence, a sequence generation model outputs a complete trajectory in an autoregressive mode under conditional constraints, and continuous tracking, occlusion interval complementation and future trend prediction of the motion of the unmanned aerial vehicle are achieved.

The invention discloses an unmanned aerial vehicle classification method and system based on a noise perception model, and the method comprises the following steps: obtaining and processing the audio data of an unmanned aerial vehicle, obtaining the energy of the audio data, processing the energy of the audio data, obtaining an enhanced signal, optimizing the enhanced signal, and obtaining the high and low frequency bands of the enhanced signal. Generating a mixed resolution spectrum based on the high and low frequency bands; and processing the high and low frequency bands through a differential Mel filter bank to obtain a frequency band boundary, generating a Mel spectrogram based on the frequency band boundary and the mixed resolution spectrum, inputting the Mel spectrogram into a convolutional neural network, and outputting the probability of the unmanned aerial vehicle category. According to the method, noise quantification, noise reduction processing and feature enhancement are performed on the unmanned aerial vehicle audio data through the noise perception model, and high-precision unmanned aerial vehicle classification is realized in combination with the convolutional neural network.

A computer architecture for interactive image based database searching includes a first server that acquires category information indicating a category of a vehicle owned by a user. The first server also acquires part information for identifying a vehicle part located at a position specified by a user operation within an image of a vehicle displayed on a terminal device of the user. A second server is in communication with the first server and searches for products by accessing a storage storing thereon product information on vehicle part products and by using the acquired category information and the acquired part information as search criteria. The system transmits search result information indicating results of the search to the terminal device.

The invention discloses a global perception method and system for vehicle detection. The method comprises the following steps: acquiring a vehicle picture and dividing the vehicle picture into a plurality of feature blocks; inputting each feature block into an attention benchmark network based on a cascade mode to obtain multi-scale feature maps of multiple stages; performing cross fusion and noise suppression on the multi-scale feature maps of the multiple stages to obtain fusion feature information; and outputting a vehicle detection frame, a vehicle category and a vehicle mask through floating point calculation and bilinear interpolation based on the fused feature information and the multi-scale feature map. Rich vehicle feature information is provided by obtaining a multi-scale feature map containing a vehicle image and fusion feature information, and then vehicle detection frame positioning and vehicle classification are performed based on floating point calculation and bilinear interpolation, so that high-precision vehicle detection and classification can be performed by greatly utilizing global vehicle feature information. And the detection and classification reliability and stability are ensured.

The present invention discloses a multi-stage vehicle re-identification method based on spatiotemporal criteria and a hybrid network architecture. A new sampling strategy based on mixup of multi-view images is proposed for the re-identification dataset to enhance the robustness of the model to vehicle perspective changes. In order to address the problem that previous deep learning algorithms have difficulty distinguishing between vehicle categories with similar appearances, the spatiotemporal criteria are designed to reduce false detections and improve the accuracy of model retrieval; through the hybrid network architecture, fine-grained global information is extracted to overcome the shortcomings of the CNN network structure in losing detailed information and enhance the robustness of the model's feature extraction. While ensuring the retrieval speed, the present invention greatly improves the accuracy of the model. The model can operate stably and efficiently in actual urban traffic scenarios and has broad application prospects.

The present invention relates to the technical field of intelligent transportation, and specifically discloses a method for obtaining vehicle orientation information, including: collecting an image of a vehicle to be detected; obtaining the body bounding box, the head / tail bounding box, the wheel bounding box, the vehicle category, and the head / tail category information in the image of the vehicle to be detected; calculating a first orientation angle and its uncertainty according to the vehicle category, the head / tail category, the body bounding box, and the head / tail bounding box information; calculating a second orientation angle and its uncertainty according to the wheel bounding box information; calculating a third orientation angle and its uncertainty by weighted calculation according to the first orientation angle, the second orientation angle, and their respective uncertainties, and updating them to output the target orientation angle and its uncertainty of the vehicle to be detected. The present invention also discloses a device for obtaining vehicle orientation information. The method for obtaining vehicle orientation information provided by the present invention can more quickly and robustly determine the target vehicle orientation information.

