30 results about "Pedestrian behavior" patented technology

The invention discloses a pedestrian behavior simulation operation control method based on an unreal engine. Efficient and vivid pedestrian behavior simulation is realized through five core modules: a three-dimensional scene construction module converts a BIM model based on an unreal engine Datasmith plug-in and constructs an interactive dynamic scene; the pedestrian model construction module defines multi-dimensional pedestrian attributes and supports data-driven initial distribution configuration; the hierarchical behavior control module realizes refined regulation and control of pedestrian behaviors through macroscopic path planning, microcosmic behavior decision and emotion driving; the dynamic resource optimization module adopts level-of-detail control and multi-thread task allocation to improve the operation efficiency of the system; and the real-time interaction and data analysis module provides scene regulation and control, data visualization and simulation data recording and playback functions. According to the method, the authenticity, the dynamic response capability and the controllability of pedestrian simulation are remarkably improved, and reliable technical support can be provided for traffic planning, emergency drilling and building design evaluation.

A control system of a vehicle detects a close-call vehicle-pedestrian encounter where the vehicle and a nearby object of concern almost collide and cause and accident, collects data for a previous period before the detected close-call vehicle-pedestrian encounter, the collected data including data captured by a set of perception sensors of the vehicle during the previous period, and transmits the collected data to a computing server configured to train a vehicle-pedestrian encounter model based on the collected data. The computing server then receives vehicle information indicative of a current state of the vehicle and executes the trained vehicle-pedestrian encounter model using the vehicle information to predict a future potential vehicle-pedestrian encounter and transmits encounter information indicative of the future potential vehicle-pedestrian encounter to the control system, which selectively generates an alert indicative of the future potential vehicle-pedestrian encounter for a driver of the vehicle.

The invention discloses a pedestrian bowing behavior detection method and system for an intelligent garbage booth, and relates to the technical field of intelligent Internet of Things, and the method comprises the steps: employing YOLOv8-Pose to detect key point coordinates and bounding boxes, generating a multi-scale optical flow field through an RAFT algorithm, constructing a sampling set, obtaining an affine transformation matrix and an optical flow residual error, marking an abnormal point, and carrying out the detection of a pedestrian bowing behavior. A replacement optical flow is generated by optimizing an objective function, a spatio-temporal feature tensor is constructed and input into a Transform encoder, smooth key points are output, and updated key point coordinates are generated by screening and complementing; according to the method, through YOLOv8-Pose attitude estimation and RAFT multi-scale optical flow fusion, the precision and robustness of bowing behavior detection are improved, and through Transform space-time coding and HMM state sequence optimization fusion, the real-time performance and reliability of intelligent garbage booth pedestrian behavior detection are improved.

The invention relates to an unmanned aerial vehicle detection-based method and system for detecting pedestrians who do not give way to a vehicle, and the method comprises the steps: carrying out the image collection through an image collection device carried by an unmanned aerial vehicle, and obtaining a traffic image sequence; performing multi-target detection on all moving targets in the frame image through a target detection model to obtain a target detection frame and corresponding confidence; performing trajectory tracking processing on the target detection frames and the confidence coefficients of all the frame images through a target tracking algorithm to obtain a tracking trajectory of the moving target; obtaining a zebra crossing control area, calculating a pedestrian warning point and a vehicle warning point based on the tracking trajectory when the zebra crossing control area in the frame image is detected to have the pedestrian trajectory and the vehicle trajectory, and calculating a vehicle dangerous area according to the vehicle warning point; and according to the tracking trajectory, the pedestrian warning point and the vehicle dangerous area, analyzing a position relationship between the pedestrian and the vehicle, and according to the position relationship and a preset non-comity pedestrian judgment logic, determining whether there is a behavior that the vehicle does not comity the pedestrian.

The application provides a two-phase signal intersection pedestrian nested phase optimization model and a judgment method for inserting a pedestrian nested phase, solves the conflict between existing intersection pedestrians and turning vehicles, and specifically designs a control system for the information interaction of the intersection street-crossing pedestrians and the passing vehicles and the signal lights.In the signal light pedestrian nested phase setting condition, a total cost (including two parts of the conflict number and the delay) of the traffic participants is used as an optimization target, a pedestrian nested phase total cost model is provided, the motor vehicle avoidance pedestrian behavior in the human-vehicle interaction behavior is considered to be different in different cities and different intersections, and therefore, the suggestion value of whether to set the pedestrian nested phase is given under different traffic flow environments, the human-vehicle can pass through the intersection under the condition that the traffic operation cost is minimum, the efficiency and safety of the human-vehicle passing through the intersection are ensured, and the efficiency and safety of the human-vehicle passing through the intersection are ensured.

