338 results about "Visibility" patented technology

The invention relates to the field of channel management, in particular to an intelligent channel management method and system based on multi-source data fusion. The invention discloses an intelligent navigation channel management system based on multi-source data fusion. The system comprises a combined ship identification module, a positioning frequency updating module, a collision risk assessment module and an intervention management module. According to the invention, the system can sense multi-dimensional environmental parameters such as illumination intensity, visibility, AIS signal quality, radar echo signal-to-noise ratio and the like in real time by constructing a combined ship identification model containing AIS, radar, vision and infrared, and dynamically adjust the fusion weight of each sensor to compensate for the deficiency of a single sensor; according to the method, the robustness of ship dynamic monitoring is improved, high-precision tracking can be kept in severe weather, the data redundancy contradiction of a traditional multi-sensor system is avoided, and therefore continuous and stable target state input is provided for follow-up risk analysis.

The invention provides a low-visibility environment pedestrian detection method based on an improved YOLOv8n model. A double-branch fusion attention network is adopted in a backbone network of an original YOLOv8n model, and a CBFuse module and a CBLinear module are introduced for feature fusion between different branches; a feature aggregation and calibration pyramid network is introduced, multi-scale feature fusion is carried out through an up-sampling module, a down-sampling module and a feature aggregation and calibration module, and the feature aggregation and calibration module carries out feature calibration and enhancement through a local attention mechanism, a global attention mechanism and a pixel attention mechanism; an adaptive task alignment detection head is introduced to execute a dynamic convolution mechanism, a task decomposition mechanism and a dynamic feature alignment mechanism; an improved YOLOv8n model is formed based on the improvement and serves as a foggy day pedestrian detection network model; according to the method, the detection accuracy and stability of the network in processing shielded and background complex images can be enhanced, and the boundary and detail features of a fuzzy target can be extracted more accurately in low-visibility environments such as foggy days and the like.

The invention relates to a sea fog quantitative forecasting method based on a period enhanced Transform, and the method comprises the steps: firstly constructing a Transform model as a forecasting model, enabling the Transform model and a self-attention mechanism to better capture the complex nonlinear relation and long-distance time dependence related to the formation of sea fog, and being superior to a conventional statistical method and an early-stage RNN / LSTM model, a data set for model training is constructed based on a time period enhancement technology and visibility standardization mapping, and the explicit time period enhancement technology enables the model to more accurately learn and forecast seasonal and daily change rules of sea fog; and a sample amplification and training set balancing strategy is executed on the data set, so that the problem of sparse fog samples is effectively solved, and the forecasting capability of the model on key low-visibility events is improved. According to the sea fog quantitative forecasting method based on the period enhanced Transform, the precision, timeliness (forecasting per hour) and spatial resolution of sea fog forecasting of a target area can be improved, and sea fog data characteristics can be effectively processed.

The invention provides an automatic driving test scene generation method based on real traffic data, and solves the problems of low accident data utilization rate, SIL / HIL test splitting and insufficient boundary coverage in the prior art. Comprising the following steps: acquiring multi-source heterogeneous traffic accident data; cleaning data by adopting a joint interpolation-anomaly detection mechanism; vehicle dynamic sudden change characteristics within 0.5 second before braking are extracted through LSTM and DTW algorithms; constructing a three-dimensional scene pipeline driven by a physical engine, and dynamically associating the pavement slippery coefficient with the rainfall intensity; analyzing the accident text into simulation parameters by using a semantic-physical parameter converter; performing SIL-HIL cooperative verification: performing extreme illumination perception test and narrow road planning verification in an SIL environment, and realizing 1ms step length fault injection test in an HIL environment; positioning failure parameters based on Bayesian optimization; a GAN is adopted to generate a long-tail scene, and a test boundary is expanded by coupling extreme conditions such as rainstorm / low visibility; and outputting a standard scene library containing the collision probability thermodynamic diagram. The safety verification efficiency under the extreme working condition is remarkably improved.