The invention relates to a method for operating an autonomous vehicle (1), in which the surroundings of the vehicle (1) and objects located in the surroundings are detected by means of detection signals from a surroundings sensor (3). According to the invention, the critical degree of the current situation detected by means of the signal behind the vehicle (1) is classified as a function of the determined vehicle class of the following vehicle (2) following the vehicle (1) and the detected distance (a) between the vehicle (1) and the following vehicle (2).

The techniques described herein relate to joint space / time domain beam management, including spatial domain beam management over an observation window and time domain beam management over a prediction window. Incorporating spatial and temporal dependencies increases the prediction interval and makes the prediction more accurate because spatial beam management is performed over the observation interval. Interaction aware multi-user equipment (UE) beam management techniques are also described that model a plurality of user interactions in a beam management procedure to predict future trajectory data and to predict an optimal future beam and / or a future optimal subset of beams per UE. For predicted future trajectory data and / or beam prediction data, a single user category or multiple user categories (e.g., vehicle category users, pedestrian category users) may be considered.

An information acquisition unit (S410-S420) acquires API access logs each indicating the usage status of API in accordance with a request from an application installed in a vehicle. A sales-related index generation unit (S480-S490) generates a sales-related index in accordance with a log aggregation value which is a result of aggregating the API access logs for each type of API and for each vehicle classification in which vehicles are classified by vehicle sales data. The sales-related index is an API index indicating the magnitude of the influence of the API on a user of the vehicle and on the application. An output unit (S510) outputs the sales-related index.

The invention relates to a damage standard determination method and device, a damage assessment method and device, equipment and a medium. The damage standard determination comprises the following steps: determining the total sample damage amount of sample transmission parts of different sample vehicles in a test operation period; different sample vehicles belong to the same vehicle category; determining at least one damage grade interval corresponding to the sample transmission part based on the total damage amount of each sample; each damage grade interval corresponds to one damage grade; for each damage level interval, according to the numerical range of the total sample damage amount in the damage level interval, determining the standard range of the evaluation target object under the corresponding damage level; wherein the target object comprises a target transmission part of the target vehicle; the target vehicle and the sample vehicle belong to the same vehicle category; the target transmission component and the sample transmission component belong to the same component category. According to the method, an objective and accurate standard is provided for damage evaluation of the transmission part, and subjective judgment of evaluation personnel does not need to be introduced in the evaluation process.

Image acquisition system (2000) for a traffic control automated system (1001) configured to: (a) measure (3001) the trajectory (TRJ), position (POS) and speed (VIT) of at least one vehicle (1003) using a road vehicle tracking device (2003); (b) illuminate (3002) the vehicle (1003) using an infrared illumination device (2001); (c) acquire (3003), using an infrared-sensitive camera (2002), at least one infrared image (IM-IR) of the vehicle (1003) illuminated by the infrared illumination device (2001); (d) process (3004) the infrared image (IM-IR) of said vehicle (1003) using a data processing device (2006); when the vehicle's speed (VIT), vehicle position (POS), vehicle category (CAT), and / or driver conduct (COMP) meets at least one offence criterion (CR-INF): (e) illuminating (3005) the vehicle with a visible light lighting device;(f) acquire (3006) a colour image (IM-VIS) of the vehicle (1003) illuminated using a colour photographic device (2005);

Disclosed herein are methods and systems for operating automated vehicles to accomplish an agricultural mission according to a hierarchically computed mission plan, comprising receiving a plurality of mission parameters defining an agricultural related mission, computing a generic plan for executing the mission by one or more of a plurality of vehicle classes grouping automated vehicles sharing common vehicle parameters coupled with one or more of a plurality of payload classes grouping payloads sharing common payload parameters, computing a plurality of specific plans for executing the mission by one or more automated vehicles of the selected vehicle class(s) coupled with one or more payloads of the selected payload class(s), selecting one of the plurality of specific plans which reduces one or more mission attributes of the mission and outputting instructions for operating the selected automated vehicle(s) and the selected payload(s) to execute the mission according to the selected specific plan.

