555 results about "Traffic accident" patented technology

The invention discloses a large transport intelligent line selection implementation method fusing a graph neural network and a large language model, and the method comprises the following steps: S1, constructing multi-source features to a unified high-dimensional vector space according to large transport historical data and real-time environment data; s2, constructing a structured prompt template through multi-modal input; s3, based on the structured prompt template, generating a candidate route set in combination with a large language model; s4, dynamically optimizing the candidate route set through a reinforcement learning framework, and outputting an optimal route. Compared with the prior art, the method has the advantages that intelligent generation and dynamic decision making of a large transportation route are realized through multi-modal feature fusion, graph structure semantic compression and large model reasoning optimization, and the transportation reliability, safety and economical efficiency can be remarkably improved; traffic accidents can be responded in real time to realize route adjustment, routes are dynamically optimized to reduce passage cost, planning quality is continuously improved through historical case learning, and safe and punctual delivery of overrun goods is guaranteed.

The invention relates to the technical field of intelligent traffic, in particular to a precise traffic accident early warning system based on multi-sensor fusion. Comprising a multi-modal data acquisition unit; the intelligent central processing unit is used for generating differentiated early warning instructions adaptive to different traffic participant types based on the three-dimensional risk decoupling result and the collision risk prediction result; a grading differentiation early warning execution unit; and an emergency linkage unit. According to the method, a meta-task sampling strategy is constructed through causal effect value grading and intersection topological feature clustering, edge lightweight adaptation is achieved in combination with gradient compression, parameter self-evolution is achieved through sliding window monitoring and knowledge distillation, and edge computing power and early warning precision are balanced. According to the invention, the multi-modal graded differentiated early warning terminals adapted to different traffic participants and a 5G edge low-delay public security and emergency linkage mechanism are matched, the early warning pertinence and the disposal practicability are quickly responded and improved, and the problem of insufficient edge adaptation and precision balance in the prior art is solved.

The invention relates to the technical field of artificial intelligence and intelligent traffic, in particular to a traffic accident liability intelligent affirmation method and system based on a multi-agent cooperation mechanism and a large language model. The whole process of credible information verification, responsibility affirmation reasoning and standard document generation is simulated. Wherein the legal expert agent adopts a competing mechanism, and the fairness and the accuracy of affirmation are improved through preliminary affirmation, double-party defense and final judgment. The reasoning ability of a large language model and accurate knowledge retrieval of a retrieval enhancement generation technology are fused, the real-time performance and accuracy of legal clause quotation are ensured, and a road traffic accident identification document conforming to specifications is automatically generated. The method effectively solves the problems that traditional manual identification is low in efficiency and high in subjectivity, and an existing intelligent method lacks interpretability and legal accuracy.

The invention provides a traffic accident scene reconstruction method and device, and relates to the technical field of three-dimensional reconstruction, and the method comprises the steps: obtaining a video frame sequence of a traffic accident scene collected by an unmanned plane, carrying out the information gain evaluation of the video frame sequence, and constructing a key frame set; according to the key frame set, macroscopic geometric reconstruction and microscopic normal recovery based on airborne light source dynamic change are carried out on the traffic accident scene, and a macroscopic depth map and a microscopic normal field are obtained; constructing a fusion optimization model with the macroscopic depth map as low-frequency constraint and the microscopic normal field as high-frequency gradient guidance, and fusing the macroscopic depth map and the microscopic normal field by using the fusion optimization model to obtain a fused depth map; and constructing an accident scene reconstruction model according to the fused depth map. By adopting the traffic accident scene reconstruction method and device, key details of the accident scene can be captured, the sensitivity to micromorphology is increased, and the accuracy of traffic accident scene reconstruction is improved.

The invention relates to a traffic accident scene live-action reconstruction method and device, equipment and a medium, and the method comprises the steps: carrying out the thinning optimization of a traffic accident scene image through employing a preset Douglas-Peucker algorithm, so as to generate a thinned and optimized traffic accident scene image; performing patch expansion and filtering on the sparse point cloud by adopting a preset PMVS algorithm to obtain dense point cloud corresponding to each traffic accident element; taking the dense point clouds corresponding to the traffic accident elements as geometric constraints, taking the thinned and optimized traffic accident scene image as a texture data source to construct a training data set, and training a preset neural radiation field model to be converged by adopting the training data set to determine a traffic accident scene three-dimensional model; and inputting a to-be-reconstructed traffic accident scene image into the traffic accident scene three-dimensional model to extract three-dimensional space information corresponding to each traffic accident element. According to the method, the accident scene investigation and modeling period can be greatly shortened, and the road blocking time is greatly shortened.