The invention discloses a pedestrian trajectory prediction method based on lightweight interactive perception GAN, and the method comprises the following steps: S101, carrying out the collection and preprocessing of pedestrian historical trajectory data collected based on a camera in a closed scene, S102, carrying out the feature extraction of a pedestrian and an interaction object based on a distance weight, carrying out the quantitative expression of an interaction relation, and carrying out the prediction of the pedestrian trajectory. The method comprises the following steps: step S103, constructing and training a lightweight GAN model based on a GRU and a double-constraint discriminator to obtain a trained lightweight GAN model, step S104, obtaining and outputting final predicted trajectory data based on trajectory prediction and effectiveness judgment of the trained GAN model, and step S105, carrying out differentiated visual output on historical and predicted trajectories. The method can be widely applied to pedestrian behavior analysis scenes of closed scenes such as shopping mall passenger flow guidance, community security and protection monitoring and supermarket moving line planning, and provides data support for scene operation management and safety prevention and control.

The application discloses a light neural network model edge end cross identification vehicle courtesy pedestrian method and system, belongs to the technical field of identifying vehicle courtesy pedestrians, and comprises the following steps: S1: a vehicle-mounted road surface video collector of a bus video acquisition system collects three groups of picture data of no zebra crossing picture D1, no pedestrian on the zebra crossing D2 and pedestrian on the zebra crossing D3 in a driving process; S2: the collected picture data is subjected to image preprocessing; S3: a zebra crossing identification model is trained by using negative samples (D2+D3) and positive samples D1, and a pedestrian identification model on the zebra crossing is trained by using positive samples D3 and negative samples D2; and S4: when a camera in the vehicle does not detect a zebra crossing on the road, only the zebra crossing identification model is kept listening, and when the zebra crossing on the road is detected; listening data is selectively collected to consider a multidimensional interaction state of a vehicle and a target traffic light area road condition, and a vehicle courtesy pedestrian behavior is quantified in a scoring mode.

Provided are methods for simulated smart pedestrians, The method includes obtaining attributes of at least one pedestrian dynamics model. Simulated sensor data associated with the environment is generated. Operation of an autonomous system in the environment is simulated based on the simulated sensor data, wherein vehicle-pedestrian interactions are modeled by the at least one pedestrian dynamics model as external forces in the environment impacting behavior of the respective pedestrian.

The application provides a commercial space value evaluation and optimization method and system based on pedestrian simulation, relates to the field of simulation, and comprises the following steps: constructing a three-dimensional mall model, including building structure, stall and vertical traffic data; setting a dynamic time engine according to the obtained external data, the dynamic time engine converts the obtained external data and is used to drive the flow of people flow when driving the pedestrian simulation; based on the constructed three-dimensional mall model and the dynamic time engine, a pedestrian simulation model based on commercial simulation is constructed, a reward function is set, a PPO algorithm is used for automatic optimization, and a comprehensive optimal three-dimensional mall model is obtained. The application adopts the above-mentioned commercial space value evaluation and optimization method and system based on pedestrian simulation, realizes the cross-scale mapping from micro pedestrian behavior to macro commercial value, and provides an interpretable, intervenable digital twin platform for commercial space optimization.

The application discloses a pedestrian interaction behavior recognition method in a complex scene, comprising: collecting pedestrian behavior videos in a preset period in a target scene, obtaining continuous trajectory data of each pedestrian and converting the continuous trajectory data into original trajectory data in a real world coordinate system, and obtaining target scene pedestrian motion trajectory data to be recognized through preprocessing; constructing initial recognition rules including initial preset rules and initial to-be-optimized rules; determining a dynamic optimization strategy of the initial to-be-optimized rules according to the target scene, optimizing the initial to-be-optimized rules, obtaining target dynamic recognition rules, and recognizing the target scene pedestrian motion trajectory data according to the rules to determine pedestrian interaction behaviors. The method can realize accurate recognition of pedestrian interaction behaviors, provides a complete pedestrian interaction behavior recognition method from data collection and processing to final behavior determination, and can provide efficient and accurate data support for urban traffic management, public security monitoring and social behavior research.

