7747 results about "Pedestrian" patented technology

The invention provides a vehicle collision risk early warning method, system and device and a medium, and belongs to the technical field of vehicle safety control. The method comprises the steps that surrounding environment information of a vehicle is collected in real time through a vehicle built-in sensor and a GPS module; a multi-sensor fusion device technology is used to carry out comprehensive processing on surrounding environment information of a vehicle, and an environment perception model is generated after data integration; based on the environment perception model, target identification is carried out by using a computer vision technology based on a convolutional neural network so as to identify travelers, vehicles, traffic signs and obstacles; performing behavior prediction based on a deep learning model to predict a behavior track of a front vehicle or pedestrian; evaluating a potential collision risk in the current scene in combination with the target recognition result and the behavior prediction result; when a potential collision risk is detected, an early warning signal is sent to a driver, and when the driver does not take corresponding measures, active safety measures are automatically triggered to avoid collision or reduce accident damage.

The invention provides an edge computing traffic light intelligent decision-making method integrated with AI video analysis, and the method comprises the steps: collecting a real-time traffic video stream of a target intersection through an edge computing device, and extracting a dynamic traffic feature set in the real-time traffic video stream, calling a pre-trained space-time analysis model to carry out multi-modal fusion processing on the dynamic traffic characteristic set, generating a traffic state vector of the target intersection, matching a candidate control strategy in a preset decision rule base based on the traffic state vector, and carrying out parameter adjustment on the candidate control strategy through a strategy optimization model to obtain a target traffic state vector; generating a target signal lamp control parameter; and issuing the target signal lamp control parameters to a traffic signal control terminal of the target intersection, and monitoring traffic state change data of the target intersection in real time to update weight parameters of the space-time analysis model. According to the invention, the traffic efficiency, pedestrian safety and traffic management intelligence degree of urban intersections can be improved.

The invention provides an edge calculation traffic light emergency control method and device for a sudden traffic event. The method comprises the following steps: acquiring real-time traffic flow information of a target intersection; determining an emergency response area of the target intersection based on the position identifier of the sudden traffic event, and extracting a dynamic offset between a historical traffic flow feature and a current traffic flow feature in the emergency response area through an edge computing node; inputting the dynamic offset into a pre-trained traffic signal adaptive model, generating a traffic signal lamp control parameter set corresponding to the emergency response area, and sending a control instruction sequence to the traffic signal lamp of the target intersection according to the traffic signal lamp control parameter set, and the execution priority of the control instruction sequence is dynamically adjusted based on the real-time change trend of the vehicle density distribution data and the pedestrian movement track data. According to the invention, the intersection passing efficiency and the safety guarantee capability of traffic participants under the sudden traffic event can be improved.

The invention relates to the technical field of traffic accident analysis, and discloses an intelligent analysis method for vehicle and pedestrian collision accident liability, and the method comprises the steps: firstly obtaining multi-source heterogeneous accident data, fusing a cross-platform data source through employing a federal learning framework when the data is insufficient, and reconstructing an accident scene three-dimensional coordinate system; and then, based on a multi-modal data fusion result, constructing a traffic participation entity relation topology model by using a graph neural network, and generating an accident dynamic evolution graph. Then, establishing a collision dynamics digital twin model by utilizing a physical engine, extracting a key collision feature vector, and constructing a responsibility probability distribution model based on a generative adversarial network; and optimizing a responsibility judgment strategy by adopting a double-layer reinforcement learning framework, verifying a physical simulation result through a hierarchical verification mechanism, analyzing a responsibility judgment logic chain, and finally outputting a responsibility analysis report with an interpretable label. The method can accurately and intelligently analyze the accident liability, and has good interpretability.

The invention discloses a road disease detection system and method based on multi-source data fusion. The system comprises a data acquisition module, a data preprocessing module, a disease detection module, a three-dimensional modeling module, a data analysis module and a maintenance module. The data acquisition module is used for acquiring road surface data and sending the acquired data to the data preprocessing module; the road surface data comprises road data, road disease data, road vehicle data, non-motor vehicle data and pedestrian data, compared with the prior art, more comprehensive and more accurate detection of road diseases is realized by fusing multi-source data; by means of multi-dimensional data acquisition of radar data, video data, three-dimensional point cloud data, geographical location information and the like, the limitation of a single data source is overcome; the data preprocessing module can effectively remove noise and abnormal values, accurately classify and mark diseases, and fuse data collected by different sensors, so that the quality and integrity of the data are improved.

