8645 results about "Road condition" patented technology

A multi-task joint perception network model and detection method for traffic road surface information can simultaneously detect a lane line and a drivable area. A coordinate attention mechanism is integrated into a traditional feature extraction network to ensure that a feature extraction effect is enhanced while a calculated amount is not increased. In a neck network, a dilated convolution residual module is proposed to enhance performance of prediction of details by the network, and a decoder part shares features of the drivable area into lane line detection to enhance a lane line detection effect under complex road conditions. In a training stage, there is provided a alternating optimization training method to improve integral segmentation performance of the model. The multi-task joint perception network model and detection method realizes quite high accuracy and excellent speed performance in a challenging BDD100K dataset.

The invention provides an AI algorithm-based scenic spot car intelligent path establishing and planning method and system, and relates to the technical field of path planning, and the method comprises the steps: collecting scenic spot road geographic information data, and constructing a weighted road network model; collecting real-time positioning of the trolley and sensor data; using the reinforcement learning model to generate a real-time optimal path planning strategy; calculating an initial recommendation path; road condition changes are monitored, and a path is dynamically adjusted; and outputting the updated optimal path scheme and carrying out navigation. According to the method, intelligent and real-time path planning of the vehicles in the scenic area can be realized.

The invention provides an intelligent traffic decision-making method based on multi-source sensor data, and the method comprises the steps: obtaining vehicle sensor data and road information, and obtaining a standardized traffic flow feature vector through data synchronization, fusion, feature extraction and dimension reduction processing; inputting the standardized traffic flow feature vector into a traffic flow prediction model, and predicting the traffic flow through a vehicle dynamics model constraint state transition matrix; according to the predicted traffic flow and the real-time road condition information, the road congestion state is judged, and a real-time congestion map is generated; in combination with the real-time congestion map, the road network speed limit rule and the starting and ending point position information, an optimal path is planned, and path smoothing processing is performed through a driving behavior mode; and constructing a vehicle intelligent decision-making system, integrating traffic flow prediction, congestion detection and a path planning model, and outputting a vehicle driving strategy according to real-time vehicle sensor data.

The invention provides an intelligent networked automobile dynamics simulation method and system based on a physical engine, and relates to the technical field of intelligent networked automobiles, and the method comprises the steps: obtaining vehicle parameters and road condition information of an intelligent networked automobile, optimizing a multi-agent cooperative control model through a self-adaptive optimization algorithm based on the vehicle parameters and the road condition information, and carrying out the optimization of the multi-agent cooperative control model. Constructing a digital twinborn model, and mapping the optimized multi-agent cooperative control model to an actual intelligent networked automobile control system; the method comprises the following steps: establishing a distributed edge computing network of an intelligent networked automobile group, constructing a decentralized knowledge base by utilizing a block chain technology, forming a decision-making experience data set, designing a multi-level attention mechanism to extract target information based on the decision-making experience data set and multi-modal data of a current traffic scene, and generating a final vehicle control instruction. And transmitting the final vehicle control instruction to a vehicle execution system to realize dynamic adaptive group cooperative control of the intelligent networked vehicle.

The invention relates to the technical field of data processing, and discloses a road condition monitoring recognition method, device and equipment based on machine vision and a medium. The method comprises the following steps: carrying out multi-source data acquisition on a road environment to obtain an original data set; performing dynamic time warping and multi-modal feature fusion processing on the original data set to obtain a spatial-temporal feature tensor; performing multi-scale pyramid decomposition and depth feature extraction on the spatial-temporal feature tensor to obtain a road condition feature vector; semantic analysis is carried out on the road condition feature vectors through feature mapping and knowledge graph enhancement, and a road condition attribute set is obtained; performing multi-level risk quantification and space-time propagation simulation on the road condition attribute set to obtain a graded early warning index and a risk distribution map; and performing multi-target tradeoff and causal chain reasoning on the graded early warning indexes and the risk distribution map to obtain a self-adaptive traffic regulation and control instruction set. According to the invention, the efficiency and accuracy of road condition monitoring and identification based on machine vision are improved.