The invention relates to the technical field of campus safety monitoring, and discloses a campus pedestrian abnormal behavior identification method based on data knowledge fusion. The method comprises the following steps: receiving real-time pedestrian data of a campus monitoring system, comparing the real-time pedestrian data with historical pedestrian behavior data, and extracting behavior characteristic components; using a behavior pattern recognition algorithm to recognize an abnormal behavior pattern from the feature components; performing association analysis on the abnormal modes through a feature importance evaluation algorithm to obtain key behavior factors; taking the key factors as input, constructing a reference behavior curve in combination with a normal behavior model, evaluating pedestrian behaviors according to the degree of deviation between the reference behavior curve and an actual behavior curve, and determining a potential abnormal risk level; and in combination with the abnormal mode, the key factor and the risk level, adopting a time sequence prediction model to predict the pedestrian with abnormal behavior in a preset time, generating an abnormal report, and assisting campus safety management.

The application discloses an intelligent agent multi-target navigation method based on deep reinforcement learning in a dynamic environment, and aims at the problems of separation of pedestrian behavior prediction and decision, insufficient modeling of behavior difference and insufficient multi-target trade-off in the prior art, and constructs a decision framework integrating pedestrian impatience prediction and Nash game. First, a simulation environment containing intelligent agents and pedestrians is established, and state and action spaces are designed; second, a multi-target reward function of safety, sociality and efficiency is constructed, and a meeting angle is introduced to depict the interaction relationship; further, a pedestrian impatience evolution model is established to realize dynamic prediction of the behavior trend of pedestrians; finally, the prediction result is embedded into a Nash equilibrium game model to guide the optimal decision; a safety penalty and a cost Critic network are introduced on the basis of a Soft Actor-Critic algorithm to realize parameter collaborative optimization.

The application relates to the field of automatic driving. A pedestrian crossing intention recognition method based on multi-source information fusion is characterized by comprising the following steps: step one, pedestrian traffic feature extraction: according to the classification of people-vehicles-roads, features obviously affecting the recognition of pedestrian intention are screened out, including road environment features, traffic features and pedestrian behavior data features, and a pedestrian traffic feature extraction model is constructed; step two, encoding all feature information extracted in step one: static, dynamic and time feature coding networks are respectively built to recode the extracted feature information, and deep information related to pedestrian crossing actions is obtained; step three, recognizing the pedestrian crossing intention through multi-feature fusion: a layered and step-by-step feature mixed fusion architecture is determined based on the correlation between feature coding and pedestrian intention recognition, an attention mechanism is introduced, a pedestrian crossing intention recognition network model is established, and the probability of the target pedestrian crossing action is calculated to judge the pedestrian crossing intention.

The invention provides a pedestrian behavior recognition system in a vehicle driving scene, and relates to the technical field of artificial intelligence and computer vision, and the system firstly fuses multi-modal sensor data to lock a high-risk pedestrian target, and then generates a probabilistic sight line cone with uncertainty measurement by using a deep network. On the basis, a space alignment Gaussian kernel with an environmental risk peak value as the center is introduced, an attention focus enhancement algorithm is constructed, and deep coupling calculation is carried out on pedestrian sight probability distribution and a dynamic environmental risk field. The mechanism can accurately quantify the spatial proximity and direction consistency of the sight focus and the key risk source, effectively suppress divergent sight and background noise, amplify an active gaze signal, and finally realize accurate pre-judgment of the pedestrian crossing intention in combination with whole body attitude features, thereby providing a reliable decision basis for an autonomous vehicle.

A method and apparatus for predicting pedestrian safety information based on video is disclosed. Pedestrian trajectory prediction data based on video input is first generated. Then, pedestrian behavior prediction data based on the video input is generated. A potential risk to pedestrian safety is estimated based on the pedestrian trajectory prediction data, the pedestrian behavior prediction data, and a surface classification data in the video data.

