48 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.

The embodiment of the invention provides a trajectory prediction method and device and a vehicle. The method comprises the steps that environment information around a vehicle, pedestrian information, intention probability and scene probability are acquired, and the intention probability is used for representing the probability that pedestrians execute each predefined pedestrian behavior mode in each predefined typical traffic scene; the scene probability is used for representing the probability that the traffic scene where the vehicle is located currently is the typical traffic scene; and performing trajectory prediction on the pedestrian based on the environment information, the pedestrian information, the intention probability, the scene probability and a pre-constructed space-time prediction network. According to the method, the scene probability and the intention probability are utilized to guide the space-time prediction network to learn a pedestrian motion mode under scene constraint and intention guidance, and the trajectory prediction accuracy can be improved.

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 provides an intelligent driving obstacle avoidance method and device based on pedestrian attitude trajectory prediction, equipment and a storage medium, and belongs to the technical field of intelligent driving. Comprising the following steps: inputting an obtained four-channel RGB image, position information, speed, acceleration, direction, attitude and skeleton key points of a target pedestrian within a preset time length into a preset attitude trajectory prediction model, and predicting coordinates of the skeleton key points of the target pedestrian; and determining an intelligent obstacle avoidance strategy according to the coordinates of the skeleton key points and the driving path data of the target vehicle. According to the invention, the prediction accuracy of the intelligent driving system on pedestrian behaviors can be improved, and the safety of obstacle avoidance decision making is enhanced.

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 present invention discloses a pedestrian circulation and evacuation assessment simulation system in public buildings, relating to the field of evacuation simulation. The system includes simulating pedestrian circulation and evacuation assessment in public buildings. The pedestrian circulation simulation analyzes pedestrian behavior patterns and dynamic changes in different scenarios. The evacuation assessment simulation utilizes multi-agent modeling technology to develop an underlying pedestrian simulation engine. Within the underlying pedestrian simulation model, pedestrian behavior planning combines behavior trees and finite state machines, using a flow chart-style behavior node editor as user behavior input. The underlying pedestrian simulation engine implements dynamic simulation based on pedestrian dynamics. The ORCA collision algorithm is used in pedestrian dynamics to ensure that agents plan paths in an optimal manner. A steering force model, SteeringBehavior, is used in pedestrian dynamics to simulate acceleration and steering behavior. The present invention utilizes the aforementioned methods to improve the performance and expandability of evacuation simulation.

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 application provides a forklift and pedestrian trajectory prediction and collision warning method based on multi-camera fusion, and relates to the technical field of forklift and pedestrian trajectory prediction and collision warning. The application acquires panoramic and regional images in a factory building, establishes panoramic and regional detection models, detects the moving state of forklifts and pedestrians, pedestrian behavior characteristics, driver driving data and forklift characteristics in the factory building respectively, establishes a pedestrian trajectory prediction model, predicts the pedestrian walking path according to the pedestrian basic prediction data, moving state and current work content, establishes a forklift trajectory prediction model, plans the forklift carrying route according to the forklift basic prediction data, moving state and current loading and unloading content, judges the fitting degree of the forklift through real-time driving safety scoring of the driver and forklift state safety scoring, sets the warning time of the forklift, and performs collision warning according to the trajectory prediction of the pedestrian and the forklift.

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.

This invention discloses a multi-label pedestrian abnormal behavior recognition method based on computer vision, belonging to the field of computer vision application technology. This method acquires pedestrian behavior sample video data from surveillance videos; preprocesses the acquired pedestrian behavior sample video data to obtain a final dataset; divides the final dataset proportionally into training and test sets to build a BDNet dual-branch convolutional neural network classification model to classify and recognize pedestrian abnormal behaviors; and uses the trained BDNet dual-branch convolutional neural network classification model to identify pedestrian actions. This method uses computer vision algorithms to automatically extract overall human features and facial detail features, fits multi-attribute pedestrian action types through multiple linear regression, and accurately predicts various pedestrian violations and provides real-time warnings in a non-contact, real-time manner, playing an important role in pedestrian safety detection.

The application relates to a multi-modal fusion-based automatic driving safety assistance method and system, and belongs to the field of automatic driving. The system is composed of a multi-modal sensor, a preprocessing unit, a feature encoder, a time sequence merging module, a context attention module, a classification module, a data storage unit and an output unit. The method comprises the following steps: S1: collecting visual image data and non-visual image data; S2: preprocessing the visual image data and the non-visual image data; S3: performing feature extraction by using the feature encoder; S4: performing key event division by using the time sequence merging module; S5: performing feature fusion by using the context attention module; S6: inputting the fused features into the classification module to predict the pedestrian behavior type; and S7: storing the predicted pedestrian behavior type result and feeding back the result to a car terminal system. The method can effectively identify and aggregate key events related to pedestrian behavior, and improve the accuracy and efficiency of pedestrian behavior intention prediction.

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.

This invention discloses a method for vehicle behavior recognition and trajectory prediction based on a recurrent convolutional neural network, belonging to the field of intelligent driving. It includes the following steps: Step 1: Collect vehicle-related data for the autonomous vehicle's driving environment; Step 2: Based on the collected dataset, model vehicle behavior, road environment, and pedestrian / cyclist behavior, constructing a vector-representation-based human-vehicle-road coupling relationship model; Step 3: Based on the human-vehicle-road coupling relationship model, use a long short-term memory network to perform behavior pattern recognition on the target vehicle; Step 4: Combining the human-vehicle-road coupling relationship model and the vehicle behavior recognition results, use a convolutional neural network to predict the target vehicle's trajectory. Compared with existing technologies, the positive effects of this invention are: This invention effectively improves the accuracy of autonomous vehicles in recognizing the behavior of surrounding vehicles and predicting their trajectories, enhancing the efficiency and safety of autonomous vehicle operation.