The invention belongs to the technical field of information, and relates to a semantic guidance pedestrian re-identification method and system based on text prompt. The method comprises the following steps: inputting a training image into a visual encoder to obtain visual embedding; mapping the visual embedding into a text space by using a reverse network to obtain a pseudo token, and integrating the pseudo token into a natural language sentence to obtain a language prompt for an input image; inputting the language prompt into a text encoder to obtain text embedding; training a multi-modal interaction module by utilizing visual embedding and text embedding; and inputting a query picture into the trained multi-modal interaction module to obtain a feature vector fusing vision and text information, and executing similarity retrieval in a pedestrian image database by using the feature vector fusing vision and text information to obtain a pedestrian re-identification result. According to the method, the retrieval performance on an existing pedestrian re-identification data set is remarkably improved.

The invention discloses an intelligent traffic signal device and a remote control method, and relates to the technical field of intelligent traffic control. The method is used for solving the problems of low emergency traffic efficiency and global and local control imbalance under sudden traffic events. The method comprises the following steps: firstly, fusing an emergency vehicle navigation path, accident point vehicle motion abnormal parameters and pedestrian aggregation distribution data, constructing a multi-dimensional event evolution feature vector through a space-time encoder, and accurately describing a traffic situation; an event influence domain boundary is delimited based on vehicle trajectory and flow direction consistency analysis, a dynamic evolution model is constructed in combination with an acceleration abrupt change propagation path, and a space-time conflict probability matrix is generated; then executing a hierarchical control strategy, dynamically adjusting an intersection signal period starting time difference in a global level to form an emergency green wave coordination band, and inserting an adaptive full red phase in a local level according to a balance relation between a vehicle arrival rate and a dissipation rate; and finally, reversely correcting model parameters through an actual pass error, updating a conflict probability generation rule, and forming a closed-loop feedback mechanism.

Techniques for detecting attributes and / or gestures associated with pedestrians in an environment are described herein. The techniques may include receiving sensor data associated with a pedestrian in an environment of a vehicle and inputting the sensor data into a machine-learned model that is configured to determine a gesture and / or an attribute of the pedestrian. Based on the input data, an output may be received from the machine-learned model that indicates the gesture and / or the attribute of the pedestrian and the vehicle may be controlled based at least in part on the gesture and / or the attribute of the pedestrian. The techniques may also include training the machine-learned model to detect the attribute and / or the gesture of the pedestrian.

The invention provides a pedestrian trajectory prediction method based on window attention and a space diagram interaction network, and belongs to the technical field of computer vision. The technical problems of difficult long-time dependence modeling and complex space interaction are solved. According to the technical scheme, the method comprises the following steps: S1, acquiring data of a data set; s2, in the time dimension, designing a window mask mechanism, and adjusting the attention receptive field at each moment; s3, constructing a hierarchical heterogeneous graph convolutional network according to a spatial dimension, and combining a pedestrian dynamic interaction graph with a scene static semantic graph; and S4, inputting the time dimension features and the space dimension features into a multi-scale expansion convolutional network to generate a multi-modal trajectory. The method has the beneficial effects that the model is subjected to experimental verification on a public data set ETH / UCY, the experimental result proves the effectiveness of the model, the superior performance of the model is shown on key indexes, and the generalization ability in processing different scenes is also excellent.

The invention discloses a cross-modal pedestrian re-identification method and system based on wavelet transform, and relates to the technical field of pedestrian re-identification, and the method comprises the steps: constructing a two-stream ResNet-50 backbone network; fusing the features of the shallow layers of the two streams of ResNet-50 backbone networks through an ICB module to obtain fused features; carrying out feature extraction on the fusion features through a WEB branch module to obtain modal sharing features of high and low frequency parts; and a final identification result is obtained through the modal sharing feature output by the dual-branch center guiding loss optimization WEB branch module and the fusion feature output by the ICB module. According to the invention, through the information compensation module, the lost valuable information in the network feature extraction process is compensated by combining the features of the shallow layer of the wavelet transform aggregation network at different stages, and the quality of the final feature vector is improved; and through a wavelet enhancement module, dual-branch center guide loss is used to guide the network to mine modal invariant information in a wavelet sub-graph, so that the model performance is improved.

