27 results about "Road grading" patented technology

The invention discloses a divided-lane speed-limiting early warning indication method based on rainfall intensity. The method comprises the following steps: collecting monitored rainfall in a red line domain of a speed-limiting road and calculating the rainfall intensity; calculating the road surface water film thickness of each lane based on the rainfall intensity value; calculating the safety critical vehicle speed of each lane based on the road surface water film thickness of each lane; and issuing a speed limit value of each lane based on the safety critical vehicle speed of each lane and an originally set speed limit value of the road, and issuing a corresponding lane warning mark based on the water film thickness of each lane. The invention further discloses a divided lane speed limiting early warning indication system based on the rainfall intensity. The system comprises a rainfall monitoring module, a critical vehicle speed calculation module and a judgment display module. Different speed limit information is issued for different lanes according to rainfall conditions in real time, the problems that rainfall weather factors are neglected in existing speed limit management and road speed limit is set to be a fixed value according to road grades are solved, and the safety and toughness of a road to cope with different rainfall weather are improved.

The invention discloses a divided lane speed limit early warning indication method based on the road surface water film thickness. The road surface water film thickness of each lane in a red line domain of a speed limit road is collected; calculating the safety critical vehicle speed of each lane based on the road surface water film thickness of each lane; and issuing a speed limit value of each lane based on the safety critical vehicle speed of each lane and an originally set speed limit value of the road, and issuing a corresponding lane warning mark based on the water film thickness of each lane. The invention further discloses a divided lane speed limiting early warning indication system based on the road surface water film thickness. The divided lane speed limiting early warning indication system comprises a water film thickness monitoring module, a critical vehicle speed calculation module and a judgment display module. Different speed limit information is published for different lanes according to the water film thickness of each lane in real time, the problem that the road speed limit is set to be a fixed value according to the road grade is solved, and the safety toughness of the road to cope with different rainfall weathers is improved.

This invention discloses a method for predicting traffic conditions in large-scale urban road networks based on graph neural networks. The method first acquires road network topology and traffic time-series data, extracts road hierarchy information, and constructs graph data. The core lies in building a spatiotemporal graph neural network model that integrates dedicated road hierarchy components: a road hierarchy embedding layer maps discrete levels to continuous vectors to enhance feature semantics; a hierarchical global context module aggregates the global states of similar roads with linear complexity; and a dynamic cross-level attention module models the dynamic interaction dependencies between roads of different levels. This method suppresses noise interference from low-level roads through a hierarchical differentiation strategy and uses a separate weighted loss function for training. This invention effectively solves the problems of insufficient global perception, missing interaction modeling, and significant noise interference caused by neglecting road hierarchy semantics in existing technologies, significantly improving the accuracy and robustness of traffic condition prediction for large-scale urban road networks.

The invention discloses a navigation voice broadcasting method and device, electronic equipment and a storage medium, and relates to the technical field of navigation.The method comprises the steps that a navigation destination request is obtained, and common city information is determined through an address information query module; vehicle running state data including administrative city codes, road grade identification values and subsequent action states are monitored in real time; based on the common city information and the vehicle running state data, intelligent guide judgment is executed, and a detailed broadcast mode recommendation instruction or a simple broadcast mode recommendation instruction is generated; and in response to the recommendation instruction, controlling the touch display panel to render the interaction window, and updating the voice broadcast mode state according to the user touch input state value or the overtime default confirmation logic. Detailed broadcasting is recommended when the vehicle is in a non-common city and the road condition is complex, simple broadcasting is recommended when the vehicle is in a common city and the road condition is simple, a countdown automatic confirmation mechanism reduces the driving interaction load, and warning tone conflicts are avoided by detecting the audio channel occupation state.

A vehicle route guidance device includes a processor configured to predict a deviation point when route guidance is performed, and a storage device configured to store data and algorithms executed by the processor. The processor is configured to calculate a deviation possibility score using at least one of a first score based on an overlapping section between a previous route and a subsequent route with respect to the deviation point, a second score based on road grades of the previous and subsequent routes, and a third score based on estimated times of arrival of the previous and subsequent routes when a route deviation occurs during the route guidance.