The invention discloses an intelligent driving multi-sensor fusion data processing system, which belongs to the technical field of fusion data processing and comprises a data preprocessing unit, a data fusion unit and a data post-processing unit. The data preprocessing unit normalizes the original data of the laser radar, the camera, the millimeter wave radar and the ultrasonic sensor through a standardization, cleaning, noise reduction and calibration synchronization module; the feature level fusion module deeply digs the characteristics of each sensor, and fuses geometric, visual, motion and close-range obstacle features by using a deep neural network; the decision-making level fusion module generates a driving instruction through weighted voting and fuzzy logic reasoning through classification and situation evaluation; and the data post-processing unit combines the vehicle and road condition optimization instruction, evaluates the risk, and stores the data feedback optimization system. The system can improve the sensing precision and decision reliability, enhances the expansion adaptability of the system, and provides guarantee for safe and efficient operation of intelligent driving.

The invention relates to the technical field of logistics transportation scheduling, in particular to a logistics management system based on intelligent scheduling, which comprises a data acquisition module, a demand analysis module, an intelligent scheduling module, a path planning module, a monitoring and feedback module, an optimization module and the like. The data acquisition module is used for collecting information such as goods, vehicles, storage, customer demands and traffic road conditions; the demand analysis module predicts logistics demands by using a time sequence analysis or machine learning algorithm; the intelligent scheduling module adopts a greedy and dynamic programming mixed algorithm to allocate resources; the path planning module calculates an optimal path according to road conditions and starting and ending points; the monitoring and feedback module monitors logistics parameters in real time and feeds back abnormity; and the optimization module optimizes the scheduling scheme according to the fitness function based on the genetic algorithm. According to the invention, data and resources of all links of logistics are effectively integrated, accurate demand prediction, efficient resource configuration, transportation path optimization, real-time monitoring feedback and overall performance improvement of the system are realized, the logistics cost is significantly reduced, and the transportation efficiency and the service quality are improved.

The invention relates to the technical field of image processing, and discloses a display method and system for a vehicle-mounted LED display screen. The method comprises the following steps: acquiring ambient light intensity data, performing fusion processing to obtain a characteristic value, and predicting ambient light change; identifying a driving scene according to the road condition data, and analyzing the priority by combining the ambient light change data; arranging a display area in combination with the eye movement data, and determining display parameters; analyzing the multi-modal interaction data, determining an interaction strategy, and verifying to generate response data; adjusting display content according to the ambient light features, and outputting a display effect in combination with response data; and energy consumption is estimated by integrating various data, and control parameters are generated to adjust an LED driving circuit. Through intelligent analysis and processing of multi-dimensional data such as ambient light intensity, driving scene and eye movement tracking, dynamic optimization and energy-saving control of display content are realized, and the problem that display effect and energy efficiency are difficult to consider in the prior art is overcome.

The invention provides a real-time map updating method and system based on multi-source geographic information data fusion. Wherein a three-dimensional model is constructed by integrating traffic flow data, remote sensing images and road network topology, the road traffic pressure is quantified, and a feature correlation index is established. And obtaining a vehicle displacement vector and a speed gradient in combination with laser point cloud and video monitoring data, generating a dynamic road feature data set, and establishing a mapping relationship with the index table. On the basis, a road state prediction model is constructed, and through correlation analysis of traffic pressure data and a real-time feature data set, deep correlation between vehicle motion features and road states is established. And dynamically adjusting a road network connection structure, synchronously updating mapping parameters and predicting model precision, and realizing real-time adaptive adjustment of the road network topology. According to the technical scheme provided by the invention, the real-time performance of map updating and the road condition prediction accuracy are remarkably improved, and the urban traffic congestion index can be reduced.

The invention discloses a power distribution operation and maintenance inspection track intelligent planning and optimizing method, and relates to the technical field of power distribution network intelligent operation and maintenance, and the method comprises the steps: collecting the topological data and equipment state information of a power distribution network, and constructing a power equipment association relationship model through an intelligent analysis model; on the basis of the incidence relation model, the inspection importance degree of the power equipment is calculated, and an initial inspection sequence is generated according to the inspection importance degree; and in combination with the initial inspection sequence and the real-time road condition information, constructing a dynamic inspection track network, and generating a final inspection path through an intelligent optimization algorithm. The problems of inaccurate equipment state evaluation, unreasonable routing inspection task planning, insufficient routing inspection path optimization and the like in the prior art are solved. The method has the main beneficial effects that the utilization efficiency of inspection resources is improved, the adaptability of an inspection strategy is enhanced, and the scientificity of an inspection path is optimized.

