30 results about "Highway accident" patented technology

The invention relates to the technical field of multi-unmanned-aerial-vehicle cooperative operation and three-dimensional modeling, in particular to a multi-unmanned-aerial-vehicle cooperative three-dimensional rapid modeling method for a highway accident scene, and the method comprises the steps: generating a three-dimensional grid map of an accident area through the scanning of a millimeter-wave radar by a main control unmanned aerial vehicle; subareas are divided according to a load balancing strategy and are distributed to slave unmanned aerial vehicles, the slave unmanned aerial vehicles traverse grids along a snake-shaped track, laser radar point clouds and five-view-angle images are synchronously collected, data are bound through double time stamps and space coordinates, the point clouds are preprocessed through edge computing nodes, and the point clouds are stored in a database; the master control unmanned aerial vehicle evaluates quality based on density standard deviation and overlapping matching degree and instructs to reacquire, performs high-precision Poisson reconstruction on an accident core area, performs voxelization processing on a peripheral area, maps image textures, optimizes vehicle deformation details, simplifies a model and retains key element precision, and finally performs data processing. License plate coordinates, a scattered object thermodynamic diagram and an emergency lane occupation state are automatically marked, a visual model is generated, and rapid and accurate restoration of an accident scene is realized.

The invention discloses a highway accident scene sensing system based on AI identification and unmanned aerial vehicle cooperation, and relates to the technical field of intelligent traffic. Comprising a central planning node for fusing geographic information, meteorological information and road obstacle information of a target site with the real-time state of an unmanned aerial vehicle, constructing a digital twin scene model, generating an initial four-dimensional collaborative awareness route plan, and dividing sub-airspaces; the distributed airborne intelligent agent obtains peripheral multi-source data of the unmanned aerial vehicle, constructs a surrounding environment real-time situation map through an AI recognition algorithm, and determines a flight instruction of the unmanned aerial vehicle according to the surrounding environment real-time situation map and the divided sub-airspace; and the reconfigurable intelligent metasurface is used for adjusting surface electromagnetic characteristics according to the divided sub-airspaces and sending directional beams to the unmanned aerial vehicle. Therefore, the problems of cooperative scheduling, obstacle avoidance and communication guarantee of multiple unmanned aerial vehicles in a complex accident scene are effectively solved, and the efficiency and safety of emergency disposal are remarkably improved.

The invention discloses an expressway accident collaborative scheduling optimization method. The method comprises the following steps: S10, determining rescue vehicle configuration and a vehicle operation sequence of each accident point; s21, determining a problem optimization target and a constraint condition; s22, defining a set, parameters and decision variables related to the problem; s23, constructing a mathematical model; s31, coding design and evaluation of the solution; s32, designing a plurality of destruction operators; s33, designing a plurality of repair operators; and S34, designing a plurality of adaptive mechanisms. According to the method, a scheduling optimization model fusing multi-type accident characteristics and collaborative operation constraints is constructed by taking minimization of the overall accident response time as a core target, and the optimization model is solved by adopting an improved adaptive large neighborhood search algorithm.

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 discloses a road accident prediction system based on big data, and relates to the technical field of big data analysis and prediction, the road accident prediction system comprises an acquisition module, a fusion module and a prediction module, the acquisition module obtains multi-source data of road accident prediction, transmits the multi-source data to the fusion module, carries out space-time alignment processing on the multi-source data through a processing unit, and carries out prediction on the multi-source data; generating feature vectors, transmitting the feature vectors to a prediction module, generating an accident risk prediction result through a prediction unit according to the real-time input feature vectors, constructing a three-dimensional data set by integrating multi-source data, establishing a unified coordinate system by means of space-time grid division, and realizing data differential fusion, static parameter positioning according to road section ID, and dynamic data space-time two-dimensional association. Historical accident space-time backtracking matching is carried out, a feature set with a space-time label is generated, the problem that traditional data benchmarks are not uniform is solved, statistical features are extracted through multiple windows in the prediction stage, multi-factor coupling is analyzed in combination with risk assessment logic, prediction space-time resolution and robustness are improved, and accurate safety management is supported.