This invention relates to a vehicle differential lock control method, terminal device, and storage medium. The method includes: S1: constructing a first fuzzy membership function, a second fuzzy membership function, and a fuzzy rule inference table; S2: real-time acquisition of environmental values, road grade, and road curvature values, obtaining the maximum value of road curvature; S3: obtaining all corresponding first fuzzy intervals and fuzzy values ​​from the first fuzzy membership function based on the road curvature ratio; S4: obtaining all corresponding second fuzzy intervals and fuzzy values ​​from the second fuzzy membership function based on the environmental values; S5: matching the first fuzzy intervals with the second fuzzy intervals to obtain a combined fuzzy value of the fuzzy interval combination; S6: taking the fuzzy interval combination corresponding to the maximum value as the fuzzy decision combination, and obtaining the decision result from the fuzzy rule inference table; S7: controlling the differential lock based on the decision result. This invention achieves timely differential locking of the vehicle in weather conditions such as rain and snow when slight slippage occurs during normal driving.

The invention provides a vehicle suspension open-loop control method, system and equipment based on road preview, which are applied to the technical field of vehicle suspension control, and the method comprises the following steps: obtaining road elevation data in a vehicle longitudinal preview range, and determining a target road type and target road related information according to the road elevation data; the target road type comprises a continuous random road and / or a mixed road, the continuous random road and the mixed road have different control requirements on the suspension, and the target road related information is used for representing the road grade and / or the road amplitude corresponding to the road in the longitudinal preview range; determining a matched target optimal control current in a suspension control law according to the target road related information, and transmitting the matched target optimal control current to a suspension system module, so that the suspension system module performs open-loop control on a suspension based on the target optimal control current; the suspension control law comprises various kinds of different road related information and the optimal control current corresponding to each kind of road related information. By adopting the technical scheme, the calculation load can be reduced.

The application discloses a kind of different types tunnel construction clearance automatic generation method, step (1), establish standard database, tunnel road grade, design speed, lane number and left side maintenance road width, right side maintenance road width, left side lateral width, right side lateral width, driving lane width, residual value one-to-one corresponding storage;Step (2), obtain the tunnel road grade, design speed, lane number input by user, and generate standard construction clearance basic parameters by matching with standard database;Step (3), receive the individualized parameter input by user, including left side widening value, right side widening value, emergency parking width, emergency parking lane cross slope value, maintenance road height;Step (4), set reference point three-dimensional coordinates, based on basic parameters and individualized parameters, the three-dimensional coordinates of each feature point of construction clearance are calculated by trigonometric function;Step (5), according to the feature point coordinates, tunnel construction clearance graphics are automatically drawn and layer, line type and color identification are completed, and the automation and intelligent level of tunnel engineering design is significantly improved.

The application discloses a kind of based on quantification total risk loss Large-scale city bridge tunnel network evaluation method, the method is: one, to open source geographic platform crawler, obtain road network topology data and establish bridge tunnel network model;Two, according to road grade and intersection situation, the edge and node of bridge tunnel network model are simplified;Three, traffic speed data is converted, and daily traffic volume data is obtained;Four, the monomer structure score data in detection report is converted, and monomer structure failure probability is obtained;Five, define spatial non-homogeneous Markov chain simulation structure state evolution;Six, define the direct and indirect total loss quantification formula considering structure failure, casualties, traffic closed detour and additional carbon emission;Seven, the long-term annual total risk loss of a city large-scale bridge tunnel network is quantified and evaluated.The method solves the current evaluation bridge tunnel network method only concerns safety loss and lacks consideration of functional loss, and is suitable for efficient management and maintenance of large-scale bridge tunnel network.

A city-level electric vehicle charging / exchanging feedforward collaborative scheduling method fusing threshold state of charge prediction and user decision updating, comprising: acquiring multi-source data of electric vehicles in a city area; identifying vehicles that may trigger energy supplement behavior in a rolling window to form expected arrival rate and arrival time distribution of each site in each period; calculating the comprehensive value of the candidate site; when detecting that the road traffic flow rate is lower than the road level threshold, re-estimating the value function in real time and judging whether to replace the original planned path; superimposing the obtained vehicle selection probability and arrival rate to form short-term load prediction of each site; jointly optimizing the guide parameters in the rolling window; collecting user actual arrival rate, guide acceptance rate and queuing time data to periodically update key parameters in the feedforward model. The invention simultaneously describes the coupling mechanism of multi-vehicle type threshold triggering, decision updating in driving and queuing backflow, effectively improving the accuracy of city-level charging / exchanging load prediction and the coordination of site resource allocation.