The invention belongs to the technical field of aviation meteorological monitoring, and relates to an airport dominant visibility automatic measurement method and system based on a three-dimensional laser radar, and the method comprises the steps: carrying out the full-space scanning of an airport through the laser radar, so as to collect echo signals; preprocessing and calibrating the echo signal; performing atmospheric extinction coefficient inversion on the preprocessed echo signal based on an inversion algorithm to obtain an atmospheric extinction coefficient, and then converting the atmospheric extinction coefficient into meteorological visibility; carrying out quantile statistics on all meteorological visibility in a period, judging a dominant visibility value, and carrying out adaptive weighted fusion on the dominant visibility value, the visibility value measured by the point-type visibility meter and the visibility value observed manually to obtain a fused dominant visibility value. The system can break through the limitation of traditional manual and point type visibility meter observation, and realizes accurate and automatic measurement of all-region, all-weather and unattended dominant visibility of an airport.

The invention discloses an automatic driving evaluation system based on artificial intelligence, and relates to the technical field of automatic driving, and the system comprises the following components: a meteorological model coupling module, a sensor parameter adjustment module, an automatic driving simulation module and an evaluation analysis module. According to the invention, data connection is established between the meteorological model coupling module and an external meteorological model, comprehensive meteorological data including temperature, humidity, wind speed, rainfall, snowfall, visibility and the like are acquired in real time, and a simulated extreme weather scene is constructed based on the data. The sensor parameter adjusting module automatically adjusts performance parameters of a vehicle sensor according to different weather types to ensure the accuracy of sensor data under extreme weather conditions, and the automatic driving simulation module operates an automatic driving system model in a simulated extreme weather scene to simulate the vehicle driving process.

According to the severe weather adaptive road violation real-time monitoring method and system provided by the invention, the image quality is dynamically judged by using the visibility index, and accurate traffic target identification and violation behavior detection are realized by combining image enhancement and a gating feature fusion mechanism. The system comprises a video acquisition module, a visibility evaluation module, an image enhancement module, a feature extraction and fusion module, a target detection module and a violation recognition module, can maintain high-accuracy recognition performance in various severe weathers (such as rain and fog), and is suitable for the field of intelligent traffic and urban safety management.

The invention discloses a visibility inversion method based on millimeter wave cloud radar and laser radar data fusion, and relates to the technical field of meteorology, and the method comprises the steps: guiding a millimeter wave cloud radar and a laser radar to carry out data correction and equivalent relation construction through employing a fog drop particle size distribution function measured by a fog drop spectrometer in a sample scene in advance; and then applying the correction coefficient and the equivalent function relationship to a target scene to perform multi-source data correction and fusion on the millimeter wave cloud radar and the laser radar and invert visibility. The method can fuse complementary advantages of the laser radar and the millimeter wave cloud radar; the introduction of the fogdrop particle size distribution function effectively avoids the distribution hypothesis of fogdrop shapes in the traditional model, can weaken the seasonal drift of the traditional empirical coefficient, significantly improves the accuracy and reliability of visibility inversion, and achieves the high-precision visibility monitoring under various weather conditions from clear sky to dense fog.

The invention relates to sensing and tracking of intelligent driving in rain, snow, fog, night and large shielding scenes. In order to solve the problems of target invisibility, track interruption and misconnection caused by low visibility, a shielded dynamic target identification and tracking method based on deep learning is provided; according to the method, under a unified spatial index, sparse point cloud and road topology, passable and traverse areas, static shielding volume coding, ray marking visibility boundary and shielding entrance and exit are carried out; generating a motion voting field with consistent visibility through multi-agent situation reasoning, performing normal directional enhancement according to a passable boundary, and extracting a risk corridor; voxelization is carried out on the multi-frame point cloud in the corridor, a dynamic sparse voxel map is constructed, a voting field is used for gating cross-frame edge connection, occupation changes and infinitesimal displacement are aggregated, and three-dimensional verification candidates, limited state estimation and a time continuous track are obtained; outputting the target and the corridor to which the target belongs, and forming a potential conflict zone according to the intersection of the target and the own vehicle path; the reproduction rate and the advance are improved, and cross-lane misconnection is reduced.

The invention discloses an automatic driving vehicle speed control method based on environmental complexity, which sequentially executes the following four steps in a cycle period less than 50ms: step 1, synchronously acquiring road geometric morphology, traffic flow density, visual distance, road surface attachment condition and meteorological data, and acquiring the road geometric morphology, the traffic flow density, the visual distance, the road surface attachment condition and the meteorological data; performing fusion by using a Kalman filtering algorithm to obtain an environmental parameter vector; step 2, inputting the environment complexity label into a double-layer evaluation network, and outputting four levels of environment complexity labels; step 3, inputting the vehicle speed into a control model constructed based on a hierarchical adaptive complexity vehicle speed control algorithm, and generating a target vehicle speed; 4, if the target vehicle speed is larger than or equal to the collision risk index, the environmental complexity level is increased in time, the step 3 is returned to recalculate the target vehicle speed, and a vehicle speed execution instruction is output until the target vehicle speed is smaller than the collision risk index. According to the invention, the safety and the driving efficiency of the vehicle are obviously improved, and the stability and the reliability of the automatic driving system are guaranteed.