The invention discloses a multi-target pedestrian re-identification system based on multiple modes and a vector database, relates to the technical field of network communication and positioning, and solves the problem of cross-target and cross-mode trajectory association in a complex multi-camera scene. The multi-target pedestrian re-recognition system comprises a monocular tracking module, a multimode extraction module, a trajectory generation module, a multi-objective matching module and a global retrieval module, through organic combination of multi-modal features and a multi-modal multi-path recall strategy, the accuracy and applicability of cross-modal pedestrian re-identification are significantly improved. Through track-level feature generation and storage design, the modeling capability of dynamic features of a target in a complex scene is enhanced; through collaborative design of a space-time constraint mechanism and multi-modal features, logic consistency and global optimality of target person trajectory association are ensured.

The invention provides an online car-hailing passenger position rapid positioning method based on voice interaction and visual perspective, and relates to the technical field of intelligent traffic, and the method comprises the following steps: arranging a sensor, a camera, a metal reflective curtain and a projector on a car; voice information of a driver is collected, and an intelligent voice wake-up model is designed; constructing a scenarized speech recognition model; detecting a pedestrian target for the panoramic image by using a sound-image multi-level cross-modal detection model, and obtaining appearance and posture feature detection information of the pedestrian target; a static feature matching method and a dynamic feature recognition model are designed, and passenger feature information matching is achieved; based on a matching result, determining position information of the passenger; acquiring a passenger direction taking the vehicle as a reference origin through position estimation in the panoramic image; according to the direction estimation result, a projector in the vehicle is controlled, the passenger information is projected on a curtain in the corresponding direction in the vehicle, and meanwhile voice information is used for giving a prompt.

The invention discloses an on-bridge pedestrian abnormal behavior identification and early warning method based on intelligent monitoring, and relates to the technical field of bridge engineering, and the method comprises the steps: carrying out the registration of a current visible light image and a current infrared image, and obtaining a registration dual-mode image based on a current image coordinate system; considering current bridge deformation to establish a current coordinate mapping conversion model for mapping pixel coordinate points in the current image coordinate system to physical coordinate points of a current bridge physical space coordinate system; obtaining the current three-dimensional actual coordinates of the pedestrian; recognizing abnormal behaviors of the pedestrians on the bridge based on the current three-dimensional actual coordinates of the pedestrians and acquiring current dynamic risk score values of the pedestrians; and determining the current risk level of the pedestrian according to the current dynamic risk score value of the pedestrian and a preset risk threshold value to realize multi-level linkage protection response. The method provided by the invention solves the technical problem in the prior art that the current actual coordinate of the pedestrian is difficult to accurately obtain due to environmental interference, and the abnormal recognition of the pedestrian on the bridge is inaccurate.

The invention relates to the technical field of information security, in particular to a security protection system for highway smart screen information release, which comprises an access activity monitoring module, an access control management module, a data source analysis module, a storage demand evaluation module, a backup frequency adjustment module and a flow mode analysis module. According to the method, the risk that the data is tampered is reduced by analyzing the access behavior of the information published by the smart screen, identifying the abnormal behavior, dynamically adjusting the authority and evaluating the consistency of the data and the correctness of the source, and the priority of data storage is adjusted according to the sensitivity and importance of the data; the data backup frequency is adjusted by using the time and frequency of information release, the efficiency and pertinence of data backup are improved, the response capability to abnormal traffic and the security of data are enhanced by monitoring the traffic mode of data transmission in real time, the stability and reliability of highway information release are ensured, and the data backup efficiency and pertinence are improved. And accurate and timely traffic information is provided for drivers and pedestrians.

The invention relates to a multi-stage filtering road spilled object detection method based on dynamic difference analysis, which is suitable for automatic identification of unstructured foreign matters in video monitoring. The method comprises the following steps: firstly, extracting a reference image road mask, eliminating vehicle and pedestrian interference by using YOLOv8 detection, and extracting a motion candidate area through a frame difference method and background modeling; and then context expansion and super-resolution reconstruction are carried out on the candidate region, the candidate region is converted into an HSV space, multi-dimensional features such as color similarity, structural similarity and shadow determination are synthesized for screening, false detection is further removed in combination with inter-frame time sequence consistency, and finally a stable detection result is output. The method provided by the invention has the advantages of strong anti-interference capability, high adaptability, high detection precision and the like, and is suitable for the intelligent recognition task of the expressway thrown objects in a complex environment.