The invention discloses a vehicle intelligent driving decision optimization method based on deep learning. The method belongs to a vehicle intelligent driving decision technology. Firstly, various sensors are installed on a vehicle to collect data, and data sets are divided according to a proportion after cleaning and marking. A model containing an encoder-decoder and an attention mechanism is constructed, the weight is initialized, and a hyper-parameter and anti-overfitting strategy is set. And training the model by using a specific algorithm, calculating a loss value and evaluating and adjusting according to a verification set. After the model is integrated to the system, a preliminary candidate decision is determined according to a probability threshold, the decision is optimized in combination with a vehicle surrounding environment information set and a risk assessment function, and decision probabilities are redistributed in real time according to environment changes by means of an environment perception feedback mechanism. The method overcomes the defects of the traditional technology, utilizes the advantages of deep learning, effectively improves the driving decision precision, enhances the adaptability of the system to complex road conditions and environments, and guarantees the safe and efficient operation of intelligent driving.

The invention provides a green agricultural product distribution path optimization and inventory control method, and the method comprises the steps: selecting a distribution path with the minimum agricultural product quality influence degree as an optimal path according to a prediction result of the agricultural product quality influence degree, and transmitting the optimized distribution path to a distribution vehicle terminal in real time; after the distribution vehicle terminal receives the optimal distribution route issued by the cloud, the distribution route is navigated in real time through a vehicle-mounted navigation system, a driver is guided to drive along the optimal route, and the distribution route is dynamically adjusted according to the real-time road condition information; meanwhile, according to the real-time position and the driving speed of the distribution vehicle, the arrival time of the agricultural products is dynamically estimated, and the estimated arrival time information is pushed to a destination customer to remind the customer to make preparation for receiving goods in advance.

The present invention relates to the technical field of highway maintenance management, and in particular to a highway maintenance safety management system. The system includes a road condition monitoring module, a traffic load assessment module, a traffic flow redistribution module, a maintenance timing determination module, and a maintenance effect verification module, which are used to monitor multi-layer road condition data of the highway, and perform table-deep correlation detection to obtain road surface abnormal point data; quantify the road surface loss risk of the road surface abnormal point data to obtain road surface loss early warning indicators; collect vehicle images and meteorological data; analyze vehicle model loss contribution based on vehicle images; construct an environmental impact coefficient matrix based on meteorological data; construct a dynamic weight model based on vehicle model loss contribution and environmental impact coefficient matrix to obtain a road surface loss dynamic weight model. The present invention improves the efficiency, benefit and intelligence level of highway maintenance management by organically integrating highway maintenance with traffic management.

The invention provides a food material distribution intelligent control method and system based on big data, and the method comprises the steps: collecting the temperature and humidity of a distribution compartment in a distribution process in real time, collecting the position information and speed of a distribution vehicle, and obtaining the sensitive parameters of distributed food materials in the distribution vehicle; all reachable paths are obtained, a prediction model is used for predicting the remaining fresh-keeping time of the food materials distributed on all the reachable paths according to the current compartment internal environment parameters, the fresh-keeping state data of the food materials and the following path parameters, and the path parameters comprise weather and road conditions; screening out reachable paths of which the difference value between the remaining fresh-keeping time and the arrival time of the reachable paths does not exceed a preset time threshold value from the reachable paths; and obtaining the reachable path with the minimum cost calculated through the environment-sensitive supply chain cost model from the screened reachable paths. By means of the method and the corresponding system, it is ensured that the food materials are delivered at the lowest cost and keep the best freshness after delivery is completed, and the quality loss caused by transportation is reduced.

The invention discloses an intelligent suspension control system based on complex road condition recognition, relates to the technical field of automobile damping adjustment, and solves the technical problems that the system is difficult to adapt to complex and changeable road conditions, suspension parameters cannot be accurately adjusted in real time according to actual road conditions, and the performance of a vehicle under the complex road conditions is limited. After the road condition information acquisition module acquires data of various sensors, the road condition comprehensive identification and analysis module deeply analyzes the data and judges that a vehicle is in a curve or straight driving state, and when the vehicle is driven on the curve, the curve intelligent analysis module determines whether the vehicle is in a curve or straight driving state according to information such as the steering angle, the quality, the vehicle speed and the turning radius of the vehicle. The turning centrifugal force and the heeling moment are calculated by means of a vehicle dynamics model, the adaptive heeling rigidity and damping requirements are determined accordingly, current suspension parameters are compared, the hydraulic oil and damping hydraulic oil flow and pressure adjusting amount are calculated, and precise suspension regulation and control, vehicle heeling reduction and curve control stability strengthening are achieved.