The application provides a highway accident monitoring method, relates to the technical field of optical fiber vibration sensing, and acquires a vibration image according to each time of emitting a detection signal, constructs an entropy matrix through setting a distance window and a time window, takes the mean value of vibration peak points in each distance window as chaotic entropy, and represents the vehicle passing state of any road section at any time by the chaotic entropy; when an accident signal is detected, the chaotic entropy of the accident position is compared in time and space through a time component and a distance component to form a variation score, a variation coefficient threshold is generated according to the variation of a historical entropy matrix at the position, whether the position is misjudged is judged according to the variation amount, and whether to alarm is decided, the application comprehensively judges whether the position truly has an accident through the passing state of the accident signal position in time and space, and the accident judgment accuracy is improved.

The invention discloses a vehicle-mounted emergency alarm unmanned aerial vehicle system for highway accidents, and the system comprises a vehicle-mounted trigger unit which is connected with a safety sensor of a vehicle and is used for automatically generating a trigger signal when an accident is detected; the unmanned aerial vehicle cabin is integrated on the vehicle, is used for storing and protecting the unmanned aerial vehicle and is provided with a cabin door capable of being automatically opened; the unmanned aerial vehicle body is composed of a flight controller, a positioning module, an obstacle avoidance sensor, a high-brightness warning lamp and a high-decibel alarm; and the ground control center is arranged in the vehicle and is used for receiving the trigger signal and carrying out whole-course automatic control on the unmanned aerial vehicle cabin and the unmanned aerial vehicle body. According to the invention, life safety risks caused by manual arrangement of warning signs are eliminated, zero-delay rapid early warning response after an accident is realized, long-distance, high-strength and more striking sound-light alarm signals are provided, and whole-course automation and intelligentization of an early warning process are realized.

The emergency control method for highway accidents provided by this invention includes: S1, establishing a basic highway information database and a highway accident information database, and constructing and verifying a basic simulation model; S2, establishing a highway management strategy expert database and a simulation management plan; S3, when a highway accident occurs, performing online analysis of the temporal and spatial impact range of the accident; S4, dynamically matching traffic management strategies based on the highway management strategy expert database; S5, after the highway emergency control is completed, organizing the event information and emergency management strategy information, and storing them as new traffic management strategy samples in the highway management strategy expert database. This invention is comprehensive and widely applicable, and can reduce the impact of highway emergencies on transportation efficiency and improve traffic safety.

The invention discloses a highway accident rescue scheduling method and device based on air-ground cooperation, and a medium. The method comprises the following steps: extracting accident features corresponding to highway accidents; according to the accident features, constructing an accident feature set including a plurality of accident feature parameters, and determining material response scores corresponding to the emergency materials through response intensity of various emergency materials to the accident feature parameters; for the specified emergency materials whose material response scores are not less than a preset activation threshold value, determining material demands to be scheduled for the highway accident through a scheduling quantity function; according to the material requirements, a scheduling model with the minimum rescue response time as a target function is constructed, and marshalling schemes corresponding to the ground vehicles and the unmanned aerial vehicles are determined through the scheduling model; and based on the marshalling scheme, constructing a target function used for minimizing the total length of the scheduling resource path, so as to carry out rescue path planning on the ground vehicle and the unmanned aerial vehicle, and obtaining a rescue path corresponding to the highway accident.

This invention relates to the field of intelligent transportation technology, and provides a method and system for dynamic control of emergency lanes in highway accident scenarios. The method includes: acquiring accident reporting information; using a deep learning network model to predict the accident handling time; based on the accident handling time, using a traffic wave model to predict the queue length and dissipation time under the impact of the accident; using the queue length under the impact of the accident as the total length of the controlled road segment and discretizing it into segments; using the dissipation time under the impact of the accident as the control duration and intervalizing it; constructing a binary control strategy combining time windows and spatial segments; constructing an objective function with the goal of minimizing the weighted average of total travel time and total collision exposure time; solving the objective function using a Kriging surrogate model; and obtaining the dynamic control strategy for the emergency lane based on the binary control strategy. This improves the response speed of accident handling.

