7139results about "Anti-collision systems" patented technology

The invention discloses a road intelligent induction and dynamic early warning method and system integrated with meteorological perception, and relates to the technical field of intelligent traffic and road safety. The method comprises the following steps: acquiring real-time weather, traffic and road data, performing multi-source data fusion by adopting an improved Kalman filtering and attention mechanism, and generating unified state estimation; dynamic risk assessment is carried out in combination with Bayesian reasoning and a Markov model, and speed-limiting adaptive adjustment is realized based on safety, traffic efficiency and energy consumption multi-objective optimization; and further calculating the length and position of the dynamic early warning area, and controlling devices such as intelligent spikes to issue induction information. The system comprises a data acquisition unit, a fusion estimation unit, a risk prediction unit, a speed adjustment unit, an early warning calculation unit and an induction unit. According to the invention, real-time monitoring, risk prediction and intelligent regulation and control of the road traffic environment in complex weather are realized, and the driving safety and the traffic efficiency are improved.

The present invention relates to a warning system and in particular to a warning system that is movable to a monitoring zone. The invention has been developed primarily for use for early notification of danger by approaching vehicles to a monitored zone such as an emergency in an emergency lane of a roadway. The warning system of the invention comprises at least two forms of detectors for detecting and tracking vehicles and obtaining characteristics of the tracked vehicle relative to the monitored zone a determinator for receiving the characteristics of the tracked vehicle relative to the monitored zone and assessing the expected relative impact of the vehicle on the monitored zone and an alarm system for providing an alarm action according to a predetermined danger of the assessed expected relative impact of the vehicle to the monitored zone.

The invention relates to the technical field of traffic safety, in particular to a beforehand prevention and control strategy generation method for traffic safety risks of a highway network in a mountainous area. Comprising the following steps: risk diagnosis and data preparation: collecting road basic information, historical traffic flow data, meteorological data and accident-prone point data of a mountainous area expressway network; constructing a risk assessment model: selecting road alignment, traffic flow, weather and environmental toughness indexes, determining index weights by adopting an improved analytic hierarchy process, constructing the risk assessment model through a fuzzy comprehensive evaluation method, and calculating the traffic safety risk level of each road section; a prevention and control strategy is generated; and strategy verification and iteration. According to the method, the total factor evaluation model is constructed by integrating the road network topological structure, the traffic flow characteristics and the meteorological sensitive section data, so that the problem of one-sided risk identification caused by single factor analysis in the prior art is solved, and systematic description of the mountainous area highway composite risk scene is realized.

The invention discloses a multi-agent traffic event real-time response system and method, and the system comprises an event perception fusion module which is disposed on vehicle-mounted and roadside equipment, fuses the vehicle motion state, environment perception data and meteorological data, and generates an unstructured traffic behavior set in real time through a lightweight recognition model; the edge decision-making module is used for defining decision-making responsibilities by adopting a dynamic role binding mechanism and decomposing tasks based on a hierarchical memory storage architecture to generate response instructions; the central collaborative decision-making module is used for realizing strategy decoupling through role adapter layering and coordinating cross-domain response measures by adopting a three-level conflict resolution mechanism; and the strategy optimization module updates center parameters through federated learning and optimizes a response strategy in combination with a reinforcement learning reward model, so that second-level identification and dynamic response of traffic events can be realized, the congestion dissipation time is shortened, the false report and missing report rate is reduced, the secondary accident rate is reduced, and the disposal efficiency and safety of complex traffic scenes are comprehensively improved.

The invention relates to an automatic driving risk quantification method based on a conflict risk field. Comprising the steps of 1, constructing a basic risk field; step 2, under a basic risk field framework, constructing a conflict risk field by taking an ADV as a center; step 3, regarding the conflict risk field as a repulsive force acting on the ADV, and using the repulsive force to quantify the dynamic influence of the environmental elements and various TP states on the driving safety of the ADV; the method comprises the following steps: firstly, constructing a basic risk field on the basis of traffic vehicle distribution and motion characteristics, further forming a vehicle forward, lateral, backward and traffic regulation constraint multi-dimensional conflict risk field on the basis, and converting an abstract traffic conflict relationship into a computable repulsive force model; dynamic quantitative characterization of driving risks in a state dimension, a space dimension and a time dimension is realized, and continuous optimization and evolution of an end-to-end automatic driving algorithm are supported by means of a repulsive force model constructed based on a conflict risk field.