The invention relates to the field of beacon light control, in particular to a beacon light intelligent control method and system based on visible light communication. The method comprises the following steps: acquiring a weather type, a weather type grade, ambient light brightness, visibility and a wind speed of a target area; monitoring the target area based on the ambient light brightness to obtain a target ship; constructing a beacon light power prediction model, and predicting the beacon light power by using the beacon light power prediction model according to the weather type, the weather type grade, the ambient light brightness, the visibility and the wind speed; and obtaining a visible light communication protocol, and controlling the beacon light to flicker at the beacon light power based on the visible light communication protocol and the target ship. According to the invention, the problem that the beacon light is difficult to automatically adjust according to the environmental information in the prior art is solved, the real-time performance, the reliability and the self-adaptability of the working of the beacon light in a complex environment are improved, and the safety of ship navigation is further improved.

The invention discloses a visibility grade identification method based on multi-modal data fusion, and relates to the technical field of meteorology, and the method employs a meteorological modal feature extraction method which takes element dimensions as a main part and time dimensions as an auxiliary part, fully considers the interactive coupling characteristics between meteorological variables, excavates the nonlinear interactive relationship between different meteorological elements, and improves the visibility grade identification accuracy. According to the method, the cooperative influence of different meteorological variable combinations on the visibility can be effectively learned, the overall evolution mode of a meteorological time sequence can be reserved, and then cross-modal joint attention dynamic weight allocation is performed on the image modal features of the visible light image and the meteorological modal features to improve the feature utilization efficiency. According to the method, the time sequence stability of the meteorological data is used for reliably supplementing the image features, so that the method is more stable, and high recognition accuracy and robustness are achieved even in complex environments such as illumination abrupt change and dynamic interference.

The present application relates to the technical field of aviation meteorological support, and provides a meteorological display and alarm method, system, device and medium for aircraft navigation, which comprises receiving an aircraft route plan and collecting numerical weather forecasts of each place at the takeoff time of the aircraft; extracting wind speed, temperature, relative humidity, liquid water content and cloud droplet particle number density, and calculating the horizontal visibility, aircraft icing index, total cloud cover and aircraft turbulence index at the forecast time through adaptive attenuation interpolation based on feature perception; constructing an information box and a floating box of the route points on the aircraft route map; and performing meteorological display and alarm for aircraft navigation through the information box and the floating box according to the aircraft route plan. The present application uses regional meteorological forecast results to form meteorological forecast results for flight routes, can intuitively display the comprehensive meteorology on the route and form an alarm prompt, reduces the risk of information omission, increases the safety margin, and reduces the meteorological information processing workload of flight support personnel.

An LED illuminated buoy system is disclosed, designed to enhance maritime safety and visibility. The system includes a weighted base with a conical shape for stability and a semi-translucent flotation part that extends above the waterline. The buoy houses an LED light source within the base, programmable for various colors and patterns. A sealing connection between the base and flotation part prevents water ingress. The system also features a communication interface for data transmission and remote operation. The buoy can be anchored securely and is adaptable for use as a lamp on land. The system's design allows for easy assembly and maintenance, with environmental resistance features integrated for durability. The buoy serves as a navigational aid, safety marker, and versatile lighting solution in aquatic environments.

The invention discloses a visual audio multi-mode monitoring system and method for identifying fan blade defects. A panoramic video monitoring mechanism can obtain a back panoramic image of a blade of a current wind driven generator and a surrounding environment image of the wind driven generator; a blade root pickup is arranged on the panoramic video monitoring mechanism, and a blade tip pickup is arranged on a tower drum of the wind driven generator, so that blade tip section audios and blade root section audios of the wind driven generator can be obtained. Firstly, the environment visibility under the current weather condition is recognized through the panoramic video monitoring mechanism, and then different real-time monitoring strategies are executed according to the environment visibility, so that the blade defect monitoring is not limited by the weather environment any more, the application scene is improved, and the blade defect monitoring accuracy is improved no matter in the high-visibility environment or the low-visibility environment. The false alarm risk can be reduced as much as possible in a visual and audio mutual verification mode, and the accuracy of blade surface defect identification is greatly improved.