The invention provides an active suspension preview control method, device and system based on a signal game, and relates to the technical field of vehicle suspension intelligent control, and the method comprises the steps: determining a first road grade index of a road based on preview sampling information; determining a second road grade index of the road based on the road amplitude of the road; by taking the optimal smoothness index of the vehicle as a target and taking a signal game theory as a game theory guidance method, performing game decision on the first road grade index and the second road grade index to obtain an optimal road grade index of the road; based on the optimal road grade index, determining an optimal weight coefficient combination corresponding to the LQR suspension controller; and determining the optimal action force based on the optimal weight coefficient combination. The problem that in the prior art, the control effect of the suspension is affected when the apparent elevation value and actual road excitation are different is solved, prediction of the elevation value of the road in front of the vehicle is achieved, and the smoothness of the vehicle is effectively improved.

This invention discloses a method and system for constructing a high spatiotemporal resolution motor vehicle pollutant emission inventory, relating to the field of motor vehicle pollution monitoring technology. The method includes: acquiring vehicle driving information and regional electronic road network linear element files, preprocessing them to obtain a standardized motor vehicle activity dataset and a standardized road network file; matching vehicle trajectories with the regional road network to obtain the vehicle's travel volume on each road segment; determining the vehicle category on the road segment, acquiring historical travel volume data corresponding to the current category of vehicles, finding the historical travel volume most similar to the current category of vehicles from the historical travel volume data, and using the instantaneous pollutant emission rate corresponding to the historical travel volume as the instantaneous pollutant emission rate corresponding to the travel volume; and weighting the instantaneous pollutant emission rates of the road segment according to road length, road grade, and operational complexity to generate an emission inventory with kilometer-level spatial resolution.

A method for operating a vehicle, wherein a check is carried out to determine whether a switch of the driving mode can be safely carried out and a corresponding output is provided, wherein an estimation is made as to whether the road class traversed by the vehicle corresponds to a target road class, including the steps of: ascertaining the position of the vehicle using a satellite navigation system, comparing the ascertained position with a road map, wherein the road map includes an allocation of roads according to road classes, and determining whether a road of the target road class is located in a specified area around the ascertained position on the road map.

The present application belongs to the technical field of traffic facility modeling, and particularly relates to a lane dynamic offset and model generation method for a bus stop. First, when a road section is updated, the lane change is judged in linkage, and the corresponding stop is automatically cleared or processed. Second, the validity of the bus stop is verified based on the range of the lane section post number, ensuring that it is completely located within the lane. Then, an accurate 3D model is generated for the effective stop, and the supporting road marking is drawn synchronously. This method innovatively realizes the linear gradual change of the offset amount of the harbor type stop with the post number, avoiding sudden changes in form. At the same time, the model and the marking are calculated based on a unified reference, ensuring that the positions are coordinated without misplacement. The whole process is automated, without the need for manual intervention, supports various stop types such as direct and harbor types, and the core parameters can be flexibly configured, which can adapt to the design standards of different cities and road grades, significantly improving the modeling accuracy and efficiency.

This disclosure provides a method, apparatus, device, and storage medium for determining road grade information, relating to the field of data processing technology, particularly artificial intelligence, intelligent transportation, map navigation, and large-scale model technology. The specific implementation scheme includes: acquiring road network topology data and crowdsourced trajectory data of the area to be updated; identifying target judgment units suspected of grade anomalies based on the matching relationship between the crowdsourced trajectory data and the road network topology data; determining the visual semantic information of the target judgment unit at a preset spatial location; and inputting the visual semantic information and a preset road engineering knowledge base into a visual language model for reasoning to obtain the updated road grade information of the target judgment unit. This disclosure achieves accurate perception of road grade determination by identifying abnormal trajectory data and extracting corresponding image data, thereby utilizing visual large-scale model semantic reasoning.

The invention discloses a road grade identification method based on vehicle height response, and relates to the technical field of automobile chassis control. The method comprises the steps that the vehicle height response characteristic, the damping asymmetry rate of the semi-active suspension and the vehicle running speed are collected in real time; and inputting the road grade into a neural network model constructed based on the inverse propositional logic, and deducing and outputting the road grade. According to the method, change signal characterization characteristics of a vehicle body balance position are utilized, driver manipulation interference is eliminated through frequency domain energy gating, and an active sensitivity detection mechanism is introduced to carry out secondary confirmation on a low-confidence result. According to the method, a special expensive sensor is not needed, high-precision road surface recognition can be realized by utilizing the existing chassis configuration, the cost and the computing power consumption are effectively reduced, and the self-adaptive control capability of the suspension system is improved.