The invention provides a smart city traffic abnormity monitoring method and system based on an Internet of Things large model, and relates to the field of Internet of Things and smart city traffic management. The system comprises an abnormity judgment module and a diversion module. The abnormity judgment module is configured to perform abnormity judgment on the target area according to the multi-source data and determine a plurality of hidden danger hot areas; the diversion module is configured to determine a plurality of standby routes according to the judgment result and the regional road network topological map; determining a plurality of main routes according to the plurality of standby routes and the position information of the plurality of variable information boards, generating a diversion instruction, and sending the diversion instruction to the emergency supervision object platform; and based on the diversion instruction, controlling a plurality of variable information boards to display a sketch of a corresponding main route, and controlling traffic lights on a plurality of standby routes to perform green light signal display according to a passing period. According to the method, the main pushing route of the variable information boards can be reasonably determined and controlled, potential traffic accident hidden dangers are prevented, and traffic safety is guaranteed.

The invention relates to an expressway road condition monitoring system and method, and belongs to the field of expressway road condition monitoring, and the system comprises an ultrasonic sensor which is used for detecting whether branches grow into a guardrail of an expressway or not; the pressure sensor is arranged on the guardrail of the expressway and is used for detecting whether the guardrail of the expressway is bent or not; the image recognition module is used for detecting whether a traffic accident occurs in the target road section; the ESP32 single-chip microcomputer is connected with the temperature and humidity sensor, the ultrasonic sensor and the pressure sensor and gives an alarm when the branches grow into the guardrail of the expressway, whether the guardrail of the expressway is bent or not and a traffic accident happens to a target road section. According to the invention, through the strong operation processing capability and low power consumption characteristic of the ESP32, in combination with various sensors such as ultrasonic, pressure, temperature and humidity sensors of the image recognition module, complex road condition scenes can be accurately analyzed, and abnormal conditions such as traffic accidents can be timely captured, so that road condition monitoring is more comprehensive and meticulous.

The invention discloses a traffic abnormal event detection method based on multi-source data fusion, and the method comprises the steps: obtaining target detection data through a single millimeter wave radar and a plurality of industrial cameras according to the characteristics of a sensor; obtaining fusion information by associating the multi-source information; tracking the fusion target to form a target trajectory, and performing smooth filtering on the trajectory to form a filtered target trajectory; violation event detection is realized by analyzing a target track, wherein violation events comprise invasion, overspeed, underspeed, lane change, illegal parking, reverse driving and line pressing; traffic state event detection is achieved by analyzing the correlation between the lane-level detection area and target data, traffic state events include congestion, event time is aligned for event evidence leaving, and event videos and event photos are included; and for a specific event, event recheck is carried out through target track anti-shake verification, and event qualification and reporting are carried out. The traffic safety risk can be effectively and intelligently prevented and controlled, the traffic efficiency is optimized, and the traffic accident occurrence probability is reduced.

This application discloses a highway traffic diversion alarm broadcasting system and method based on AI recognition, relating to the field of highway diversion technology. This application analyzes the load of each vehicle and the driving experience, historical highway violations, and historical highway accidents of each driver to divert vehicles, diverting high-risk vehicles to safer highways, thereby ensuring vehicle safety. Different safe following distances and speeds are set for different highways to avoid traffic accidents caused by excessively close following distances or excessive speeds. Furthermore, the system monitors all vehicles traveling on each highway, broadcasting warnings when accidents or violations are detected, which enhances deterrence against drivers and prevents repeated violations from leading to traffic accidents.