The invention relates to the technical field of automatic driving, in particular to an automatic driving vehicle management method and system, electronic equipment and a storage medium. The method comprises the following steps: firstly, automatically identifying and classifying a current driving scene based on vehicle real-time data, and selecting a most suitable driving strategy from a preset strategy library; then, map data are optimized in real time to reflect the current road condition; then predicting future trajectories of surrounding dynamic objects; planning a safe bypass path according to a prediction result; and finally, generating a corresponding vehicle control instruction. The system can adapt to complex and changeable driving environments, can improve the driving efficiency on the premise of guaranteeing the safety, and remarkably improves the intelligent level and practicability of an automatic driving system.

This application discloses a method, system, and related equipment for intelligent energy management of new energy vehicles. Based on the vehicle's starting and ending points, at least one candidate energy-saving path is determined. The predicted energy consumption of the vehicle along at least one candidate energy-saving path is lower than that of other paths. The total energy consumption is predicted based on road condition information and energy consumption impact information for each path. In response to the selection of at least one candidate energy-saving path, a preset travel route is determined. The preset travel route includes multiple road segments, and the total energy consumption includes the energy consumption of each road segment. With the goal of minimizing fuel consumption along the preset travel route, the engine's operating state is controlled based on the initial state of charge (SOC) of the power battery in each road segment, the energy consumption of the road segment, and the vehicle's actual overall demand, ensuring the engine operates within its high-efficiency range. Using this application can reduce fuel consumption for users and improve the driving experience.

The invention is suitable for the field of logistics transportation, and provides a hazardous chemical substance transportation path planning method and system, and the method comprises the steps: collecting road network information, road condition real-time data, weather data, population density distribution data, environment sensitive point distribution data, and hazardous chemical substance characteristic data; based on the collected data, establishing a risk assessment model, and performing risk assessment on each preset transportation path; constructing a multi-objective function, and distributing a corresponding weight for each objective; and searching and generating an optimal or suboptimal transportation path in the road network based on the multi-objective function, and when the change of the road condition data or the weather data exceeds a preset threshold value, performing risk assessment and path planning again, and regenerating the optimal or suboptimal transportation path. And the transportation data security is ensured. A path can be adjusted in real time to cope with road conditions and weather changes, the transportation risk is reduced, and the transportation cost, time and risk are balanced.

The invention discloses an intelligent logistics path optimization system based on big data, and relates to the technical field of logistics, the system comprises a data acquisition module, a data analysis module, a path planning module, a feedback adjustment module and a user interface module; according to the method, the driving style and behavior characteristics of each driver can be deeply analyzed by comprehensively collecting the historical driving data of the driver, such as the driving speed, the braking frequency and the turning habit, and by means of the detailed driver driving habit analysis, the path planning better fits the personal preference of the driver, for example, for the driver with aggressive driving style, the driving habit of the driver can be better optimized. The system intelligently recommends routes with excellent road conditions and less traffic restrictions, so that not only can a driver give full play to the driving speed advantage, but also the driving experience that the driver pursues speed and passion can be met to a certain extent, and for drivers with careful driving styles, the system tends to recommend routes with small traffic flow and high road safety.

A multi-degree-of-freedom drive-by-wire unmanned vehicle obstacle avoidance path planning method mainly comprises the following steps that based on environment perception information, surrounding obstacles are analyzed and processed, and an environment safety boundary is established; on the basis of an environment safety boundary, speed sampling is carried out based on dynamic window trajectory planning of hierarchical response, and an optimal local trajectory is calculated and selected; in the driving and obstacle avoidance process, according to obstacle distribution and road condition fluctuation real-time information, the posture of a vehicle body is dynamically adjusted, speed sampling is updated, and path planning is updated. According to the method, the optimal obstacle avoidance path can be planned in real time, so that the unmanned vehicle can quickly respond in a complex scene, and potential collision risks can be avoided in time; the characteristics that the multi-degree-of-freedom drive-by-wire unmanned vehicle is suspended and the vehicle body posture is adjustable are fully utilized, vehicle posture adjustment is fused into an obstacle avoidance strategy, and the feasibility of an obstacle avoidance planning track and the stability of vehicle execution are improved.

