69 results about "Road grading" patented technology

The invention discloses a road grading speed limiting method based on deep learning, and the method comprises the steps: obtaining expressway flow parameters, inputting the expressway flow parameters into a strategy model, and enabling the strategy model to output a grading speed limiting strategy based on the expressway flow parameters; inputting the grading speed limit strategy into an evaluation model, and outputting grading speed limit evaluation indexes by the evaluation model; and according to the evaluation index and based on a graded speed limiting strategy, adjusting a speed limiting indication sign arranged beside the expressway. According to the technical scheme provided by the invention, the traffic smoothness can be effectively improved, and the expressway graded speed limiting method based on deep learning has the advantages of high reliability, low cost and high expandability.

The invention discloses an urban road and form integrated intelligent generation method based on a heterogeneous graph network. The method comprises the following steps: collecting a target design site and road, plot and building data of a city where the target design site is located; selecting a planning background data range; constructing a heterogeneous graph structure adjacent matrix, and performing feature extraction and model pre-training to obtain a pre-trained heterogeneous graph neural network based on a road structure and an urban form; importing the planning background data into the pre-training model, and predicting road and form integrated generation of the target design site; planning background data is mapped to a potential space, road and plot morphological parameters are learned, the parameters are optimized in combination with a cross entropy loss function and a back propagation algorithm, and a road network and spatial morphological scheme matched with the plot features is generated; and finally, realizing visual display of the scheme through a UMAP method and a three-dimensional rendering engine, and outputting professional drawings and interactive three-dimensional models containing parameters such as road grades, land use functions, building density and the like.

A method for providing traffic information to a vehicle occupant may include identifying a junction location in the environment surrounding the vehicle. The junction location is the point of intersection between a first road of a first road class, on which the vehicle is traveling, and a second road of a second road class. The first road class is lower than the second road class. The method may further include determining traffic data about one or more long-distance vehicles traveling on a segment of the second road adjacent to the junction location. The method may further include determining an estimated waiting time for the vehicle, based at least in part on the traffic data and a distance between the vehicle and the junction location. The method may further include performing an initial action based at least in part on the estimated waiting time.

The invention discloses a garden night light control method and system, and the method comprises the steps: obtaining the historical pedestrian flow of each road in a garden region in a night time period, determining the road flow grade of the road according to the historical pedestrian flow, marking the intersection region between the roads, and obtaining the intersection region; when the intersection area is an intersection position of a road with a high road flow level and a road with a low road flow level; acquiring a monitoring video of monitoring equipment in a night time period, wherein the monitoring range of the monitoring equipment is over against an intersection position and covers an intersection area on a road with a low road grade; analyzing the walking direction of the pedestrian in the obtained monitoring video, and when it is detected that the walking direction of the pedestrian faces the road with the low road grade, obtaining lamps arranged on the road with the low road grade and corresponding lamp information; and the arranged lamps are sequenced according to the distances from the intersection position, and the lamps from the intersection position are controlled to be turned on. And lighting resources are saved, and the pedestrian flow of roads with high road flow grades is reduced.

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 subject disclosure relates to estimating a grade of a road and calibrating sensors of an autonomous vehicle based on the grade. A process of the disclosed technology can include effectuating an autonomous vehicle to perform one or more maneuvers that cause the autonomous vehicle to face different directions along a road, obtaining a first set of sensor measurements of an autonomous vehicle in a first pose, wherein the first set of sensor measurements includes a first measured specific force, obtaining a second set of sensor measurements of the autonomous vehicle in a second pose, wherein the second set of sensor measurements includes a second measured specific force, determining a direction of gravity based on the first measured specific force and the second measured specific force, and calibrating at least one sensor of the autonomous vehicle based on the determined direction of gravity.

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.

The present application belongs to the field of vehicle control technology, and particularly relates to a kind of automobile off-road mode automatic exit control method, the control method includes road surface identification method and mode exit method;When vehicle manually enters off-road mode, vehicle obtains the signal of rainfall sensor, judges whether current is rain and snow weather, if it is rain and snow weather, control vehicle to enter off-road mode;Vehicle obtains position information and weather information of current position through Internet of Vehicles, judges the road condition information of current position, determines whether there is road wet or icing warning information, if there is road wet or icing warning information, control vehicle to enter off-road mode;Vehicle determines the road grade and road material of current driving road surface according to position information.It overcomes the shortcomings of prior art, formulates the automatic exit mechanism of off-road mode through the collection of road surface condition and driving condition, can minimize fuel consumption, protect transmission system parts and reduce failure rate.

The present invention proposes a method for planning an airport collection and distribution network based on multi-objective optimization, the method comprising: taking the airport radiation range determined according to the functional positioning of the target airport and the upper-level planning as the research area, screening out the nodes corresponding to the air travel demand; determining the importance of the connecting sections between adjacent nodes according to the preset node importance evaluation index in the region, and preliminarily determining the airport collection and distribution road network architecture using the spatial connected circle method according to the importance of the sections; constructing a multi-objective road network optimization model for the airport collection and distribution network with the goal of minimizing the total travel time and minimizing the construction cost of different road grades, and obtaining the optimal road network planning scheme combination by solving the model. The method provided in this application combines the traffic characteristics of the airport hub itself, determines the road network planning scheme by building an optimization model, and improves the overall operating efficiency of the airport collection and distribution road network. At the same time, the optimization scheme can better adapt to the development of airport collection and distribution traffic.