A vision language model (VLM) may be used to evaluate signs that designate restricted or toll lanes, determine whether it is permissible (and / or the cost) to merge into a restricted or toll lane, and / or determine when to merge out of a restricted or toll lane based on the cost. Frames from one or more (e.g., front-facing) camera(s) may be evaluated for applicable signs (e.g., using a sign recognition DNN or a VLM). If detected, the (e.g., cropped) image of the sign may be provided as input to a VLM with a textual prompt instructing the VLM to determine whether to drive in the restricted or toll lane (e.g., whether it can be taken within budget) and / or what the cost would be. The generated response may be provided to an ADAS to trigger an initiation of a merge left or right or a determination to stay in the current lane.

The invention relates to a road section risk early warning method based on thunder-vision fusion perception, and the method comprises the following steps: S1, obtaining the high-precision space-time position information of a vehicle in a tunnel through the multi-source information fusion of a millimeter-wave radar and machine vision; s2, mapping the vehicle position under the global coordinate system to a coordinate system expanded along a road center line, and representing the vehicle position as a longitudinal projection position and a transverse offset along the road direction; s3, under the coordinate system, taking anti-collision time as a core index, combining lane judgment and relative speed, constructing a dynamic longitudinal risk grading model, and performing linkage triggering with a warning system; s4, constructing a regional risk integral model; and S5, speed limiting information is issued to all driving-in vehicles in real time through the variable speed limiting screen, and the rear-end collision risk is actively prevented and controlled. According to the method, the sudden risk response time can be shortened, the speed limit control response precision can be improved, the rear-end collision rate of the diversion and convergence areas can be reduced, false alarm interference can be reduced, and the tunnel driving safety and the information guiding efficiency can be improved.

A warning system according to the present disclosure includes: a character information generation unit that generates, by using a captured image of the inside or the outside of a mobile body, character information indicating a character representing an event appearing in the captured image; and a warning information notification unit that issues a notification of warning information corresponding to the character information generated by the character information generation unit. In a warning method according to the present disclosure, a computer: generates, by using a captured image of the inside or the outside of a mobile body, character information indicating a character representing an event appearing in the captured image; and issues a notification of warning information corresponding to the generated character information.

The invention discloses a blind area monitoring and lane changing auxiliary intelligent safety detection system. The system comprises a data acquisition subsystem, a risk calculation subsystem and a lane changing early warning subsystem. The data acquisition subsystem acquires speeds and collision distances of parallel vehicles in dead zones right behind and on two sides of a vehicle through millimeter-wave radars, the camera module acquires front image information, the data preprocessing module fuses multi-source data, and the environment sensing module adjusts a collision threshold according to illumination, rainfall and the like. And the risk calculation subsystem calculates a collision risk value by using a dynamic threat integral and a space threat potential field model, predicts a lane changing trajectory through an LSTM neural network and optimizes risk pre-judgment in combination with a driver intention. And the lane changing early warning subsystem triggers early warning in a grading manner through a multi-modal output unit based on the collision risk value and the lane changing conflict probability. According to the system, through multi-sensor fusion and an intelligent algorithm, blind area monitoring accuracy, environmental adaptability and driver response efficiency are improved, and lane changing safety is guaranteed.