A control system of a vehicle detects a close-call vehicle-pedestrian encounter where the vehicle and a nearby object of concern almost collide and cause and accident, collects data for a previous period before the detected close-call vehicle-pedestrian encounter, the collected data including data captured by a set of perception sensors of the vehicle during the previous period, and transmits the collected data to a computing server configured to train a vehicle-pedestrian encounter model based on the collected data. The computing server then receives vehicle information indicative of a current state of the vehicle and executes the trained vehicle-pedestrian encounter model using the vehicle information to predict a future potential vehicle-pedestrian encounter and transmits encounter information indicative of the future potential vehicle-pedestrian encounter to the control system, which selectively generates an alert indicative of the future potential vehicle-pedestrian encounter for a driver of the vehicle.

The invention discloses a traffic station pedestrian path selection prediction method and system based on a space syntax, and the method comprises the steps: carrying out the matching of dynamic pedestrian trajectory data and a static station space structure based on the same coordinate system, and constructing a target traffic station mapping database; performing full-dimensional parameter extraction and screening removal processing on the target traffic station mapping database through a spatial syntax to obtain key spatial syntax features; and constructing a hierarchical heterogeneous nested Logit decision model, and performing pedestrian path selection prediction on the key space syntactic features to obtain a pedestrian path selection prediction result. According to the method, the pedestrian path selection prediction precision of the traffic station can be improved through the quantitative relationship between the space structure in the rail traffic station and the pedestrian behavior. The traffic station pedestrian path selection prediction method and system based on the space syntax can be widely applied to the technical field of traffic station pedestrian planning.

The invention relates to the technical field of abnormal behavior real-time early warning. The invention relates to a pedestrian abnormal behavior real-time early warning system based on image analysis. The system comprises an image acquisition module, an early warning starting module, a safety personnel management module, an early warning adjustment module and an early warning feedback module. The image acquisition module is used for acquiring image data of a parking lot, performing image extraction on vehicles and pedestrians in the image data, and acquiring vehicle images and pedestrian images; the early warning starting module is used for identifying vehicle information according to the vehicle image and extracting a vehicle door image according to the vehicle information and the vehicle image; through the design of dynamically adjusting the abnormal monitoring distance, the method effectively adapts to the scene difference of the number change of pedestrians in the parking lot, optimizes the detection range in real time in combination with the density of the pedestrians around the vehicle, avoids the false early warning flooding when the pedestrians are dense, prevents the risk omission when the pedestrians are sparse, and improves the detection efficiency. And the scene adaptability and accuracy of early warning judgment are greatly improved.

The invention relates to a test scene generation method and device, computer equipment and a storage medium. The method comprises the following steps: acquiring scene parameters; generating initial scene information according to the scene parameters and a scene generation model; the initial scene information is optimized according to a scene guiding model, vehicle behaviors and pedestrian behaviors are adjusted, and target scene information meeting a preset target and a preset rule is generated; the scene generation model is obtained based on diffusion model training; the initial scene information comprises vehicle behaviors and pedestrian behaviors, the vehicle behaviors comprise the position, speed and orientation of a vehicle, and the pedestrian behaviors comprise the position, speed and orientation of a pedestrian; the scene parameters include the number of agents, location, speed, type and boundary information. The scene coverage degree is improved, vehicle and pedestrian behaviors in the scene can be flexibly adjusted, the test scene with the specific shielding relation is generated, and the customization requirements of different test scenes are met.

The invention discloses a pedestrian behavior simulation scene construction method and system, a terminal and a storage medium, and relates to the technical field of crowd simulation, and the method comprises the steps: obtaining a static three-dimensional virtual scene, and determining simulation environment data according to the static three-dimensional virtual scene, the simulation environment data comprising a two-dimensional structured navigation map and at least one global reference trajectory; obtaining state data corresponding to the virtual pedestrian, and obtaining a predicted motion trajectory corresponding to the virtual pedestrian through a trained trajectory diffusion generation model according to the state data and the simulation environment data; and controlling the virtual pedestrian to move in the static three-dimensional virtual scene according to the predicted motion track. Therefore, the pedestrian motion trail closer to the real scene can be generated based on the pre-trained trail diffusion generation model, so that the motion trail of the virtual pedestrian is closer to the pedestrian motion trail in the real scene, the authenticity of pedestrian behaviors can be improved, and the authenticity of the constructed pedestrian behavior simulation scene can be improved.