The invention discloses a foggy weather visibility real-time monitoring method based on multi-source data fusion, and the method comprises the following steps: S1, linking a front scattering visibility measuring instrument and a camera, and obtaining visibility data and image data in a corresponding region at the same time; s2, pairing visibility data provided by the front scattering visibility measuring instrument and image data captured by the camera in the same time window; s3, classifying and marking visibility grades by adopting a ConvNeXt classification model and fusing an inverted residual module of adaptive channel attention; and S4, deploying the improved ConvNeXt classification model to an edge computing device, performing feature extraction and classification calculation by using a real-time image acquired by a camera, and outputting a corresponding seaborne visibility grade. According to the invention, real-time acquisition, intelligent classification and automatic marking of visibility data can be realized, and the accuracy and efficiency of maritime visibility monitoring are improved.

The invention discloses a visibility inversion method and device of a laser radar, equipment and a storage medium, and relates to the technical field of laser, and the method comprises the steps: determining a detection range boundary based on a laser radar voltage value and a preset threshold value; synchronously measuring the visibility in a segmented manner by using a ground single-point measurement visibility meter and a laser radar visibility meter, and performing inversion to obtain a function value; constructing a joint relation model by combining a laser radar equation according to the average error of the function value and the radar measurement signal, and generating a parameter mapping relation lookup table; acquiring signal intensity by means of a near-field visibility measuring device, and determining a target laser radar ratio and an extinction coefficient through the look-up table; and finally, resolving an extinction coefficient profile and inverting a visibility profile by adopting a Fernald inversion algorithm in combination with the laser echo signal path, the target extinction coefficient and the laser radar ratio. The efficiency of laser radar visibility inversion is improved.

The invention relates to the technical field of intelligent ship driving, and discloses an intelligent ship driving assistance system based on multi-source information fusion and AI decision, which comprises a multi-source information acquisition module, an information data processing module, an information fusion processing module, an AI decision calculation module and a man-machine interaction module, according to the ship intelligent driving assistance system based on multi-source information fusion and AI decision making, perception blind areas of scenes with low visibility and the like are eliminated through a multi-source information collection and vision enhancement technology, and a target is accurately recognized; through data processing and three-level fusion, multi-source information is efficiently integrated, and the manual analysis pressure is reduced; the AI decision calculation module provides accurate guarantee for berthing, stranding, collision avoidance and other scenes, and accident risks are reduced; the system can also optimize the route and speed in combination with data such as weather, reduce energy consumption, shorten the navigation time, take into account the safety and economy of ship driving, and greatly improve the intelligent level of shipping.

The invention provides a satellite multi-load moon forecasting method and device and electronic equipment. The method comprises the following steps: acquiring coordinates of a satellite moon vector connecting line under preset time precision in an instrument coordinate system; performing cold air view field constraint, scanning view field constraint, earth night side observation constraint and lunar phase angle constraint on each instrument to obtain a rough time window of the moon entering the view field corresponding to each instrument; and carrying out dynamic adaptive golden section extremum search at a preset time interval, calculating the visibility of each instrument view field to the moon, and obtaining the optimal observation time of the moon. According to the satellite multi-load lunar forecasting method and device and the electronic equipment provided by the embodiment of the invention, the FY-3 F satellite multi-load lunar observation rapid forecasting method based on the combination of rough time search and adaptive golden section extremum search is constructed, and by optimizing the calculation of the cold air view field and the scanning view field angle, the accuracy of lunar observation is improved. The moon forecasting method for the cross-rail circular scanning instrument is provided, and limitation of a traditional rectangular view field is avoided.

The utility model relates to a radar system for simultaneously detecting visibility and a wind field, and the system comprises a laser emission unit which is used for emitting laser with a first wavelength and laser with a second wavelength to the atmosphere; the optical receiving unit is used for receiving a first wavelength laser echo signal and a second wavelength laser echo signal which are scattered by the atmosphere and contain atmosphere information; the detector is used for respectively converting the first wavelength laser echo signal and the second wavelength laser echo signal received by the optical receiving unit into a first electric signal and a second electric signal; the data acquisition module is used for acquiring a first electric signal and a second electric signal of the detector; and the upper computer is connected with the laser emission unit and the data acquisition module. According to the utility model, two atmospheric elements of visibility and an atmospheric wind field can be monitored simultaneously and synchronously.