The invention discloses a deep-sea mining system mine car out-of-limit early warning method, which realizes synchronous real-time acquisition of information such as position movement of a mining car and a support ship through cooperative positioning of an ultra-short baseline (USBL), a GNSS (Global Navigation Satellite System), a gyrocompass and an INS (Inertial Navigation Satellite System); meanwhile, based on a multi-parameter constraint model of umbilical cable tension, curvature and mine car upsetting moment, a mining vehicle safety operation area is established; a safe operation area of a mining vehicle is built in a system early warning program to define the safe operation area, and real-time evaluation and analysis are carried out on movement position information of the mining vehicle and a support ship obtained by multi-source cooperative positioning and the safe operation area, so that visual evaluation and visual early warning of the safe state of the mining vehicle in deep-sea mining operation are realized. And in combination with a high-frequency data refreshing mechanism, the mine car out-of-limit risk is quickly early warned, and the release length of the umbilical cable and the movement of the mining vehicle are dynamically regulated in an auxiliary manner, so that the efficient and safe management and control target of deep-sea mineral mining is achieved, and the accident probability is remarkably reduced.

The invention discloses a curve driving collision early warning method and system based on multi-risk field fusion, and the method comprises the steps: 1, collecting vehicle dynamics data in real time, and carrying out the short-time prediction; step 2, geometric modeling and rasterization of a road; 3, constructing a geometric risk field; 4, constructing a kinetic energy risk field; 5, constructing a behavior risk field; step 6, constructing a comprehensive risk field; step 7, collision risk judgment; and step 8, generating an active intervention strategy. Collaborative perception and risk prediction of vehicles, roads and driving behaviors are realized through multi-risk field fusion, and potential collision hidden dangers of curve driving are identified in advance; a vehicle dynamics model and risk correction based on road adhesion are introduced to realize accurate risk assessment of the vehicle; multi-time window trajectory prediction and behavior modeling are adopted, so that the recognition capability of complex driving behaviors is improved; a closed-loop system from risk prediction and decision making to active control is realized, and the safety and stability of the vehicle driving in the curve scene are effectively improved.

The invention relates to the field of traffic control, in particular to an intelligent network connection inductive control platform for road traffic safety facilities. The traffic data acquisition module is used for acquiring a data flow of a traffic sensor and outputting traffic data with a traffic data source identifier through an identification technology; the traffic data processing module is used for obtaining standardized traffic parameters according to a traffic data source identifier adaptive analysis protocol; evaluating a data reliability index according to the index system; the situation fusion module is used for forming a vehicle driving track through a graph neural network, generating a global traffic situation map by using a data reliability index, and constructing a traffic network digital twinborn model; and the decision and control module is used for analyzing traffic states from the traffic network digital twin model, predicting traffic events and collision risks and generating traffic control instructions. According to the platform, through the equipment fingerprint and protocol reverse technology, an information island is broken, the comprehensiveness and high credibility of a data source are ensured, and the road traffic safety and passing efficiency are remarkably improved.

The invention provides an automatic driving vehicle lane changing intelligent decision-making method based on multi-dimensional evaluation, and the method comprises the steps: obtaining the environment information of a passing road section of an automatic driving vehicle queue through advanced sensing equipment and technology, constructing an optimization decision-making space in an automatic driving lane changing vehicle target queue, and carrying out the optimization decision-making space; and determining a candidate set where the lane changing vehicle is inserted into the target queue. And then, selecting a sine curve and model prediction control (MPC) to carry out track planning and control on the lane-changing vehicle, and traversing in sequence to obtain tracks and control information of the lane-changing vehicle inserted into each candidate position. Then, calculating the influence of the lane changing vehicle at each candidate position on four dimension indexes of operation safety, stability, efficiency and smoothness of the original queue and the target queue of the lane changing vehicle, dynamically updating the weights of the four dimension indexes according to road environment information, and carrying out weighted calculation to obtain a comprehensive score; finally, after traversal is finished, the comprehensive scores are compared, the insertion position with the highest score and the trajectory planning and control decision of the insertion position are selected as the optimal lane changing strategy to complete lane changing, the queue safety is guaranteed, and the passing efficiency is improved.