The invention belongs to the field of data processing, and discloses a test method for pedestrian trajectory prediction and emergency braking in an automatic driving platform. Comprising the steps of segmenting a real road environment in advance, obtaining grades of small road segments according to calculation of segmentation coefficients, carrying out resource allocation on the small segments according to the grades, and collecting multi-source heterogeneous data; importance optimization is introduced into a Transform model, possible prediction trajectories of individuals are generated, graph data are constructed by using the possible prediction trajectories, an Autoencoder anomaly detection mechanism is introduced, high-risk trajectories are screened out, and high-risk scenes are output; combining the road geometric model with the high-risk scene to form a scene library, loading the scene in a simulation platform, carrying out automatic driving test, carrying out virtual-real data cross validation in combination with real vehicle test data, and obtaining performance evaluation of an algorithm; pedestrian track prediction and emergency braking in the automatic driving process are achieved.

The invention relates to the technical field of vehicle safety, in particular to a pedestrian body model chest injury risk curve adjusting method. Comprising the following steps: analyzing the relationship between the speed and the chest injury risk, and drawing a first curve between the vehicle speed and the injury risk based on accident statistical data; fitting data of a rib cortical bone uniaxial stretching material mechanics experiment; constructing a vehicle-pedestrian side impact simulation experiment matrix under different working conditions, and generating a simulation sample number; extracting the rib strain of the human body model in the sampling model, calculating the fracture probability of a single rib of the chest of the human body model, and then calculating the maximum simple injury probability of the specified chest of the human body model; fitting the relationship between the vehicle speed in the simulation model and the chest injury risk of the human body model, and drawing a second curve between the vehicle speed in the sample and the chest injury risk of the pedestrian model; and constructing an objective function according to the difference between the first risk curve and the second risk curve. According to the technical scheme, the accuracy of accident damage prediction can be improved.

The invention relates to the technical field of smart city traffic, in particular to a smart city traffic indication system and a working method thereof. According to the technical scheme, the system comprises a multi-source heterogeneous data acquisition module, a data preprocessing and fusion module, a feature extraction and analysis module, a traffic flow prediction and signal timing decision module and a signal display and communication module, the acquisition frequency is dynamically adjusted according to the traffic flow. Traffic and meteorological data are dynamically collected through multiple types of sensors, the quality of data preprocessing and fusion is guaranteed, features are accurately extracted, signal timing is optimized through reinforcement learning, the display and communication module effectively transmits information and cooperates with surrounding traffic, emergency situations can be dealt with, tail gas emission is reduced, pedestrian waiting time is shortened, and traffic safety is improved. And a basis can be provided for long-term traffic planning, and the traffic efficiency, the environmental protection property and the pedestrian friendliness are comprehensively improved.

The invention belongs to the technical field of computer vision and pattern recognition, and particularly relates to a clothes changing pedestrian re-recognition method based on clothes perception mixing and foreground enhancement. The method comprises the following steps: acquiring image data to construct a data set, performing text description on acquired images, preprocessing the image data in the data set, and dividing the image data into a training set and a test set; a clothes changing pedestrian re-recognition model is constructed, the model comprises a text feature extraction unit, an image feature extraction unit and a feature fusion unit, the pedestrian images in the training set are input into the clothes changing pedestrian re-recognition model for training, and a loss function of each unit is obtained; summarizing the loss to obtain total loss, and training and optimizing the clothes changing pedestrian re-identification model through the total loss; inputting data in the test set into the optimized and trained clothes changing pedestrian re-identification model for similarity comparison, and selecting an image with the highest similarity as a final retrieval result; according to the invention, the accuracy of re-identification of clothes changing pedestrians can be improved.