The invention relates to the technical field of intelligent traffic and artificial intelligence crossing, and particularly discloses a traffic accident intelligent responsibility affirmation AI auxiliary method and system based on a large model, and the method comprises the steps: obtaining a responsibility judgment knowledge base, and processing the knowledge in the responsibility judgment knowledge base; key information of the traffic accident case is acquired, and occurrence passing information of the traffic accident case is generated according to the key information of the traffic accident case; through a large model, according to a responsibility determination knowledge base, the key information of the traffic accident case and the occurrence information of the traffic accident case, determining the wrong behavior and responsibility of the traffic accident party; and outputting error-passing behaviors and responsibilities of the traffic accident parties, and collecting actual responsibility affirmation results of the traffic accident parties to form complete feedback data of traffic accident cases. According to the traffic accident intelligent responsibility determination AI auxiliary method based on the large model provided by the invention, standardization, intelligence and high efficiency of a responsibility determination process can be realized.

The application discloses a risk road section prediction method and system, computer equipment and a storage medium, and relates to the technical field of road safety. The method comprises the following steps: acquiring street view image data of traffic accidents and accident-free road sections, inputting the street view image data of the traffic accidents and the accident-free road sections into a semantic segmentation model, outputting a predicted segmentation image, and obtaining the pixel proportion of different environmental elements of the road according to the segmented image; meanwhile, a risk road section prediction model is constructed based on an XGBOOST algorithm; the pixel proportions of different environmental elements are input into the risk road section prediction model, and the prediction probability of each street view image for an accident is output; the accuracy and stability of XGBOOST prediction are enhanced by deeply analyzing the pixel proportions of various objects in the visual environment of drivers, the risk of local transportation lines is accurately evaluated and analyzed in terms of environmental elements, and the method has a wide application prospect.

The application discloses a vehicle braking method, device, equipment and storage medium. In the process of driving a target vehicle, if the state of a traffic signal at a road intersection changes to prohibition of passing after a first preset time period, a first distance between the target vehicle and a stop line of the intersection is determined. If the first distance is not less than a first preset threshold, a first braking force is determined to brake the target vehicle. If the first distance is less than the first preset threshold, it is determined whether the rear wheel of the target vehicle can cross the stop line of the intersection within the first preset time period without changing the current speed. If yes, a continue driving signal is sent to the target vehicle. If no, a second braking force is determined to brake the target vehicle. The scheme can brake or pass the intersection in time when the vehicle encounters a limit situation, such as the traffic signal changing to prohibition of passing immediately, effectively reduces the phenomenon of vehicle running a red light, and can also avoid traffic accidents caused by the vehicle not reacting in time.

The invention discloses an intelligent networking driving recording system based on multi-mode perception. The intelligent networking driving recording system comprises a perception layer, a processing layer, an application layer and a block chain evidence storage module. The invention further discloses a data tamper-proofing method of the intelligent networked driving recording system based on multi-mode perception, which comprises the following steps: S1, synchronously acquiring thermal radiation data, radar point cloud data and video image data in a driving environment through the infrared thermal imaging sensor, the 4D millimeter wave radar and the high-definition optical camera; s2, infrared thermal imaging data are optimized, radar point cloud clustering is optimized through a DBSCAN algorithm, and target tracking is achieved based on UKF. The system can cover the whole temperature range of-30 DEG C to 85 DEG C and severe weather such as rain, snow and haze, effectively reduces the false alarm rate of infrared thermal imaging and the missed detection rate of a millimeter wave radar static target, avoids the loss of accident key evidence caused by environmental factors, greatly improves the evidence obtaining reliability at night and in severe weather, and reduces traffic accident disputes.

The invention discloses a mountainous area expressway secondary accident active prevention and control method and system, and the method comprises the steps: carrying out the global monitoring of a mountainous area expressway continuous slope road section, extracting traffic scene features, and identifying an accident scene; in combination with the current traffic flow state, the road static characteristics, the environmental conditions and the vehicle behaviors, estimating the initial accident occurrence risk based on an accident risk estimation model of risk coupling; the method comprises the following steps: predicting whether a secondary accident of the continuous slope bending of the expressway occurs or not based on a Bayesian optimization random forest model, if so, predicting the occurrence time and place of the secondary accident, and then performing active prevention and control through a warning means and a forced control means. According to the invention, through air-ground integrated monitoring and intelligent control, rapid detection of a primary traffic accident and accurate assessment and active prevention control of a secondary accident risk are realized, so that the traffic accident frequency and damage degree of a high-risk road section are greatly reduced.