An assistance system includes: an augmented reality module configured to i) determine a path of a host vehicle, ii) detect one or more objects with one or more trajectories predicted to interfere with the path of the host vehicle, and iii) based on the one or more trajectories, generate a first digital gateway indicative that the host vehicle should yield for the one or more objects; a display control module configured to control an augmented reality head-up display to display the first digital gateway in a closed state over an environment forward of the host vehicle and along the path of the host vehicle; and a vehicle control module configured to yield to the one or more objects as the host vehicle approaches the first digital gateway.

The invention discloses an emergency vehicle rescue control method under a virtual emergency special lane, and solves the problems that an emergency vehicle is difficult to pass in time due to traffic jam, the lane changing efficiency is low, and the path interference factor is complex in the existing traffic environment. The method comprises an emergency vehicle rescue dynamic control model; establishing a simulation scene, deploying a model and performing training; vehicle and road information is obtained, an emergency vehicle lane changing strategy is obtained according to the emergency vehicle autonomous lane changing decision and the optimal emergency lane selection model, and an optimal virtual emergency lane is selected; and guiding social vehicles to give way according to the lane dynamic emptying frame model. According to the invention, lane traffic density is monitored, an emergency vehicle rescue control strategy is triggered, an emergency vehicle lane change decision is optimized by using an emergency vehicle autonomous lane change decision model, an optimal virtual emergency lane is dynamically selected based on an optimal emergency lane selection model, social vehicles are guided to give way through a lane emptying frame, and rapid passing of the emergency vehicles is ensured.

The invention discloses a vehicle collision detection, description and early warning system and method based on a driving video, and belongs to the technical field of artificial intelligence and intelligent traffic safety. According to the system, on the basis of a vision-language model, vehicle-mounted videos such as an automobile data recorder are automatically analyzed, and detection, severity grading, natural language description generation and real-time early warning of vehicle collision events are achieved. The system extracts high-dimensional semantic features of video frames through a CLIP model, focuses key information through an attention weighting network, inputs the key information into a deep classification network composed of a plurality of full connection layers, a batch normalization layer and an activation function, and outputs a multi-level accident severity classification result. Integrating target detection and environment information, and generating a structured accident description text by using a fine tuning BART model; a sliding window and time sequence modeling mechanism is adopted, and recognition and early warning of the pre-collision state are achieved. The method effectively solves the technical problems of lack of semantic understanding, inaccurate severity judgment, incapability of early warning and the like of a traditional method, has high accuracy, high interpretability and real-time response capability, and is suitable for intelligent driving assistance and traffic safety monitoring scenes.

The invention relates to the technical field of parking lot digital management, and discloses a digital twin enabling parking lot digital global management system. According to the system, a global digital twin model of a parking lot is generated, and multi-source sensing data such as parking space states, vehicle identities and environmental parameters are fused; generating a parking space distribution matrix with priorities and time windows in combination with the parking request of the user and the real-time parking space state; according to the parking space two-dimensional code hardware parameters, the digital twin model and the parking space distribution matrix, outputting a vehicle guiding path set with a timestamp; a real-time obstacle avoidance and dynamic adjustment system is constructed to generate a safe driving path set so as to dispatch vehicles; outputting a power distribution control instruction set and updating the digital twinborn model by combining the electric quantity of the energy storage unit, the power distribution network data and the safe driving path set; and finally, system knowledge evolution and end-to-end closed-loop optimization are realized through scheduling execution logs. According to the system, global efficient management of the parking lot is realized, and the parking space utilization, vehicle scheduling and energy utilization levels are improved.

A method, device and system for active management and control of road traffic safety are disclosed. The method includes acquiring traffic data of a target road in real time, wherein the target road includes management and control sections; for each management and control section, judging whether there is a traffic accident according to the acquired traffic data; if so, formulating an emergency management and control strategy; if not, extracting traffic flow data from the traffic data, and generating predicted traffic flow data according to the traffic flow data by a traffic flow prediction model; generating a risk level according to the predicted traffic flow data by a risk prediction model; determining the current active management and control strategy of the management and control section according to the risk level and the predicted traffic data; and issuing the corresponding management and control strategy of each of the management and control sections.