The application discloses a kind of pedestrian trajectory classification methods based on semi-supervised random neural network.The method includes constructing pedestrian trajectory data;Pedestrian trajectory data is preprocessed;Different trajectory patterns are established according to pedestrian trajectory data;Finally, the combination algorithm of quantifiable minimum error entropy criterion and semi-supervised random neural network algorithm is used to classify pedestrian behavior.The application uses quantifiable minimum error entropy criterion to replace traditional mean square error criterion, and is combined with semi-supervised random neural network algorithm, which can further improve the classification ability of existing semi-supervised learning model, and also improves the accuracy of pedestrian trajectory classification.

The invention discloses a multi-attribute and multi-stage microscopic simulation method and device based on pedestrian behavior characteristics. The microcosmic simulation method comprises the steps that the pedestrian attribute and the initial position of a pedestrian agent are initialized, the pedestrian attribute comprises the initial speed, and the size of the initial speed is determined based on emotion driving characteristics; under the condition that the preset stage judgment condition is not met, each pedestrian agent is controlled to be in a normalization stage, and the real-time normal speed is determined in real time based on the target guiding characteristic; under the condition that the preset stage judgment condition is met and the simulation scene is a non-emergency scene, continuously controlling each pedestrian agent to be in a normalization stage; under the condition that the preset stage judgment condition is met and the simulation scene is an emergency scene, each pedestrian intelligent body is controlled to be in an evacuation stage, and the real-time evacuation speed is determined in real time based on the target guiding characteristic, the emotion driving characteristic, the physical strength guarantee characteristic and the state evaluation characteristic. Therefore, the accuracy of microscopic simulation is improved.

The invention belongs to the technical field of automobile automatic driving, and particularly relates to an automatic driving vehicle pedestrian behavior prediction method, which comprises the following steps of: detecting a pedestrian pose by adopting an open-source Mediape model; preprocessing the detected image; after the grayscale image is obtained, carrying out statistics and storage on the related state quantity of the pose point in each frame; after predicting and obtaining the position of the pose point in the next frame, judging whether the pose point is reliable or not; and after the pose point positions of the front and back frames are obtained, pedestrian behaviors are predicted. According to the invention, accurate prediction of pedestrian behaviors is realized through a high-precision pedestrian recognition model and a pose tracking algorithm. Real-time tracking is carried out by using an open source model and a grayscale image processing technology, the reliability of pose points is ensured through reverse optical flow tracking and boundary inspection, and finally a reliable decision basis is provided for an automatic driving system. The high-precision detection capability in a complex environment, optimization of calculation efficiency and accurate prediction of pedestrian behaviors are realized, so that the safety and reliability of automatic driving are remarkably improved.

A method of generating pedestrian behavior prediction information includes setting image information from present to a certain time in the past as the target observation image, extract multiple visual input feature information and non-visual input feature information from the target observation image, grouping the multiple visual input feature information and non-visual input feature information, and inputting the grouped information to separated processing modules and generating pedestrian behavior prediction information by concatenating output results of the processing modules.

The invention relates to the technical field of traffic scene simulation, and discloses a multi-traffic participant modeling method and system fusing intention reasoning and density gradient, and the method comprises the steps: obtaining the dynamic data of multiple traffic participants in a complex traffic scene, and the multiple traffic participants comprise motor vehicles, non-motor vehicles and pedestrians; constructing a motor vehicle behavior model according to the dynamic data of the motor vehicle, wherein the motor vehicle behavior model predicts an overall behavior intention and a specific behavior intention of the motor vehicle; and according to the dynamic data of the non-motor vehicles and the dynamic data of the pedestrians, constructing a non-motor vehicle behavior model and a pedestrian behavior model based on the behaviors of the non-motor vehicles, the behaviors of the pedestrians and the traffic density. According to the method, the behaviors of the traffic participants can be modeled, and the simulation effect of pedestrians and vehicles is improved.