The invention discloses a method for detecting illegal entry of a highway truck into a passenger car lane based on a roadside camera, and mainly solves the problem that illegal vehicles cannot be efficiently and accurately detected and recorded due to dependence on manual judgment in the prior art. According to the implementation scheme, an expressway image is shot through a roadside camera, and a truck forbidding lane area is calibrated in the image; training a highway vehicle identification model, and detecting images collected by a camera by using the trained vehicle identification model to obtain a vehicle detection frame and a vehicle category; training a license plate detection model, and using the trained license plate detection model to detect images collected by a camera to obtain a license plate detection frame; judging whether the truck runs in the forbidden area or not by using the position information of the vehicle detection frame and the license plate boundary frame; and cutting out a license plate bounding box area of a truck which is detected to illegally drive in the forbidden area, inputting the license plate bounding box area into a license plate recognition model CRNN to recognize a license plate number, and recording the license plate number. The method is high in detection precision and can be used for safety management of the expressway.

System (10) for route planning of a vehicle (12) based on the road characteristics, wherein the system (10) comprises: a global navigation satellite system (GNSS) (16) for determining a geographic location of the vehicle (12); a vehicle communication system (20) for communicating with server systems (28); and a vehicle controller (14) in electrical connection with the GNSS (16) and the vehicle communication system (20), wherein the vehicle controller (14) is programmed: to determine a vehicle mass, a vehicle height and a vehicle width; to receive a navigation request from an occupant of the vehicle (12); and to determine a navigation route using at least the GNSS (16) and the vehicle communication system (20) in response to receiving the navigation request, wherein the navigation route is based at least partially on the vehicle mass, vehicle height and vehicle width, wherein, in order to determine the navigation route, the vehicle controller (14) is further programmed as follows: to determine the geographical location of the vehicle (12) using the GNSS (16); to transmit the vehicle's geographical location (12), navigation request, vehicle mass, vehicle height and vehicle width to a server controller (30); and to receive the navigation route from the server controller (30) using the server controller (30), which is programmed as follows: to determine several possible routes at least partially based on the geographical location of the vehicle (12), each of which satisfies the navigation requirement; to determine a route-drive comfort score for each of the several possible routes; and to select the navigation route from the several possible routes at least partially based on the route drive comfort score for each of the several possible routes, wherein the server controller (30) is electrically connected to a database, wherein the database contains several segment drive comfort records, wherein each of the several segment drive comfort records corresponds to one of several road segments of several lanes, and wherein each of the several segment drive comfort records has a location attribute, a vehicle class attribute and a drive comfort score attribute, wherein, in order to determine the route drive comfort score for each of the several possible routes, the server controller (30) is further programmed as follows: to determine a vehicle class of the vehicle (12) at least partially based on the vehicle mass, vehicle height and vehicle width; to retrieve a subset of the multiple segment drive comfort records from the database, wherein the location of each subset of the multiple segment drive comfort records is along one of the multiple possible routes, and wherein the vehicle class of each subset of the multiple segment drive comfort records corresponds to the vehicle class of vehicle (12); and The route drive comfort score for each of the multiple possible routes is determined by summing the drive comfort score of each subset of the multiple segment drive comfort data records along each of the multiple possible routes.