The invention discloses an intelligent road traffic risk early warning method and system based on vehicle-road cooperation, and relates to the technical field of traffic early warning, and the method comprises the steps: unifying real-time collected road data to a UTM coordinate system through combining time alignment and coordinate conversion, and constructing standardized spatio-temporal data; based on the standardized spatio-temporal data, using a hypergraph neural network to construct a dynamic hypergraph, obtaining node risk features, and generating a comprehensive risk index; according to the risk index feature weight distribution vector, risk root cause probability distribution is obtained through intervention calculation, and a risk level and an early warning type are output; real-time road data are safely aggregated through federal learning, and the dynamic hypergraph is updated in combination with differential privacy. According to the method, the dynamic hypergraph is constructed, the hypergraph neural network is used for capturing the high-order interaction relation between the vehicles, the problem that complex space-time interaction modes between the vehicles are difficult to capture is solved, and the extraction efficiency of node risk features is improved.

The invention provides a vehicle-road cooperation method and system, and the method comprises the steps: S1, obtaining original road side data and original vehicle end data which are reported by a road side and a vehicle respectively, and carrying out the standardization processing; and S2, carrying out sensing fusion on the standardized roadside data and the vehicle end data to obtain fused sensing data, carrying out sensing calculation by using the sensing data, and meanwhile, re-engraving the original vehicle end data, the original roadside data, the fused sensing data and a sensing calculation result. And S3, acquiring duplicated data in real time, performing quasi-real-time scene calculation in combination with the signal lamp, the roadside equipment and the vehicle end equipment, and acquiring and duplicating a quasi-real-time scene calculation result. And S4, issuing the quasi-real-time scene calculation result to the vehicle end or the roadside, and sending an early warning signal to the vehicle end or controlling roadside equipment. According to the invention, accurate replication and dynamic early warning of the vehicle-road data are realized, the traffic efficiency and the driving safety are improved, and the reliability and expansibility requirements of a large-scale vehicle-road collaborative network are met.

The invention provides a blind area early warning method and device and a storage medium, and the method comprises the steps: carrying out the multi-dimensional recognition of a vehicle road scene, obtaining the data of a plurality of sensors, determining the environment type of the road scene where a vehicle is located, adjusting the original weight of each sensor based on the environment type, obtaining a first weight corresponding to each sensor, and carrying out the adjustment of the first weight. Through recognition of environment types, weights are dynamically distributed based on indexes related to environment scenes in a non-linear mode so as to adapt to complex scenes. The sensor data is fused according to the first weight, the influence of the data source with low confidence on the fusion processing is dynamically reduced, blind area early warning is performed based on the fusion result, and the false alarm rate is effectively reduced. In addition, according to dynamic weight adjustment of the indexes related to the environment, the response speed of system execution control is increased.

A system for detecting the motion state of a vehicle behind a mobile maintenance vehicle based on thunder-vision fusion belongs to the technical field of traffic intelligent control and comprises a multi-source sensing module, a target detection module, a trajectory tracking module and a vehicle motion state modeling module. A structured state output module performs standardized integration on vehicle target trajectory information provided by a detection and tracking module based on a unified data expression model to form a multi-dimensional structured data format including fields such as a relative distance, a relative speed, an acceleration, a vehicle type, a lane number, a frame timestamp and the like; consistency analysis and downstream calling can be conveniently carried out among different systems, the problem that a traditional detection system is difficult to integrate due to lack of a structured interface is avoided, the system supports flexible butt joint with a road sensing platform, a vehicle-mounted control system and an upper-layer data analysis module, good expansibility and cross-platform adaptation capacity are achieved, and the system is suitable for being popularized and applied. And the engineering availability and deployment efficiency of the traffic flow state information are obviously improved.