The application relates to the technical field of traffic scene simulation, and discloses a multi-traffic-participant modeling method and system fusing intention reasoning and density gradient, which comprises the following steps: acquiring dynamic data of multi-traffic-participants in a complex traffic scene, wherein the multi-traffic-participants comprise motor vehicles, non-motor vehicles and pedestrians; constructing a motor vehicle behavior model according to the dynamic data of the motor vehicles, wherein the motor vehicle behavior model predicts overall behavior intention and specific behavior intention of the motor vehicles; and constructing non-motor vehicle behavior models and pedestrian behavior models based on behaviors of the non-motor vehicles, behaviors of the pedestrians and traffic density according to the dynamic data of the non-motor vehicles and the dynamic data of the pedestrians. The application can model behaviors of traffic participants and improve simulation effects of pedestrians and vehicles.

The application provides a text-guided cascaded network pedestrian crossing intention prediction method and system, relates to the technical field of pedestrian behavior analysis in an intelligent transportation system, and comprises the following steps: collecting road video by using a vehicle-mounted camera, detecting pedestrians in the road video based on a YOLO algorithm, and extracting pedestrian coordinate sequences with continuous frames; sequentially performing dimension expansion and feature extraction on the pedestrian coordinate sequences to obtain predicted pedestrian coordinates corresponding to the pedestrian coordinate sequences; splicing the pedestrian coordinate sequences and the predicted pedestrian coordinates to obtain complete coordinate sequence features; generating a crossing behavior description corresponding to a structured prompt word based on a structured prompt word generated by a large language model and a pedestrian crossing video, encoding the crossing behavior description into behavior description features; and performing feature alignment on the complete coordinate sequence features and the behavior description features by using a cosine similarity function to obtain a pedestrian intention prediction result. The application helps to improve the expression ability of pedestrian dynamic intention.

The invention discloses an intelligent sensing anti-collision protection device for a revolving door, and relates to the technical field of revolving door protection equipment, the intelligent sensing anti-collision protection device comprises a fixed glass frame, a central shaft assembly, the revolving door, a detachable anti-pinch part, an anti-collision part and a buffer assembly; the anti-collision part comprises a protective curtain, and the protective curtain is of an array type grating structure and can be rapidly unfolded and achieve buffering through gas filling; the detachable anti-pinch part can adjust the gap between the revolving door and the fixed glass frame, so that the hand is prevented from being pinched, and disassembly and maintenance are convenient; through cooperative linkage of multiple structures, all-directional anti-collision and anti-pinch and pedestrian behavior warning are achieved, operation is stable, universality is high, the intelligent revolving door is suitable for revolving doors in various public places, use safety and management convenience can be remarkably improved, and the problem that an existing revolving door lacks pedestrian behavior sensing warning and protection is solved.

The invention discloses an escalator pedestrian falling detection method based on CEF-YOLOv10 and attitude time sequence modeling, and belongs to the field of computer vision pedestrian real-time behavior detection. According to the method, an improved model CEF-YOLOv10 is constructed, and collaborative modeling of local convolution and image overall perception is realized by combining local window attention and grouping attention mechanisms. A bidirectional feature fusion and context information enhancement strategy is adopted to construct a context enhanced bidirectional feature pyramid network and a dynamic background suppression and hierarchical feature decoupling module, and an improved Openpose attitude estimation model is adopted to perform attitude estimation on a confidence frame region image. And a long-short-term memory network model is introduced to perform tumble behavior reasoning and judgment, so that real-time and reliable tumble detection and intelligent early warning are realized. The method has the advantages of being high in spatial-temporal feature expression ability, flexible in deployment, rapid in response and the like, and is suitable for setting an intelligent security system in a public environment of the escalator.

The application discloses a personnel abnormal behavior recognition method and system based on a space-time action positioning algorithm, generates a video saliency map dynamically through a self-attention mechanism, can focus on a key pedestrian area, and suppresses background interference. Through a timestamp judgment module, invalid frames such as fuzzy and no-person positioning frames are screened out, and only high-quality effective image frames are reserved. Combined with mixed scale attention calculation, the feature extraction granularity can be adaptively adjusted according to the area importance, fine-grained analysis is adopted for a salient area to improve the recognition precision, and coarse-grained processing is adopted for a non-salient area to save the computing resources. A personnel attention enhancement mechanism is introduced, a pedestrian area saliency map is generated by using a target detection result, prior knowledge is provided for a video understanding model, and therefore the fine-grained feature extraction capability for pedestrian behavior is enhanced. Through an end-to-end joint optimization framework, the target detection and the behavior recognition task are unified, and the performance loss caused by task fragmentation in the traditional method is avoided.