The road construction vehicle-mounted early warning system comprises a radar sensing module, a first communication module, a controller module and a warning module, the output end of the radar sensing module is electrically connected with the input end of the first communication module, and the radar sensing module is used for detecting the running speed and vehicle distance data of a vehicle; the output end of the first communication module is electrically connected with the input end of the controller module, and the first communication module is used for transmitting the detected vehicle running speed and vehicle distance data to the controller module to screen abnormal data; the output end of the controller module is electrically connected with the input end of the warning module, and the controller module is used for carrying out early warning and reminding on a vehicle corresponding to the abnormal data; the driving speed and the vehicle distance data of the vehicle are monitored in real time through the radar sensing module, the detected data are transmitted to the controller module to be processed, the warning module carries out acousto-optic reminding on the vehicle with abnormal data, the potential collision risk can be found and reminded in time for a driver, the occurrence rate of traffic accidents is reduced, and the safety is improved.

In order to at least partially automatically control the braking of a vehicle (1), object data containing an object class (9) of an object (6) in an environment of the vehicle (1) is generated or received by means of a data processing system (3) of the vehicle (1). By means of the data processing system (3), environment data (10) which contains statistical traffic accident data relating to the environment and / or relating to environment conditions of the environment is generated or received. By means of the data processing system (3), control data relating to a braking intensity for a desired braking maneuver of the vehicle (1) is determined according to the object class (9) and the environment data (10), and at least one control signal for at least one brake actuator of the vehicle (1) is generated according to the control data.

The invention discloses a traffic scene simulation method based on an accident report, and aims to solve the problem of insufficient complex dynamic scene data in traffic simulation. According to the method, physical clues (such as vehicle speed, collision angle, quality and the like) and context information (such as road types and environmental conditions) are extracted from a real traffic accident report, and a high-fidelity traffic scene data set is generated. In foreground processing, the extracted physical clues are used for accurately constructing a vehicle motion trajectory, diversified dynamic traffic scenes are generated through a pre-collision trajectory planning algorithm, and trajectory prediction is optimized in combination with a large language model to ensure that the real physical law is met. In background processing, high-quality three-dimensional scene reconstruction is carried out according to environment information in an accident report, a vivid background image is generated, and the consistency of a visual angle and illumination is kept. And finally, through fusion of the foreground vehicle track and the background image, a visual and physical double real traffic simulation video is generated. The method significantly improves the diversity and authenticity of traffic scenes, reduces the dependence on real data collection, is widely suitable for high-fidelity traffic simulation scenes such as automatic driving test and traffic flow analysis, and effectively promotes the progress of the traffic simulation technology.

To provide a road that allows a vehicle that should be entering the correct lane to enter the wrong lane to return to the correct lane smoothly and safely, reduces congestion, delays, and resulting traffic accidents for vehicles traveling on both the correct and wrong lanes, and maintains a high traffic flow rate. [Solution] The road structure for guiding vehicles traveling in the wrong direction according to the present invention has a first double-lane road (10), a second double-lane road (20), and a central median (30), A predetermined central bypass road (40) is provided in the aforementioned central median (30). It is characterized by having a fence (50) to guide vehicles traveling in the wrong direction and / or a fence (60) to guide vehicles traveling in the right direction.

The invention discloses an unbalanced discrete data generation method fusing causal constraints and potential variables, and the method comprises the steps: firstly collecting environmental factors and accident severity of a road traffic accident occurrence point, recording the environmental factors and accident severity as road traffic accident data D, building a causal graph G of the road traffic accident data D, and obtaining an adjacent matrix A; secondly, performing encoder mapping calculation on the original data feature X through a variational auto-encoder VAE to obtain a potential variable z; taking the potential variable z and the category label Y as the input of a WGAN-GP network generator to obtain a virtual potential variable, and distinguishing the potential variable z and the virtual potential variable in combination with the authenticity score Di output by a discriminator to obtain a high-fidelity potential variable; and finally, taking the high-fidelity potential variable as the input of a VAE decoder, and generating few-class sample synthesis data in combination with causal constraints. According to the invention, the urban traffic accident data monitoring quality can be improved, and the road safety management and decision-making accuracy can be improved.