The invention relates to the technical field of traffic management, in particular to an urban road intersection passage intelligent optimization method based on multi-modal information, which comprises the following steps: acquiring real-time sensing data of pedestrians and non-motor vehicles through a video camera, a millimeter wave radar and a laser radar, and generating fusion sensing data through timestamp synchronization and coordinate system unification; identifying and generating a target list with category labels by using a detection and clustering algorithm, and obtaining a stable motion trail and intensity by combining with multi-target tracking; predicting a crossing intention and a path based on time sequence deep learning, calculating an interleaving point and quantifying a conflict risk; and according to a comparison result of the conflict risk coefficient and a threshold value, generating a strategy control instruction of different time periods, different paths or a mixed mode, and in combination with execution time window information, forming an optimized timing scheme through cooperative execution of an intelligent prompt identifier, a telescopic isolation belt and a signal control machine, so as to realize cooperative passage. The method improves the recognition precision, reduces the conflict risk, and improves the passing efficiency.

The invention provides a method and a device for controlling and testing formation driving of autonomous vehicles. The method comprises the following steps: acquiring current scene features based on a sensing device configured on a road side unit and / or an autonomous vehicle; the current scene feature is matched with a preset scene library, target formation control information is obtained, the preset scene library stores a mapping relation between multiple groups of scene features and formation control information, and the target formation control information comprises pilot vehicle control information and control information corresponding to at least one following vehicle; and based on the target formation control information, controlling the leading vehicle and the following vehicle to perform formation driving. According to the method and the device, the formation scene of the automatic driving vehicles in multiple scenes such as expressways is introduced, the problems of scattered test organization and limited scene coverage in the traditional road test are solved, multi-source heterogeneous sensing fusion dynamic correction can be constructed, and the formation cooperative control of the automatic driving vehicles in the high-speed environment is realized; the problem of perception degradation caused by failure of a single sensor is solved.

The invention provides a traffic control system based on multi-modal data fusion, and belongs to the technical field of traffic control. Comprising a multi-source data acquisition and self-adaptive preprocessing module, a modal difference identification module, a modal difference modeling analysis module and a strategy fusion module, the multi-source data acquisition and self-adaptive preprocessing module is used for constructing a multi-modal original data set, the modal difference identification module is used for constructing a difference index sequence, and the modal difference modeling analysis module is used for analyzing the multi-modal original data set. The modal difference modeling analysis module is used for predicting a position difference coefficient, a speed difference coefficient and a behavior state difference coefficient of the k-type traffic target, and the strategy fusion module is used for constructing a strategy fusion coefficient. According to the system, the position, speed and behavior state differences of the traffic participation targets are analyzed, and finally the prediction effect of the traffic condition is improved through strategy fusion.

The invention discloses an accident analysis and intelligent intervention method and system based on an intelligent networked automobile, and particularly relates to the technical field of accident analysis, which comprises the following steps: when the intelligent networked automobile approaches an accident-prone place, extracting important features from historical traffic data in the accident-prone place of the intelligent networked automobile by using a t-SNE dimension reduction method; acquiring real-time traffic data of the intelligent networked automobile, and determining similar information of the traffic data of the intelligent networked automobile; bayesian inference is carried out based on historical traffic data in an accident-prone place of the intelligent networked automobile and real-time traffic data of the intelligent networked automobile, and probability information of the traffic data of the intelligent networked automobile is determined by analyzing statistical data and constructing a machine learning mode; through comprehensive analysis of the similar information and the probability information of the traffic data, the risk that the intelligent networked automobile enters the accident-prone place is evaluated, the road traffic safety can be improved, and the accident occurrence rate is reduced.

The invention belongs to the technical field of industrial vehicle active safety control, and particularly discloses an industrial vehicle safety anti-collision early warning method and device, and the method comprises the steps: building a unified space-time perception reference through fusing ultrasonic wave, inertial navigation, laser radar and camera data; extended Kalman filtering is fused with inertial navigation prediction and laser radar poses, and high-precision positioning in the tunnel is achieved. Constructing a high-dimensional time sequence feature vector, and predicting a future collision risk in combination with a Bi-LSTM model; the early warning threshold value is dynamically adjusted according to the load, the gradient and the sensor state; the method has the following advantages that the early warning accuracy and response speed under the complex working condition are remarkably improved, and the operation safety of the industrial vehicle is guaranteed.