The invention provides a commercial space value evaluation and optimization method and system based on pedestrian simulation, and relates to the field of simulation, and the method comprises the following steps: constructing a three-dimensional shopping mall model which comprises a building structure, bunks and vertical traffic data; a dynamic time engine is set according to the obtained external data, and the dynamic time engine converts the obtained external data and is used for driving flowing of the pedestrian flow during pedestrian simulation; and based on the constructed three-dimensional shopping mall model and the dynamic time engine, constructing a pedestrian simulation model based on commercial simulation, setting a reward function, and performing automatic optimization by using a PPO algorithm to obtain a comprehensive optimal three-dimensional shopping mall model. According to the commercial space value evaluation and optimization method and system based on pedestrian simulation, cross-scale mapping from microscopic pedestrian behaviors to macroscopic commercial values is realized, and an explainable and intervening digital twin platform is provided for commercial space optimization.

The invention discloses a pedestrian interaction behavior recognition method in a complex scene, and the method comprises the steps: collecting a pedestrian behavior video in a preset period in a target scene, obtaining the continuous track data of each pedestrian, converting the continuous track data into original track data in a real world coordinate system, and carrying out the preprocessing, and obtaining the pedestrian motion track data of the to-be-recognized target scene; constructing an initial recognition rule comprising an initial preset rule and an initial rule to be optimized; and determining a dynamic optimization strategy of the initial to-be-optimized rule according to the target scene, optimizing the initial to-be-optimized rule to obtain a target dynamic identification rule, and identifying the pedestrian movement track data of the target scene according to the rule to determine the pedestrian interaction behavior. According to the method, accurate identification of pedestrian interaction behaviors can be realized, a complete pedestrian interaction behavior identification method from data acquisition and processing to final behavior determination is provided, and the method can provide efficient and accurate data support for urban traffic management, public safety monitoring and social behavior research.

Methods, systems, and apparatus, including computer programs encoded on computer storage media, for agent behavior prediction using keypoint data. One of the methods includes obtaining data characterizing a scene in an environment, the data comprising: (i) context data comprising data characterizing historical trajectories of a plurality of agents up to the current time point; and (ii) keypoint data for a target agent; processing the context data using a context data encoder neural network to generate a context embedding for the target agent; processing the keypoint data using a keypoint encoder neural network to generate a keypoint embedding for the target agent; generating a combined embedding for the target agent from the context embedding and the keypoint embedding; and processing the combined embedding using a decoder neural network to generate a behavior prediction output for the target agent that characterizes predicted behavior of the target agent after the current time point.

The embodiment of the invention relates to the technical field of pedestrian behavior recognition, and particularly discloses a pedestrian behavior recognition method and system based on an FPGA. According to the embodiment of the invention, the method comprises the steps: receiving an input video to be detected, carrying out the frame-by-frame processing of the video to be detected, obtaining a plurality of input images, and constructing a pedestrian detection network and a pedestrian tracking network; performing behavior intention recognition on the plurality of input images; and generating a plurality of future prediction frames by using the PredNet, and performing behavior intention prediction on the plurality of future prediction frames. Pedestrians can be detected and tracked, postures of the pedestrians are extracted from each frame of picture, skeleton features are fitted, microscopic features of the pedestrians during crossing of the street are obtained, pedestrian behaviors are predicted by adopting a PredNet network so as to judge pedestrian behavior intentions, high-accuracy and low-delay pedestrian behavior intention recognition can be achieved, and the pedestrian behaviors can be classified and predicted. Therefore, the current behavior and future intention of the pedestrian are judged, and potential dangers are found and avoided in advance.

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 relates to the field of artificial intelligence, and provides a pedestrian abnormal behavior detection method in a pedestrian and vehicle mixed driving environment, and the method comprises the steps: obtaining continuous image frames of a pedestrian and vehicle mixed driving scene in real time; detecting pedestrians in the continuous image frames, extracting positions and appearance features of the pedestrians, and generating an initial pedestrian trajectory; performing dynamic tracking on the pedestrian trajectory based on the multi-modal motion state, and generating a pedestrian tracking trajectory updated in real time; constructing a space-time interaction graph network, predicting a pedestrian future trajectory and generating a trajectory confidence coefficient; according to the space-time deviation value, the track confidence coefficient and the interaction relation mutation feature of the pedestrian future track and the pedestrian tracking track, the pedestrian behavior abnormal level is judged; and when the pedestrian behavior abnormal grade exceeds a preset threshold value, triggering a grading early warning signal and generating avoidance guidance information. According to the technical scheme, tracking continuity, prediction accuracy and early warning timeliness can be effectively improved.

The invention provides a role-divided walking behavior modeling method and system based on a deep reinforcement learning model, and belongs to the technical field of behavior recognition. And inputting the state into a deep reinforcement learning model DB-DDPG, and outputting a corresponding walking action to guide the pedestrian to move towards a preset getting-on and getting-off target. According to the method, a deep reinforcement learning model is introduced in a two-dimensional particle dynamics simulation environment, and refined modeling is carried out on pedestrian behaviors in a high-density complex getting-on and getting-off scene. The model aims to construct a new behavior optimization normal form for a universal complex getting-on and getting-off walking scene, so that an intelligent agent can autonomously learn a walking path and realize adaptive optimization on the premise of not presetting a human rule or a path strategy, thereby enhancing the response capability and behavior intelligence of the intelligent agent to dynamic environment change.

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.