The invention discloses a cross-modal pedestrian re-identification method based on a multi-scale cross attention Transform, and the method comprises the following steps: step 1, image acquisition, step 2, feature set acquisition, step 3, feature fusion, and step 4, feature determination: in an initial stage of a network, two standard data sets are used to acquire RGB and infrared images, and then, the RGB and infrared images are subjected to feature fusion; the method comprises the following steps: extracting key features from RGB and an infrared image by using MSCAViT to obtain two feature sets, then converting the RGB image into a grayscale image, inputting the grayscale image into an MSCAViT model to obtain a third feature set, determining an optimal weight by using an OSCSO method through weighted fusion of the three feature sets, finally obtaining a fused feature, inputting a result feature into a decoding end of the MSCAViT, and finally obtaining a final image through the decoding end of the MSCAViT. According to the method, features finally used for image matching are generated, the model is evaluated through multiple indexes, compared with a traditional method, and the result shows that the proposed method shows a more excellent effect on solving the modal difference problem in a VI Re-ID task.

The invention provides an intelligent video monitoring method and system based on multi-source data fusion, and belongs to the technical field of video data analysis. Firstly, multi-source video data are obtained and preprocessed; inputting the preprocessed video data into a spatial-temporal feature coding and decoding network to obtain a preliminary result of pedestrian image abnormal behavior detection; based on the obtained preliminary detection result, pedestrian images in the abnormal behaviors are extracted, and abnormal behavior image data are obtained; inputting the obtained abnormal behavior image data into a feature recognition network to obtain a feature vector; and finally, inputting the obtained feature vectors into a cross-modal pedestrian feature registration network based on double-alignment feature embedding to obtain a final pedestrian image abnormal behavior detection result. The abnormal event detection method based on multi-source data fusion is efficient and stable, multi-source monitoring data can be fused, the method adapts to changes of different scenes and illumination conditions, and sudden abnormal behaviors in the monitoring scenes are recognized in time.

The invention provides a Seq2Seq-AM model construction method and a pedestrian trajectory prediction and safety risk early warning method. Firstly, a Seq2Seq-AM model is constructed, and the Seq2Seq-AM model is composed of an encoder module, an attention mechanism module and a decoder module; the encoder module extracts context information of an input feature sequence, the attention mechanism module performs weighting processing on input at an important moment, the decoder module outputs a variable-length prediction sequence, and the model can adaptively pay attention to key features in a pedestrian trajectory. Pedestrians in the scene can be predicted by using the Seq2Seq-AM model. According to the method, a crowd pressure index is introduced, pressure value ranges of three typical pedestrian motion states are calculated based on a pedestrian prediction result, safety risks in crowd motion are graded, and low, medium and high risk levels are divided. Finally, real-time risk early warning of the crowd gathering area is realized through risk level division, and safety risk assessment support is provided for safety management in high-density scenes such as major activities and transportation hubs.

To improve social safety by functioning as a crime prevention buzzer, predicting crimes, and transmitting real-time alerts to nearby pedestrians.SOLUTION: A system comprises: means for functioning as a crime prevention buzzer; preemptive correlation means in which criminal psychology, behavioral psychology, aura, and past crime data (news images(patterns)) are combined; and means for transmitting alerts to mobile devices of nearby pedestrians using information from satellites and big data.SELECTED DRAWING: Figure 1

The invention discloses an intelligent signal lamp phase optimization method and system based on vehicle infrastructure cooperation. According to the invention, through the dynamic game model, the system can sense multi-dimensional states such as traffic flow and pedestrian waiting time in real time, dynamically adjust demand weights of vehicles, pedestrians and roadside facilities, flexibly allocate green light duration, i.e., multi-direction multi-passing time of vehicles and multi-pedestrian time period to preferentially guarantee the street crossing demand; the average waiting time of vehicles and pedestrians is effectively shortened, the overall traffic efficiency of the intersection is obviously improved, and the suitability of daily traffic operation is obviously improved. The dynamic game model rapidly recognizes special requirements through a state value function, dynamically improves the weight priority of emergency vehicles or pedestrians, guarantees the efficient passing of key scenes (such as the rapid passing of emergency vehicles), avoids the long-time overstock of vehicles in other directions, and improves the safety of emergency vehicles or pedestrians. And the response capability and coordination level of the system to complex and emergent scenes are obviously enhanced.