Bus stop system, characterized by: a) Classification of roads into yellow (country roads) and blue (motorways), b) Arrangement of stopping areas exclusively at intersections of the following types: - Yellow-Yellow (YY), - Yellow-Blue (YB), - Blue-Blue (BB), c) Average distance of 20km between adjacent stopping areas.

The invention relates to an asphalt pavement visual blackness evaluation method and system based on human eye visual simulation, and the method comprises the steps: collecting apparent optical parameters of a monitoring point, inputting the apparent optical parameters into an asphalt pavement visual blackness comprehensive evaluation model, and obtaining a blackness comprehensive score; the asphalt pavement visual blackness comprehensive evaluation model is obtained by training a blackness perception intensity model and a color deviation perception intensity model by using a training set; the training set comprises original optical parameter data; in the training process, the maximum value of the Spearman level correlation coefficient between the output value of the blackness comprehensive evaluation model and the subjective score is taken as an optimization target, and the coefficients in the model are iteratively optimized by adopting an optimization algorithm to determine the final model coefficient. According to the method, the tiny attenuation of the apparent characteristics of the pavement can be identified before the pavement is subjected to structural damage, and early warning and preventive maintenance support are realized. Meanwhile, evaluation thresholds are self-defined according to road grades, regional climate and maintenance standards, and different materials and regional road conditions are adapted.

This invention discloses a method and system for localizing the calibration of carbon emission factors for motor vehicles, comprising the following steps: S1, collecting high-spatiotemporal precision trajectory data of motor vehicles at the second-by-second level and performing hierarchical completion or elimination processing; S2, projecting the preprocessed discrete trajectory data points onto the road network of a traffic geographic information system and performing map matching to obtain a continuous driving path; S3, dividing the journey into multiple driving units based on the road grade and traffic service level of the matched road segments; S4, extracting features, standardizing them, and then selecting typical operating conditions in the feature space; S5, matching the typical operating conditions with a benchmark emission factor model operating condition library, inheriting or interpolating emission factors for matching operating conditions, and dynamically correcting non-matching operating conditions to obtain emission factors; S6, constructing a localized carbon emission factor library; S7, measuring exhaust gas concentration and calculating the measured emission factors, thereby achieving a high-precision, high-reliability, and verifiable localized motor vehicle carbon emission factor library at a low cost.

The application discloses a kind of distributed road weight training methods.The method is first obtained target area motor vehicle travel trajectory and is converted into learning target section sequence using map matching algorithm;Subsequently, the road section characteristic vector containing multi-dimensional objective attributes such as road grade, type and real-time traffic speed is constructed, and the training set containing positive and negative samples is constructed in combination with temporarily generated temporary coefficient;The training set is hashed into fragments using distributed computing architecture, the local update result of each training node is summarized by parameter server, and the global road weighting coefficient is generated based on road network topology constraint to perform gradient arbitration;Before outputting the final coefficient, construct virtual traffic to perform road network load balancing deduction, and use the penalty mask fed back to correct the feature dimension that causes abnormal aggregation by weighting reduction.The application realizes the accurate inversion of large-scale road network traffic cost by deep ordering learning and distributed parallel computing, effectively improves the rationality of path planning and the balance of road network flow.

The invention discloses a modified ardealite-based artificial aggregate and application thereof in a road base, and a preparation method of the artificial aggregate comprises the following steps: crushing and screening ardealite, mixing and stirring with composite modified slurry, and forming and curing to obtain the modified ardealite-based artificial aggregate. According to the preparation method, the phosphogypsum is subjected to enhancement and hydrophobic modification through the composite modified slurry, the prepared artificial aggregate has excellent mechanical properties and water stability, the 24-hour water absorption rate is lower than 5%, the surface hydrophobic angle is larger than 150 degrees, the dissolution rate of soluble phosphorus and soluble fluorine is reduced by 85% or above compared with that of unmodified homologous phosphogypsum, and the prepared artificial aggregate has a good application prospect. The artificial aggregate is used as a main aggregate for a road base material, and the unconfined compressive strength and the strength retention rate after water immersion of the artificial aggregate are subjected to formula adjustment according to a road grade or a structural layer position and are not lower than those of existing cement concrete; bulk and high-value utilization of the ardealite is realized, and the method has remarkable environmental protection and economic benefits.