The invention discloses a method for correcting safe driving speed of an expressway in severe weather, and aims to solve the problems that speed limiting measures of the expressway are lack of scientific basis and subjective reasons are difficult to quantify in severe weather. The driving speed correction method comprises the following steps: 1, acquiring road condition information, and acquiring a dynamic anti-skid coefficient of a highway by adopting a machine learning model; 2, on the basis of the highway dynamic anti-skid coefficient, simulating the simulated motion states of the vehicle in different road scenes under the severe weather condition, and constructing a calculation value data set; 3, actual motion states of the vehicle in different road scenes are actually measured, and an actual value data set is obtained; 4, establishing a driving speed compensation model; and 5, setting a safety value in the road scene as an actual value so as to determine the safe driving speed. According to the invention, through two-way verification and correction of simulation and real vehicle data, the influence of human factors on driving safety is quantified, scientific research and judgment of the safe driving speed are realized, and the occurrence probability of highway accidents in severe weather can be reduced.

The invention relates to the technical field of traffic accident intelligent processing, discloses an air-ground cooperative highway accident rescue scheme automatic generation method, and introduces unmanned aerial vehicles, medical helicopters and other low-altitude rescue equipment to be combined with ground rescue equipment for rescue in order to overcome the limitation of the rescue efficiency of vehicles. In order to overcome the limitation of insufficient cooperation of rescue strength, a command center is introduced, and a specific cooperative rescue scheme of rescue equipment is generated for each department; in order to improve the scheme adaptability of a complex traffic environment, a heuristic rescue path planning method considering traffic parameters is provided, and the arrival time of ground rescue equipment is accurately calculated. The invention provides an air-ground cooperative highway accident rescue scheme automatic generation method, and the method is characterized in that a command center automatically generates rescue equipment dispatching and cooperation schemes of all departments based on accident scene detection information. Under the condition of considering a complex traffic environment, a route for the rescue equipment to arrive at an accident scene as soon as possible is planned in real time.

An expressway guardrail monitoring and early warning system based on vibration sensing comprises a control center and a guardrail, the guardrail comprises a guardrail body, a guardrail base and a wave-shaped guardrail plate, the guardrail body is fixed to the guardrail base, the wave-shaped guardrail plate is arranged on the inner side of the guardrail body, and the wave-shaped guardrail plate is fixed to the guardrail base. The wave-shaped guardrail is characterized by further comprising a vibration optical fiber, an optical fiber host, an intelligent ball machine and an audible and visual alarm, the vibration optical fiber is fixed to a notch in the inner side of the wave-shaped guardrail plate, the optical fiber host and the intelligent ball machine are arranged on the road side, the audible and visual alarm is arranged above the guardrail body, the vibration optical fiber is communicated with the optical fiber host, and the optical fiber host is communicated with the audible and visual alarm. The vibration host is in wireless communication with the control center, and the control center is in wireless communication with the audible and visual alarm and is in bidirectional wireless communication with the intelligent ball machine. The system is based on an optical fiber vibration sensor, and is combined with an intelligent ball machine and an audible and visual alarm to realize early monitoring and early warning of highway accidents.

The application discloses a highway accident early warning structure and relates to the field of accident early warning, which comprises a warning mechanism, a mounting mechanism arranged at the bottom end of the warning mechanism, a power mechanism arranged in the warning mechanism, and a maintenance mechanism arranged in the warning mechanism; the warning mechanism comprises a shell, a motor fixedly connected to the top end of the inner wall of the shell, one end of a first screw rod fixedly connected to the output end of the motor, the other end of the first screw rod rotatably connected to the inner wall of the shell, and a warning board rotatably connected to the side wall of the shell. When an accident occurs on the front highway, the local situation is observed by using a high-speed monitoring system, then the highway accident early warning structure is started by the personnel in the monitoring room, so that the motor drives the screw rod to rotate, the screw rod pushes the warning board to overturn and expand, and passing vehicles are prompted, thereby effectively avoiding the occurrence of secondary accidents.

The invention belongs to the field of high road traffic control, and particularly relates to a highway accident scene joint management and control method and system, which comprehensively considers factors such as road characteristics, accident information, flow level and the like, and establishes a highway numerical model'simulation model module 'including toll stations, ramps and service areas in advance; the method comprises the following steps: acquiring and calculating a traffic control threshold value and control effective time after an accident occurs through a control threshold value and control effective time evaluation module, determining a control unit and a control node according to an accident influence range, determining a control measure weight through sensitivity analysis through a control measure weight calculation module, and calculating the traffic control threshold value and the control effective time according to priority ranking and target contribution degree analogy. According to the method, a high-robustness comprehensive management and control scheme, namely a robustness comprehensive management and control scheme generation module, can be adaptively and quickly generated, and the traffic flow and the management and control effective time are automatically adjusted, so that the traffic operation efficiency is improved, the flow and the capacity are intelligently matched, and a management and control strategy meeting the requirements of a management party is automatically generated.