The invention discloses a path planning method and device for a 5G nursing robot, and relates to the field of path planning. In the method, map information, first sensor data and first image data dynamically updated in a hospital are received in real time through a 5G network, and a three-dimensional space model in the hospital is constructed according to the map information and the first sensor data; analyzing the first image data to determine a human traffic mode and a static obstacle distribution condition in the hospital, and predicting a dynamic obstacle distribution state in a future preset time according to the human traffic mode and the static obstacle distribution condition; determining a preliminary path according to the three-dimensional space model, the obstacle distribution state and the emergency degree of the work demand; and in the process of moving according to the initial path, identifying the pedestrian and the movement trend of the pedestrian by using a second sensor and a second camera, and adjusting the initial path according to the movement trend. By implementing the technical scheme provided by the invention, the path planning efficiency and safety are improved.

The invention belongs to the technical field of vehicle control, and particularly relates to a vehicle-mounted image recognition and target detection system based on deep learning, and the system comprises a distributed monitoring module which collects the operation, obstacle and traffic signal information of a target vehicle through multi-modal classification and scene matching, completes the marking of a shielding region and the matching of information through the combination of shared data, and achieves the recognition of the target vehicle. Forming an enhanced monitoring set; the label planning module constructs an enhanced topological space based on the enhanced monitoring set, and adjusts moving tracks in different scenes by combining with vehicle and pedestrian track probability distribution fed back by dynamic intention recognition; the action recognition module predicts trajectory parameters and collision probabilities of non-target vehicles and pedestrians by using Bayesian and multi-modal algorithms; the decision-making module generates a real-time control instruction through particle swarm optimization and fuzzy control, and optimal control parameters are fed back through simulation; according to the invention, intelligent track planning and real-time control in a complex scene are realized, and the detection precision and control robustness of the shielded and label-free area are improved.

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

The invention discloses a long-time pedestrian re-identification method based on dual-path cooperation and key frame guided reconstruction. The method comprises the steps of firstly collecting a pedestrian video to be recognized, and extracting a video feature sequence; space and time position coding is introduced into the video feature sequence; capturing local fine-grained dynamic features through a local dynamic feature capturing path, and modeling long-range time sequence association through a cross-frame global feature modeling path; then, dual-path feature complementation is realized through bidirectional gating interaction; further screening out key frames, and realizing feature reconstruction through a full-frame attention propagation mechanism; and finally fusing the dual-path fusion features, the key frame guide reconstruction features and the refined features to generate pedestrian identity features. And processing pedestrian identity features to obtain standardized feature vectors, performing similarity comparison on the standardized feature vectors and pedestrian features in an image library, and returning a matching list. According to the method, video time sequence information is fully utilized, and the problem of insufficient robustness caused by appearance change in long-time pedestrian re-identification is effectively solved.

The invention relates to an automatic driving vehicle obstacle avoidance method and device based on TransFormer-APF-MPC and a medium, and the method comprises the following steps: obtaining pedestrian motion data and interaction scene data in real time, inputting the pedestrian motion data and the interaction scene data into an automatic driving vehicle obstacle avoidance network based on TransFormer-APF-MPC, and outputting an automatic driving vehicle obstacle avoidance scheme; the automatic driving vehicle obstacle avoidance network based on the TransFormer-APF-MPC comprises a TransFormer model and an APF-MPC obstacle avoidance model, the TransFormer model is used for predicting pedestrian trajectories according to input data, the APF-MPC obstacle avoidance model comprises a path planning layer and a trajectory tracking layer, the path planning layer is used for generating an optimal obstacle avoidance path by improving an artificial potential field method, and the trajectory tracking layer is used for tracking the optimal obstacle avoidance path. According to the improved artificial potential field method, the repulsive force between the vehicle and the obstacle and the gravitational force between the vehicle and the target point are calculated according to the improved repulsive force constant function with the adjustment factor introduced; and the trajectory tracking layer is used for calculating a vehicle control instruction through a model prediction control method, so that the vehicle runs along the optimal obstacle avoidance path. Compared with the prior art, the real-time obstacle avoidance planning and control of the vehicle can be better realized.

The invention discloses a target detection tracking method and device based on radar visual fusion perception, and a medium, and the method employs visual detection as a leading part to establish a radar visual fusion tracking module, and solves a problem that the detection precision, tracking stability and environment robustness are difficult to give consideration to the existing single-mode perception in a complex traffic environment at the same time. Through unified multi-modal fusion of distance and speed information of visual detection, visual tracking and millimeter wave radar, stable, continuous and reliable target identification and track output of targets such as pedestrians and vehicles under a low-computing-power platform are realized.