The invention discloses a heavy fog visibility prediction system and method based on deep learning, and relates to the technical field of data processing, and the method comprises the steps: determining a particle size modal category representing a heavy fog particle size distribution type; screening a historical particle size modal sequence matched with the particle size modal category of the current region based on an internal corresponding relation between the meteorological environment state and the particle size modal category; based on the category transfer complexity reflected by the transfer path, performing sequence reconstruction on typical particle size modal categories in the historical particle size modal sequence; inputting the reconstructed historical particle size modal sequence and the current particle size modal distribution characteristics into a long short-term memory network model optimized by a particle size modal attention mechanism, and outputting a predicted heavy fog visibility grade of the current region at a future moment; according to the method, the fog particle size modal physical characteristics and the deep learning model are fused, so that the fog visibility grade prediction accuracy and reliability are remarkably improved.

The invention provides a multilayer laser wind finding radar data processing method and system, and relates to the technical field of radar data processing. The method comprises the following steps: acquiring echo signals of each height layer, synchronously acquiring meteorological parameters of visibility, humidity and rainfall intensity, judging weather types according to meteorological conditions, and setting corresponding SNR reference thresholds; dividing a near-surface layer, a middle layer and a high-altitude layer based on the SNR statistical characteristics of the height layer, and implementing threshold difference adjustment; performing threshold dynamic smoothing according to the meteorological change rate; judging the data validity based on the smoothed threshold sequence, and performing graded repair on discrete missing points, continuous missing segments and large-range missing regions; and finally, calculating the confidence coefficient through the SNR threshold value factor and the repair algorithm factor, and outputting a wind field profile with a reliability identifier. And the data utilization rate, the restoration precision and the result credibility under the complex meteorological condition are remarkably improved.

The invention discloses a target association method based on multi-modal data feature alignment, and relates to the technical field of multi-modal information processing and intelligent decision making. The multi-modal information of the synthetic aperture radar, the high-resolution range profile, the track, the visible light, the infrared, the text, the voice and the like is covered. And through cooperative work of a plurality of intelligent association sub-models, feature extraction and alignment association are performed on different modal data by using deep learning technologies such as a two-dimensional convolutional neural network, a one-dimensional convolutional neural network, a bidirectional long-short term memory network, an attention mechanism and Transform, association similarity is calculated, and pairing mode data is output. The method is applied to the fields of target intelligent identification, resource environment monitoring and the like, has obvious advantages in complex sea conditions, low visibility and multi-interference scenes, can improve the target detection, identification, tracking and early warning capabilities, and provides accurate decision support for safety management and control, emergency disposal, resource monitoring and the like in the fields of shore, sea, air and sky.

Disclosed is a method of detecting and rejecting a spoofing or jamming signal source by receiving at a first device a forecast of a visibility for each Global Navigation Satellite System (GNSS) satellite signal source in the forecast at a GNSS receiver coupled to the first device, calculating from at least an elevation and the received visibility of the satellite signal sources in the forecast a predicted Signal to Noise Ratio (SNR), comparing SNR acquired by the GNSS receiver of one or more of the satellite signal sources to the predicted SNR, detecting a spoofing signal source based on acquiring a higher SNR than predicted or a jamming signal source based on acquiring a lower SNR than predicted, and rejecting the spoofing or jamming signal source based on differences between the acquired and predicted SNR.

The invention discloses an active avoidance method for an unmanned aerial vehicle, and relates to the technical field of unmanned aerial vehicles. Comprising the following steps: SA: weather adaptive sensing: obtaining physical characteristics of an external environment through an anti-scattering laser radar group, a super-resolution millimeter wave radar group and an anti-turbulence sound wave positioning group; sB: multi-modal decision fusion: aligning the laser radar data and the millimeter wave radar data, obtaining the confidence interval of each sensor through Gaussian process regression calculation, and determining the weight of the sensor; and SC: hierarchical fault-tolerant control: adjusting a sensor combination according to the visibility of the external environment, and determining a sudden obstacle avoidance track according to the arranged UWB positioning module and the inflatable buffer structure. The sensor combination is automatically switched according to the visibility, and the inflatable buffer structure is started after the emergency mode is triggered, so that the safety of the hovering or landing track of the unmanned aerial vehicle can be ensured, and the obstacle avoidance success rate is improved.