The invention discloses a highway accident vehicle rapid transfer device which comprises a transfer plate, a hydraulic rod is fixedly connected to the bottom of the transfer plate, an electric telescopic rod is fixedly connected to the inner wall of the hydraulic rod, a driving wheel is rotatably connected to the inner wall of the electric telescopic rod, and a water storage tank is fixedly connected to the bottom of the transfer plate. A high-pressure water pump is fixedly connected to the bottom of the inner wall of the water storage tank, and one side of the high-pressure water pump communicates with a first water conveying pipe. The invention relates to the technical field of accident vehicle transfer. Through cooperation of a water storage tank, a high-pressure water pump, a first water conveying pipe, a water bag, a temperature sensor, a connecting water pipe, a water storage bin, a first pressure sensor, a water through hole, a second pressure sensor, an electromagnetic valve, a sliding circular plate and a drainage pipe, an accident vehicle chassis can be protected, and the accident vehicle chassis can be cooled; meanwhile, when the temperature of a water source in the water bag gradually rises, the accident vehicle can be cooled by spraying water, and spontaneous combustion of the accident vehicle due to too high temperature is caused.

The application discloses a big data highway accident mode recognition system, comprising: a multi-source data acquisition module for collecting environmental data, facility operation data and vehicle behavior data of a highway section; a data processing module connected with the multi-source data acquisition module, used for fusing and processing various collected data to generate a spatio-temporal aligned joint data set; a mode recognition module connected with the data processing module, used for extracting and fusing multi-dimensional features from the joint data set, and identifying the cause mode and precursor mode of the accident based on the features; and an early warning output module connected with the mode recognition module, used for issuing a road section risk early warning information; the application integrates three heterogeneous data sources of environment, facility and vehicle behavior, constructs a multi-dimensional three-dimensional risk perception system, significantly improves the discovery ability and recognition accuracy of implicit or early risks such as thin ice and fog, and realizes the leap from single appearance monitoring to multi-dimensional essential perception of highway risks.

The application relates to a highway driving risk discrimination method based on a speed risk potential field, which comprises the following steps: 1) data acquisition and processing: acquiring and processing gantry bay road section monitoring data, highway accident data and map POI data; 2) analyzing the data acquired and processed in step 1) to obtain speed data and traffic accident data; 3) establishing a speed risk potential field model according to the data analysis result in step 2), determining the speed potential V i,j , the speed potential field intensity and the speed risk potential field intensity realize the highway driving risk discrimination based on the speed risk potential field. The method can solve the problem that the current driving risk discrimination is difficult to realize risk classification in a macroscopic level.

The invention relates to a method and a system for identifying multiple accident points of an expressway. The method comprises the following steps: acquiring road section characteristic data, vehicle brake performance parameters and historical accident data of a target expressway; carrying out analogue simulation on the basis of the road section characteristic data and the vehicle brake performance parameters, calculating a brake distance and acquiring brake efficiency indexes under different road section characteristics; classifying the historical accident data based on the brake efficiency indexes, extracting brake accidents and analyzing aggregation characteristics of the brake accidents under different road section characteristics to obtain risk road sections; extracting characteristic data of the risk road sections, grouping the risk road sections with similar road section characteristics and brake efficiency reduction characteristics by adopting a clustering analysis method, and determining road section categories with common risks; key influence factors for the braking distance and high-risk road sections in each category are obtained, namely accident-prone points of the target expressway are obtained. According to the invention, the dangerous road section with reduced brake efficiency caused by road characteristics can be identified.