The invention discloses a 4D traffic accident scene reconstruction method based on an automobile data recorder video, and belongs to the technical field of intelligent traffic. According to the technical scheme of the invention, the method comprises the steps: inputting an automobile data recorder video, extracting video frame features through a pre-trained geometric basis large model DUSt3R, carrying out the frame-by-frame reconstruction and optimization of a 4D scene containing a static background and a dynamic object through the combination of a 3D Gaussian reconstruction technology and dynamic object motion modeling, and finally generating an accident reconstruction video which can be observed at any visual angle. The method solves the problems that the reconstruction scene generalization ability is weak and the static background appearance cannot change along with time in the prior art, realizes the high-precision and high-reality reconstruction of the traffic accident process, and is suitable for the fields of traffic management, accident liability affirmation and the like.

Video streaming from the first external electronic device, such as the vehicle image acquisition device according to various embodiments, may be processed based on a designated state (e.g., a traffic accident) identified by the first external electronic device. For example, when receiving a notification related to a designated state from the first external electronic device, the electronic device may acquire information related to the designated state and then broadcast the obtained information to one or more terminals (e.g., terminal 130 of FIG. 5), through a second external electronic device such as a navigation server. In response to the broadcasting, the terminal may provide information related to the designated state to the user of the terminal. The information may be used to change a navigation path provided by the terminal.

The utility model relates to the technical field of road monitoring, in particular to an expressway collapse real-time monitoring and early warning system based on distributed optical fibers, which comprises the distributed optical fibers laid on two sides of an area to be monitored of an expressway along the length direction; an optical fiber sensing monitor; and the optical fiber sensing monitor is connected to the distributed optical fiber and is used for transmitting an optical carrier signal to the distributed optical fiber. According to the utility model, on the basis of the distributed optical fiber sensors, monitoring and early warning are carried out on easy-to-collapse road sections of the expressway, once the road collapse is monitored, the collapse position can be positioned in real time, and the acousto-optic indication component in the corresponding area generates an alarm signal to warn drivers in the current road area, so that the driving safety of the expressway is improved. A certain time and space are reserved for effective danger avoiding of vehicles coming behind, and the probability of traffic accidents occurring in the neutral period before the site is blocked after collapse can be reduced.

1. Name of the product in this design: Integrated Emergency Command Vehicle. 2. Application of this design: This product is used for on-site dispatching, communication command and emergency operations of drones. It can be equipped with a drone lifting platform and is suitable for scenarios such as major events, traffic accidents and severe weather. 3. The key design features of this product are the combination of shape and pattern. 4. The image or photograph that best illustrates the design's key points: the right view. 5. The bottom surface is not commonly seen during use, so the bottom view is omitted.

The invention provides a traffic road ghost probe warning lamp, and relates to the technical field of traffic signals. The warning lamp comprises a detection device, a warning device and a control device. The detection device comprises at least two groups of laser correlation sensors, an ultrasonic distance measuring sensor and a networking monitoring camera, and is used for detecting approaching conditions of pedestrians, non-motor vehicles and vehicles at an intersection. The warning device comprises an LED lamp strip, a full-color LED dot matrix display screen and a motor vehicle display screen and is used for carrying out real-time early warning on passing vehicles and pedestrians. The control device comprises a control circuit board and a power management circuit. According to the invention, through a plurality of sensor fusion technologies, accurate detection of pedestrians and vehicles at the intersection is realized, and real-time early warning is carried out on passing vehicles and pedestrians by using the LED lamp strip and the display screen, so that traffic accidents caused by a ghost probe phenomenon are effectively prevented, the safety and traffic efficiency of the traffic intersection are improved, and accident risks are reduced.