The invention relates to the technical field of intelligent transportation control, in particular to a tomato transportation speed self-adaptive adjustment method based on path road condition feedback, which comprises the following steps: acquiring road images, vibration waveforms and altitude data through a multi-source sensing unit, and constructing real-time road condition information; identifying a driving mode and extracting a corresponding bumping parameter; detecting the maturity grade of the tomato by combining multispectrum and thermal imaging, and querying a maturity-compressive strength corresponding table to calculate a cargo damage threshold value; constructing a dynamic mapping model under multiple working conditions, predicting vibration response and converting the vibration response into equivalent pressure; comparing the equivalent pressure with a damage threshold value to obtain a safety margin, constructing a speed adjustment decision tree and generating a maximum allowable speed value of each road section; and dynamically generating a segmented variable-speed control instruction based on the speed decision matrix, and controlling a throttle valve and a braking system to cooperatively change speed. The method has the advantages of accurate working condition identification, dynamic fruit adaptation, closed-loop speed regulation and control and the like, and is suitable for fine speed control of a high-sensitivity fruit and vegetable transportation scene.

The invention belongs to the technical field of intelligent monitoring, and particularly relates to a 5G Internet of Things fused video monitoring intelligent early warning method, which comprises the following steps of: acquiring vehicle attribute information and environment perception data, constructing a 5G Internet of Things perception network, fusing a vehicle movement track and road condition environment data through a cross-modal attention mechanism, generating an enhanced environment perception graph, and simultaneously, carrying out early warning on the vehicle movement track and the road condition environment data. Optimizing the transmission efficiency by adopting a dynamic resolution adjustment algorithm; the method comprises the following steps: constructing a dynamic behavior prediction model by using a space-time diagram convolutional network, predicting a vehicle abnormal behavior probability and an evolution trajectory, calculating an early warning level through an adaptive risk quantification algorithm, simulating a risk diffusion coefficient by using a dynamic risk propagation model according to the road section vehicle density, the average vehicle speed and the road traffic capacity, and correcting early warning sensitivity parameters in real time. And transmitting to a command platform, performing situation deduction, triggering a grading early warning instruction, and generating thermodynamic diagram warning information. Therefore, the problems of insufficient positioning precision, weak analysis capability, poor transmission efficiency and the like in the prior art are solved.

The invention relates to the technical field of road condition intelligent perception and application, in particular to a road condition adaptive topology perception and application system and method. A condition cluster perception module monitors road condition parameters in real time; the data transmission communication module realizes differential data transmission strategies; a road section dynamic risk grade evaluation module calculates a road section risk index and grade according to the road structural state parameters and a risk evaluation model; the node adjustment and resource allocation module adjusts the activation state and sampling frequency of each sensor according to the road section risk level, simulates and verifies the implementation effect of a resource allocation scheme under different road section risk levels by using a constructed road system digital twin model, and optimizes the resource allocation scheme by using a multi-objective optimization algorithm; the application service module converts the risk level into a specific service action; the cooperative operation of the modules realizes the flexible allocation of road condition perception, communication and computing resources, and improves the utilization efficiency of the resources and the service efficiency of the system.

The invention discloses an end-cloud cooperative detection method and system for traffic anomalies, and relates to the technical field of intelligent traffic control. The method comprises the following steps: collecting traffic video streams through edge equipment, and identifying abnormal behaviors and generating structured event data by using a lightweight YOLOv3-tiny model; when the confidence exceeds a dynamic threshold and the event type is a high-risk type, uploading a video clip and data to a cloud; the cloud integrates a historical road condition map, meteorological data and a real-time traffic flow state, reconstructs a three-dimensional event scene by adopting a space-time attention pyramid network, and verifies the authenticity of an event in combination with a traffic flow sudden change detection algorithm; and generating a signal lamp forced switching instruction for the risk level overrun event, and issuing the signal lamp forced switching instruction to roadside equipment within 3 seconds to execute emergency response. According to the invention, full-link closed-loop control of traffic accidents from identification to response is realized, and the false alarm probability is greatly reduced while the identification accuracy is guaranteed.

The invention discloses a scheduling information management method and system based on fire fighting of a fire fighting truck, and relates to the technical field of intelligent fire fighting scheduling, and the method comprises the steps: receiving fire alarm information, and collecting real-time fire scene data based on a preliminary fire behavior evaluation result; combining the collected real-time fire scene data with historical fire data, and analyzing a fire spreading trend through an AI fire prediction model; according to the fire behavior spreading trend and the equipment state of the fire fighting truck, dynamically allocating fire fighting truck resources, and planning an optimal driving route; the driving state of the fire fighting truck is monitored in real time, dynamic adjustment is performed based on road conditions and equipment information, and task allocation of the fire fighting truck is updated according to the real-time fire scene situation; in the fire extinguishing process, resources are dispatched in real time for supply according to the material consumption condition of the fire fighting truck. By monitoring the material consumption condition of the fire fighting truck in real time, the supply resources are intelligently dispatched, and the continuity and high efficiency of the fire extinguishing action are ensured.