The invention discloses an expressway accident rescue resource dynamic scheduling and path optimization method, which comprises the following steps: acquiring expressway road network GIS data, dynamic interference data and static parameters, and performing cleaning and standardization to form an initial data set; constructing an expressway directed graph, quantifying a congestion coefficient and an ice and snow influence factor, and dividing four types of rescue scenes including no interference, congestion interference, ice and snow interference and composite interference; establishing a sub-scene real-time vehicle speed calculation model by taking the minimization of the total driving time of rescue resources as a target, and setting a single rescue point allocation constraint, a path-allocation association constraint and a binary variable constraint; multi-scene road section weights are calculated in real time, rescue points are screened, the shortest distance of nodes is updated through relaxation iteration, and a rescue path is rebuilt through reverse tracing; and performing multi-dimensional analysis on the rescue path, and optimizing dynamic parameters according to road network characteristics and historical data. The technical problems of inaccurate dynamic road condition recognition, poor road condition topology adaptability and incomplete scene coverage of a traditional method are solved.

The utility model relates to an intelligent high-speed sensing early warning system which comprises a road condition monitoring device, a vehicle condition monitoring device, an LED early warning lamp device, an Internet of Things controller and a server. The utility model aims to provide the intelligent high-speed sensing and early warning system, which can sense the running condition of vehicles on a highway and the road condition at low cost and report the information to a traffic control department and linkage equipment at the first time to warn vehicles coming behind when an accident occurs. After a microcontroller unit D of the Internet of Things controller receives alarm information, the Internet of Things controller automatically controls an LED early warning lamp device electrically connected with the microcontroller unit D to respond to early warning in real time according to the alarm information, and meanwhile, the alarm information, GNSS position information and an equipment number are added into a data packet and reported to a server through a wireless communication module. And the server synchronizes the alarm information to a traffic management platform and dials an early warning call to a specified worker, so that the whole-section sensing early warning of the expressway is finally and really realized, and the occurrence of expressway accidents is reduced.

The invention discloses a highway accident congestion position detection method based on ETC big data, and the method comprises the steps: collecting ETC transaction data of all toll stations on a highway, carrying out the data cleaning, and matching the passing record of a vehicle at an adjacent toll station according to the ETC transaction data; segmenting the ETC transaction data according to a fixed time interval, and respectively calculating the vehicle average passing time, the vehicle average speed and the traffic flow of each time period; calculating the flow change rate and the speed change rate in the current time period t, and comparing the flow change rate and the speed change rate with corresponding threshold values to judge whether a traffic accident occurs or not; calculating an accident position by using an accident position estimation model; the congestion length is calculated based on the average passing time of normal passing, and the starting and ending positions of congestion are determined in combination with the accident position. According to the invention, quick response to accidents is realized.

The present invention provides a driver stress training scenario generation system based on massive highway accident data. The system pre-processes the collected traffic accident data, establishes a stress scenario library based on the massive highway accident data, constructs corresponding indicators of the scenario library, classifies the scenario library by clustering, establishes a scenario generation method model, and performs three-level screening based on driver customization. The model driver stress training scenario generation system disclosed by the present invention provides drivers with more targeted and rich stress scenarios, which can effectively reduce the accident rate.

The invention relates to the technical field of intelligent traffic, and provides an emergency lane dynamic management and control method and system in a highway accident scene. The emergency lane dynamic management and control method in the highway accident scene comprises the following steps: acquiring accident report information, and predicting accident handling time by adopting a deep learning network model; based on the accident handling time, a traffic wave model is adopted to predict the queuing length and the dissipation time under the influence of the accident; taking the queuing length under the influence of the accident as the total length of a controlled road section, and performing segmentation discretization; taking the dissipation time under the influence of the accident as the management and control duration, and carrying out interval processing; constructing a binary management and control strategy combining a time window and a space segment; constructing a target function by taking weighted minimization of the total travel time and the total collision exposure time as a target; and solving the target function by adopting a Kriging agent model, and obtaining an emergency lane dynamic management and control strategy according to a binary management and control strategy. And the response speed of accident treatment is improved.

The embodiment of the application discloses a highway accident control scheme library generation method, a platform, an accident control scheme determination method, equipment and a medium. The accident control scheme library generation method determines a simulation accident from a historical traffic scene according to a preset rule, the simulation accident does not exist in the historical traffic scene, and micro-simulation of the simulation accident and a control scheme is performed, a control scheme meeting a prediction requirement is taken as an active control scheme, and corresponding information is added to an accident control scheme library; the accident control scheme determination method matches accident information in the accident control scheme library to obtain an accident control scheme, wherein the accident control scheme library is generated by the generation method. The application has the beneficial effect of expanding the accident control scheme library and taking into account the feasibility of the accident control scheme.