To provide an evaluation support device, an evaluation support method, and an evaluation support program for supporting the evaluation of line-of-sight conditions in a building.SOLUTION: A support device 20 includes a control unit 21 that supports the design of a building arranged in a three-dimensional space. The control unit 21 acquires a building model in which element models are arranged in an interior space, and calculates statistical values of line-of-sight distances to the element models for each line-of-sight direction at each viewpoint position set in an evaluation target area of the interior space. The control unit 21 calculates a line-of-sight index in accordance with the distribution of the statistical values of the line-of-sight distances in the evaluation target area.SELECTED DRAWING: Figure 1

The invention discloses an unmanned aerial vehicle visual navigation method based on infrared semantic feature matching and inter-frame anchoring, and aims at the core requirement of unmanned aerial vehicle autonomous navigation in a GNSS denial environment, the stability of infrared imaging and the robustness of high-level semantic features are fully combined, and the high-precision, continuous and stable visual autonomous navigation capability is realized. The method is especially suitable for complex scenes such as low-illumination, weak-texture and large-area homogenized areas at night. Through innovation in the aspects of infrared semantic map construction, deep semantic feature extraction, multi-scale semantic matching, semantic anchoring key frame selection, inter-frame recursion positioning and closed-loop correction, Bayesian fusion estimation longitude and latitude solution and the like, the positioning precision and robustness of the unmanned aerial vehicle in GNSS denial, low-visibility and large-area homogeneous scenes are remarkably improved.

The invention discloses a mountainous area fog forecasting method, which comprises the following steps that: through the cooperative work of a satellite sub-module, a ground sub-module and a forecasting sub-module, the accurate forecasting of a foggy weather condition is realized, and the satellite sub-module records the complete cycle change of a cloud layer and an air temperature, extracts parameters and carries out space-time analysis; the ground submodule records fog visibility, aerosol concentration and air temperature data in real time; the prediction sub-module receives and processes the data, establishes a foggy day prediction model by using cloud computing and machine learning technologies, sets thresholds of different foggy day types, and predicts and judges the foggy day types by comparing the real-time monitoring data with the thresholds; by combining a satellite remote sensing image processing technology and ground real-time monitoring data, the system can obtain more comprehensive and accurate meteorological information, thereby improving the accuracy and reliability of foggy day prediction. A machine learning algorithm is utilized to train a large amount of historical data, so that the prediction model can learn rules and features generated by foggy days, and the prediction precision is improved.

The invention relates to the technical field of data analysis, and provides a meteorological state prediction method and system based on a spatio-temporal interpolation coupling model.According to the embodiment of the invention, after credibility authentication of a meteorological forecast field database is passed, a historical meteorological forecast data set is called, and a dynamic influence area is divided by combining geographic position coordinate information of a target observation point; determining a cloud cover ratio time sequence change trend under the condition of no rainfall in the region, and building a meteorological state prediction model of space-time interpolation coupling by combining the visibility state parameters; and then inputting the current meteorological observation data set into the meteorological state prediction model, carrying out spatial-temporal feature coupling calculation to obtain a current state feature vector, and generating a meteorological state prediction result that the target observation point contains a nepheline state within a preset time period based on the current state feature vector, thereby effectively improving the accuracy and reliability of meteorological prediction.

The invention provides an avoidance path planning method, device and equipment and a storage medium, and the method comprises the steps: obtaining real-time visibility and traffic light states based on a vehicle-mounted sensor; a dynamic elliptical avoidance domain is constructed for the freight vehicle, the long axis radius of the elliptical avoidance domain is determined according to the real-time visibility and the vehicle speed, and the short axis radius of the elliptical avoidance domain is determined according to the real-time visibility and the traffic light state; calculating an avoidance constraint vehicle speed and a green wave band passing vehicle speed for the common vehicle, and taking a smaller value of the two as a recommended vehicle speed; if the overlapping degree of the avoidance domain and the current path exceeds a set threshold value, triggering path re-planning; and performing path re-planning based on the traffic light state, the real-time position of the freight vehicle and the avoidance constraint condition. Reliable data are acquired through the vehicle-mounted sensor, the dynamic elliptical avoidance domain adaptive to the environment and the traffic state is constructed, the safe vehicle speed is recommended for the common vehicle, the path is re-planned when the threshold value is exceeded, the safety and the efficiency are both considered, and the road passing safety and smoothness in the haze day are greatly improved.