10 results about "Road grading" patented technology

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.

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 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.

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.

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.

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.

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.

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.