[Problem] To provide a vehicle inference method, a vehicle inference system, and a program, capable of inferring a vehicle classification of a transportation vehicle without checking the vehicle classification of the transportation vehicle, and of calculating, on the basis of the inferred vehicle classification, the emission amount of carbon dioxide. [Solution] This carbon dioxide emission amount calculation method is executed by a carbon dioxide emission amount calculation system for calculating the carbon dioxide emission amount to be discharged from freight automobile-vehicles which are cargo transportation means, and is provided with: a step for managing shipment data of cargo; a step for storing data of maximum loading capacities of respective vehicle classifications for the freight automobile-vehicles; a step for extracting, on the basis of the shipment data of cargo acquired from a management system, data of cargo to be transported to a prescribed delivery destination, aggregating, on the basis of the extracted data, the total weight of the cargo to be transported to the prescribed delivery destination, and acquiring data of the aggregated weight; a step for inferring, on the basis of the acquired data of the aggregated weight and the stored data of maximum loading capacities of respective vehicle classifications, a vehicle classification of one freight automobile-vehicle on which all the cargo to be transported to the prescribed delivery destination is to be loaded; and a step for calculating, on the basis of the inferred vehicle classification of the freight automobile-vehicle, the carbon dioxide emission amount to be discharged from the freight automobile-vehicle transporting the cargo.

The invention discloses a power breakdown van identification method and device for a cable terminal field, terminal equipment and a storage medium. The method comprises the steps of obtaining a to-be-identified power breakdown van image; then inputting the image into a preset vehicle identification model to enable a backbone network to perform feature extraction to obtain features with different resolutions, and inputting the features into a neck network to perform feature fusion to obtain a plurality of fusion features; wherein a CA module is arranged in the backbone network, and when the backbone network carries out feature extraction on the electric power breakdown van image, local feature recognition is carried out through the CA module; and finally, inputting the fusion features into a corresponding Detect module, generating vehicle identification results of different scales, and according to the identification results, determining a first-aid repair vehicle in the image and a vehicle category. According to the invention, the method can reduce the recognition difficulty caused by the shielding object and illumination transformation in the image, and improves the accuracy of the model in vehicle recognition.

The invention relates to the field of warehouse management, in particular to a logistics warehouse management system based on big data, which comprises a division unit used for determining effective task points according to task updating frequency and determining a region division mode according to the number of the effective task points and a distribution coefficient, determining the vehicle type of the carrying equipment according to the operation efficiency and the loading proportion; the analysis unit is used for determining an analysis processing mode according to the vehicle matching degree of the carrying area; the control unit is used for determining a priority coefficient of each adjustable vehicle, determining a response vehicle according to the priority coefficient, and determining a moving route of the response vehicle according to the radiation demand degree of the movable route; the scheduling unit is used for determining an area state according to the task dispersion of the second-class carrying area and the number of respondable vehicles during vehicle scheduling analysis, determining a scheduling mode according to the area state, and determining a vehicle scheduling range according to a task fluctuation coefficient and obstacle complexity; and the logistics warehouse management efficiency is improved.

A method in a telematics system is provided. The method is for identifying an asset category of a plurality of asset categories. The method includes executing category identifying asset data queries after excluding error causing asset data requests from a main list and excluding asset categories from the main list based on the result of executing category identifying vehicle data queries. The steps are repeated until a single asset category remains in the main list.

The invention discloses an unmanned aerial vehicle battle scoring platform, a scoring mode and an unmanned aerial vehicle battle assembly. The unmanned aerial vehicle battle scoring platform comprises a bearing platform, a sensing module, an identification module and a statistics module, the bearing platform has a bearing surface; the sensing module is used for sensing whether the unmanned aerial vehicle is parked in the bearing surface; the identification module is used for identifying the type of the unmanned aerial vehicle parked in the bearing surface; the statistical module is connected with the sensing module and the recognition module, and the statistical module is used for performing timing or scoring statistics on the unmanned aerial vehicle parked in the bearing surface according to the sensing structure of the sensing module and the recognition result of the recognition module. The unmanned aerial vehicle battle scoring platform provided by the invention can improve the accuracy of unmanned aerial vehicle battle result statistics.