The invention discloses a low-traffic-volume road grading detection evaluation method and device based on pavement performance prediction, and the method comprises the steps: constructing an annual average daily traffic volume condition inference tree of a to-be-evaluated region, and obtaining a traffic volume grouping boundary; performing road network screening on the to-be-evaluated region based on the traffic volume grouping boundary, and selecting target road sections of different traffic volume groups; constructing a PPI attenuation curve based on the composite pavement performance decay function, and obtaining a maintenance time window based on the PPI attenuation curve; based on the maintenance time window, constructing an objective function about the comprehensive detection cost of the operation period, and adopting Weibull distribution considering the reliability to restrain the probability of missing detection risk in the objective function; and on the basis of the objective function and the traffic volume classification, determining recommended regular check periods of different traffic volume groups, thereby solving the problem that a classification detection mode for low-traffic-volume roads in the traditional technology cannot form a complete comprehensive evaluation system.

The invention discloses a high temporal-spatial resolution motor vehicle pollutant emission list construction method and system, and relates to the technical field of motor vehicle pollution discharge monitoring, and the method comprises the steps: obtaining vehicle driving information and a regional electronic road network linear element file, carrying out the preprocessing, and obtaining a standardized motor vehicle activity data set and a standardized road network file; matching the vehicle track with the regional road network to obtain the driving amount of the vehicle on each road section; determining the types of vehicles on the road section, obtaining historical driving amount data corresponding to the vehicles of the current type, and finding the historical driving amount most similar to the driving amount of the vehicles of the current type from the historical driving amount data; taking the instantaneous pollutant emission rate corresponding to the historical driving amount as the instantaneous pollutant emission rate corresponding to the driving amount; and weighting the instantaneous pollutant emission rate of the road section according to the road length, the road grade and the operation complexity to generate an emission list of kilometer-level spatial resolution.

The present disclosure provides a data processing method and device, electronic equipment and storage medium for generating a lane-level map, relating to the technical field of artificial intelligence. The specific implementation scheme is: obtaining road data of an original map, a target road level and a target kilometer number; performing road segmentation on the road data based on the target road level to generate a work unit; clustering the work unit based on the target kilometer number to obtain a work material package; and outputting the work material package. The present disclosure does not require manual segmentation of the road data of the original map, thereby solving the technical problem of low lane-level map generation efficiency in related technologies, and further achieving the technical effect of improving the lane-level map generation efficiency.

This invention discloses a method for identifying abnormal driver speed changes based on vehicle GPS data. First, the pre-acquired vehicle GPS data is preprocessed. Second, discrete speed change thresholds are calculated by dividing the GPS data into bins using vehicle speed and acceleration as key indicators, and determining the discrete acceleration threshold for each bin using the characteristic line method and elbow diagram method. Then, dynamic speed change thresholds are fitted by fitting the discrete thresholds using a modified least squares method to obtain the dynamic threshold for abnormal driver speed changes, and a mathematical equation for this threshold is derived. Finally, abnormal speed change behavior is identified by substituting the dynamic threshold equation into historical or real-time data. This invention does not rely on complex environmental parameters such as road grade, weather conditions, or traffic status, and possesses real-time performance, accuracy, and adaptability. It is applicable to driver abnormal speed change identification in various environments and can be practically applied in pre-emptive driving prevention and proactive management platforms.

The invention relates to the field of intelligent traffic monitoring and unmanned aerial vehicle video analysis, and particularly discloses an unmanned aerial vehicle image-based vehicle congestion degree estimation method, which comprises the following steps of: firstly, constructing a high-precision road region-of-interest mask from an unmanned aerial vehicle overlook road image by utilizing a semantic segmentation technology; and replacing a global statistical direction with a local tangent flow field generated by a mask, and carrying out adaptive spatial sorting conforming to a road topological structure on vehicles through path integration. And then, in combination with real-time pose data of the unmanned aerial vehicle and a precise photogrammetry model, reversely mapping vehicle pixel coordinates into WGS-84 geodetic coordinates, and precisely calculating a real ground surface physical distance between the head vehicle and the tail vehicle. And furthermore, the congestion degree estimation of the queuing length is realized by matching a road grade congestion threshold value. Therefore, the estimation error caused by perspective deformation and curve interference is overcome from microscopic vehicle topological sequence construction to macroscopic geographic queuing length quantification.