The invention discloses a steel guardrail and base combined structure for an expressway, and belongs to the technical field of traffic safety facilities. According to the structure, a concrete base, stand columns and wave-shaped beam plates are adopted to form a main body frame, and an automatic fire extinguishing system and an intelligent detection module are combined to form a comprehensive protection system; a collision face of each wave-shaped beam plate is provided with a first temperature sensor and an infrared spectrum analyzer; a multi-agent fire extinguishing agent storage device (containing ABC dry powder, heptafluoropropane and water-based foam three cabins) and a gas sensor array are embedded in the concrete base, and the control module analyzes and judges the fire category through fusion of the spectral characteristic peak intensity ratio and the gas concentration ratio. Water-based foam is sprayed for oil fire disasters, heptafluoropropane is released for electrical fire disasters, and a dry powder extinguishing agent is used for solid fire disasters. The method is used for multi-disaster-type rapid identification and directional suppression of the highway accident scene, and the fire control efficiency and the personnel survival probability after vehicle collision are remarkably improved.

The utility model provides a highway accident temporary warning device which comprises a base and a temporary warning indicator, the temporary warning indicator is detachably connected with the base, and the base is fixed to a set position beside a highway. The temporary warning indicator comprises a warning indicator shell and an active warning unit arranged in the warning indicator shell; the active warning unit comprises a warning lamp LED, a lithium battery, a lithium battery charging circuit and a lithium battery power supply control circuit, the input end of the warning lamp LED is connected to the output end of the lithium battery power supply control circuit, and the input end of the lithium battery power supply control circuit is connected to the positive electrode of the lithium battery; the lithium battery charging circuit is used for managing charging of the lithium battery; the warning indicator shell is provided with a light-transmitting window, and the light-transmitting window is sealed by a transparent material; a reflective tape is arranged on the outer side wall of the warning indicator shell.

The present invention discloses a highway accident warning method and system based on image recognition, which relates to the technical field of highway accident warning and includes the following steps: first, analyzing road traffic images through image recognition technology, detecting various traffic elements in the image, and identifying abnormal conditions in the image. The present invention solves the problem of misjudgment caused by interference from on-site factors in the traffic monitoring system through image recognition technology, dynamic feature extraction and deep learning evaluation. It processes image data in real time, adapts to different traffic scenarios, and ensures the accuracy of feature extraction. By analyzing feature change trends and capturing abnormal fluctuations, the system can accurately identify potential accidents and avoid false warnings. The low-level warning and verification stage ensures that no formal alarm is issued without high-level confirmation, effectively reducing false alarms and excessive alarms, reducing alarm fatigue, and improving the accuracy and efficiency of system responses.

This invention discloses a method and system for accident perception on highways, relating to the field of traffic information perception technology. It includes: acquiring vehicle traffic flow data collected by the highway ETC gantry system; dividing the time axis into continuous sliding windows and aggregating vehicle traffic flow data from adjacent gantries to obtain a segment-level traffic flow sequence; inputting the segment-level traffic flow sequence into a perception model to obtain the probability of accident occurrence; determining anomalies in the segment-level traffic flow sequence using traffic anomaly criteria to obtain rule-based reasoning results; integrating the rule-based reasoning results with the probability of accident occurrence to obtain a fusion score; and determining that a traffic accident has occurred in the corresponding monitoring section and identifying the vehicles involved in the accident when the fusion score exceeds a set threshold. This invention can automatically extract the inherent feature representation of accident evolution from vehicle traffic flow data, identify implicit or progressive anomalies that are difficult to cover by rules, and improve the accuracy of highway accident perception.

The invention discloses a highway accident emergency method and system based on an aircraft, relates to the technical field of emergency disposal, and discloses the highway accident emergency method based on the aircraft. The three-dimensional environment sensing data is used for extracting accident characteristics and carrying out grade judgment, rescue resources are reasonably allocated, warning is carried out through the navigation system and the aircraft, accident information is timely and effectively transmitted to other vehicles, the vehicles are guided to detour, and therefore secondary accidents are effectively avoided.