The invention relates to the technical field of expressway safety warning, in particular to a segmented flashing warning lamp system triggered at any point and a dynamic indexing method.The system comprises a central monitoring layer, a core control layer, an area control layer and a field equipment layer, and a PLC of the core control layer numbers warning lamps in sequence; after any trigger point i is triggered, the index 300 adjacent warning lamps are turned on, and the specific index number can also be set according to site requirements. The flash is divided into three sections according to the number and is divided into six times per second, four times per second and two times per second, indexes are updated for new trigger points in the same range, warning sections are newly added for new trigger points in different ranges, and resetting can be carried out after faults are removed; according to the segmented flicker warning lamp system triggered at any point and the dynamic indexing method, the first-time warning of an accident can be achieved, a driver can judge the distance from the accident point through the flicker frequency and rapidly brake, major serial traffic accidents are effectively restrained, casualties and economic losses are reduced, and the segmented flicker warning lamp system is suitable for multiple accident scenes and high in reliability.

The embodiment of the present disclosure provides a kind of automatic driving test scene extraction method based on deep embedding clustering and medium, belong to data processing technical field, specifically include: to real traffic accident is deep accident deconstruction and obtains accident data, reconstructs the accident data collected, and extracts the pre-collision trajectory sequence of accident participant;Pre-collision trajectory sequence and static environment information are combined, and pre-collision trajectory matrix is created;With pre-collision trajectory matrix as input, dimension reduction is processed using heap auto-encoder, and low-dimensional embedding feature is obtained;Latent feature is used as input variable, clustering is carried out using k-mean algorithm, and multiple class feature cluster is obtained;According to the label in each feature cluster, backtrack pre-collision trajectory matrix, obtain the scene information of each cluster and summarize the scene commonality of each cluster, obtain the scene description corresponding to each typical accident class.By the scheme of the present disclosure, the implantability and adaptability of typical scene are increased.

The invention provides a vehicle anti-collision method and device, a vehicle and a medium, the method is applied to a first vehicle, and the method comprises the steps that vehicle information of a second vehicle is acquired under the scene that obstacle shielding exists between the first vehicle and the second vehicle, and the vehicle information at least comprises vehicle positioning information; performing scene positioning based on the vehicle information of the second vehicle to obtain relative position information of the second vehicle relative to the first vehicle; and carrying out anti-collision pre-judgment processing based on the relative position information. In this way, the technical problem that in the prior art, due to the fact that the vehicle shielded by the obstacle cannot be recognized, traffic accidents occur can be solved.

A system is provided.SOLUTION: A system for predicting traffic accidents, the system comprising: means for generating a prediction map of traffic accidents; means for analyzing the prediction map using a generative AI model to generate a prediction of traffic accidents; means for plotting the prediction map on a map; and means for distributing the generated prediction map to a plurality of devices in real time.SELECTED DRAWING: Figure 1

The utility model relates to the technical field of vehicle suspensions, in particular to an air suspension lifting device which comprises an air bag, an installation part, a telescopic assembly and a connecting assembly, the installation part is connected with a vehicle frame, the telescopic assembly and the installation part are connected through the connecting assembly, and a containing space is formed after the telescopic assembly and the installation part are connected. The air bag is arranged in the containing space, one end of the air bag can abut against the installation part, the air bag is configured to change the height of the air bag along with the change of the inflation volume, and the installation part can cooperate with the frame to move along with the change of the height of the air bag and drive the telescopic assembly to move so as to change the height of the containing space; by the adoption of the air suspension lifting device, the air bag can be protected, traffic accidents caused by damage of the air bag in the driving process of an automobile are avoided, and the safety performance of the automobile is further improved.

This utility model relates to the field of traffic marking vehicle technology, and in particular to a traffic marking vehicle nozzle anti-clogging and flow guiding device with self-cleaning function. It includes a mounting plate and a first motor. The first motor is fixedly connected to the mounting plate, and a drive shaft is fixedly connected to the output end of the first motor. The first motor drives the drive shaft to rotate. The drive shaft is rotatably connected to the mounting plate. A drive gear is fixedly connected to the drive shaft, and a driven gear is meshed with one side of the drive gear. A fixed shaft is fixedly connected to the middle of the driven gear and is fixedly connected to the mounting plate. This utility model, through the combination of a quick-switching structure for two nozzle bodies and a self-cleaning component, significantly reduces downtime for replacement and automatically cleans residual paint from the nozzles, preventing clogging, extending equipment lifespan, preventing paint drying or mixing contamination, ensuring that the color and thickness of each line marking meets standards, improving the durability of the markings, and reducing the risk of traffic accidents.