The invention relates to a multi-mode AIGC cold-chain logistics path optimization decision-making method and a multi-mode AIGC cold-chain logistics path optimization decision-making system. The method comprises the following steps: inputting position data, temperature data, road condition data and weather data in a cold-chain logistics system into an AIGC model of a stack auto-encoder for feature extraction and reconstruction error calculation to obtain a feature index set; performing space-time interval division and similarity matching on the cold chain distribution path to obtain an interval reference path library; inputting a hierarchical control model for stage processing to obtain an initial control parameter; an objective function including temperature constraint, time window constraint and vehicle dynamics constraint is constructed for solving, and a path optimization scheme is obtained; performing road condition random disturbance and temperature fluctuation influence analysis to obtain a path risk scoring index; and performing feedback correction and iterative optimization on the initial control parameter to obtain a target control parameter. According to the invention, the temperature control precision is ensured, and the distribution efficiency and the system reliability are effectively improved.

The invention provides a highway event intelligent association analysis and prediction method, which comprises the following steps: constructing an event time-space association network according to time-space distribution characteristics of events, in which nodes represent events and edges represent association between the events; training the event space-time association network by adopting a graph neural network model to obtain an association evolution rule of the event in time and space; if an event possibly occurring in the future is predicted, other associated events possibly caused by the event are reasoned according to the event space-time association network, and an event causal logic chain is formed; according to the event causal logic chain, evaluating the effects of different coping strategies by adopting a reinforcement learning algorithm to obtain an event handling scheme; in the event occurrence and evolution process, vehicle track data and road condition monitoring data are continuously obtained, and the event time-space correlation network and the prediction model are dynamically updated.

The invention relates to the field of sensor activation, in particular to a tire pressure sensor activation method and device, equipment and a storage medium. The method comprises the following steps: acquiring vehicle running state monitoring parameters and vehicle driving monitoring images in real time based on a multi-sensor array; vehicle vibration time-frequency analysis is carried out on the vehicle running state monitoring parameters, vehicle vibration situation mining is carried out, and a vehicle vibration situation curve is constructed; road object recognition is carried out on the vehicle driving monitoring image based on the vehicle vibration situation curve, real-time road condition scene structure rendering is carried out, and a real-time road condition scene model is constructed; obtaining a historical behavior log of the vehicle; carrying out multi-time-point vehicle position calculation on the vehicle historical behavior log, and carrying out dynamic vehicle movement track reconstruction so as to construct a historical vehicle dynamic track; and performing dynamic behavior feature analysis on the historical vehicle dynamic trajectory, and constructing a personalized driving portrait of the vehicle owner. According to the invention, efficient and rapid tire pressure sensor activation is realized.

A road topology detection method, which can be applied to the field of artificial intelligence and the field of autonomous driving. The method comprises: acquiring target information, wherein the target information is data collected regarding a road condition of a target road; on the basis of the target information, obtaining sequence information by means of a deep learning model, wherein the sequence information comprises point information of feature points in a topology of the target road and edge information of feature lines in the topology of the target road, the point information comprises physical positions and categories corresponding the feature points, and the edge information comprises a connection relationship and connection direction between the feature points; and determining the topology of the target road on the basis of the sequence information. In the present application, by means of directly predicting information of nodes (feature points) and information of edges (feature lines) in a stage, accumulative errors of an overall road structure caused by an error of each stage are avoided, and by means of fusing data of the nodes and data of the edges, mutual promotion is achieved in a network, thereby increasing the precision of a final result.

The invention relates to the technical field of intelligent driving, and discloses an obstacle avoidance analysis system for intelligent driving. The system comprises a data acquisition module, a road feature extraction module, a lane type judgment module, a virtual marking generation module, an obstacle detection and trajectory prediction module, a dynamic obstacle avoidance decision module and a system verification and optimization module. The method comprises the following steps: acquiring data through a laser radar, a camera and the like; fusing point cloud and an image to extract the width and curvature of a road; judging the type of a lane according to the width; generating a virtual marking line on a marking-free road, fitting by using a B-spline curve when the curvature is large, fusing the point cloud and the image to detect an obstacle, and predicting a track by using Kalman filtering or a social force model. In combination with virtual marking and risk assessment, a safe trajectory is generated through an OccupanyGrid map, an A * algorithm and MPC, and a simulation and real road test optimization system is adopted, so that the problem of marking-free road trajectory planning deviation is solved, and the obstacle avoidance precision and the road condition adaptability are improved.