A method of optimizing energy consumption includes receiving vehicle route information, generating a baseline vehicle route profile including a baseline energy consumption and a baseline travel time, and processing the baseline vehicle route profile by detecting one or more changes in a road grade. For each detected change in the road grade, the method includes triggering a breakpoint to divide the baseline vehicle route profile into a plurality of segments, each segment in the plurality of segments having a baseline vehicle velocity, and for each segment in the plurality of segments, performing local optimization to generate a modified vehicle velocity. The method also includes generating an updated vehicle route profile and performing global optimization on the updated vehicle route profile to generate an optimized vehicle route profile, the optimized vehicle route profile having a different energy consumption than the baseline energy consumption.

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 embodiment of the invention provides an aircraft operation method based on a mobile hangar and a flight system. The mobile hangar is detachably connected with an aircraft. The method comprises the following steps: generating an operation area based on a road grade of a road network in a task area and an operation range of an aircraft; the road network comprises roads of one or more road grades; the union set of all the operation areas covers at least part of the task area; determining track points of the mobile hangar in each operation area, and generating a planned path passing through all the track points; the planned path comprises roads under at least one road grade; and controlling the aircraft to execute the operation task in each operation area while controlling the mobile hangar to travel along the planned path. Therefore, when the mobile hangar travels along the planned path, the unmanned aerial vehicle executes the operation task in each operation area, and better cooperative operation of the mobile hangar and the aircraft is achieved.

The invention relates to the technical field of road cleaning operation path optimization and navigation planning, in particular to an intelligent road cleaning path navigation planning method and system, which comprises the following steps: S1, data acquisition and storage: acquiring static data, real-time data and historical data, the static data comprising road grade, vehicle type exclusive operation parameters and cleaning standard; s2, data preprocessing and binding rule establishment: filtering three types of abnormal values through a local edge computing node of the vehicle-mounted terminal, wherein the three types of abnormal values are GPS positioning drifting, mechanical sweeping vehicle speed abnormity and water spraying amount super-normal fluctuation; s3, dual-scene route planning: planning a peak avoidance route by adopting an A * algorithm for a machine scanning / watering scene, and planning a load balancing route by adopting a Dijkstra algorithm in combination with a minimum spanning tree algorithm for a collection and transportation scene; s4, abnormal response and cross-regional scheduling are carried out; step S5, navigation pushing and operation state supervision: when the state is abnormal, triggering graded alarm; and S6, data feedback and model optimization.

The invention discloses a tourist road grading method and device, electronic equipment, a storage medium and a computer program product. The method comprises the following steps: acquiring a road network data set; based on the road network data set and a pre-trained classification model, determining a weight coefficient level of each traffic node and each scenic spot; inputting the weight coefficient grade of each traffic node, the weight coefficient grade of each scenic spot and the road network data set into a preset path search algorithm for processing, and generating a global path data set; and generating a multi-level road network based on each shortest path in the global path data set. By adopting the method of the invention, the accuracy of tourist road identification can be improved.

Method for monitoring an admissibility condition (21) for an automated driving mode of a motor vehicle (10) by a control circuit (18), wherein the admissibility condition (21) includes that the driving mode may only be active when driving on roads (16) of at least one predetermined approved road class (29, 31), and for checking a current road class (29, 31) of a road segment (17) ahead in the direction of travel (15) of a road (16) currently being traveled in a first signal path (19): from a digital road map (27) to the road segment (17), map data indicating the road class (29, 31) of the road segment (17) are read out from a data memory and then checked to see whether they indicate the at least one permitted road class (29, 31), wherein, independently of the first signal path (19), in a second signal path (20) operated independently of the first signal path (19), environmental data (33) describing respective environmental features of the road segment (17) are received from an environmental sensor (32) or a plurality of environmental sensors (32), characterized in that independent of the first signal path (19) in the second signal path (20) operated independently of the first signal path (19), characteristics (38) of the road segment (17) are estimated by means of an estimation routine (35) on the basis of the surrounding data (33), and it is detected whether the estimated characteristics (38) in the road segment (17) lying ahead signal a probability (30, 41) of the presence of the at least one permitted road class (29, 31) that is greater than a predetermined threshold value, and checking the admissibility condition (21) comprises signaling the presence of the at least one permitted road class (29, 31) for the road segment (17) ahead only if both the first signal path (19) and the second signal path (20) confirm the existence of the road class (29, 31), and otherwise carrying out a predetermined blocking measure (22) for the driving mode.

A refined calibration method for a simplified car-following model based on multi-source data belongs to the field of intelligent transportation technology. In order to improve the accuracy of the simulation results of the simplified car-following model, the present invention includes obtaining road network data, geomagnetic detection equipment data, geomagnetic detection data, floating vehicle GPS Data is collected; roads are divided into different grades and roads are interrupted and merged; data is processed to remove missing or abnormal data; the average vehicle length of different types of vehicles is calibrated; the average headway between different types of vehicles at rest is calibrated through intersection surveys; the average effective vehicle length of different types of vehicles at rest is calculated; the free flow speed of different grades of roads is calibrated; the capacity of different grades of roads and the corresponding average effective vehicle length are calibrated based on processed geomagnetic detection data; and the driver reaction time coefficient of different grades of roads is calculated. This invention improves the accuracy of the simulation results of the simplified car-following model.

The invention discloses a large-scale urban bridge and tunnel network evaluation method based on quantitative total risk loss, and the method comprises the steps: 1, carrying out the crawling of an open-source geographic platform, obtaining the topological data of a road network, and building a bridge and tunnel network model; 2, simplifying edges and nodes of the bridge and tunnel network model according to road grades and intersection conditions; 3, converting the traffic speed data to obtain daily average traffic volume data; 4, performing conversion on the single structure score data in the detection report to obtain a single structure failure probability; 5, defining spatial inhomogeneous Markov chain simulation structure state evolution; 6, defining a direct and indirect total loss quantification formula considering structural failure, casualties, traffic closed detour and additional carbon emission; and 7, carrying out quantitative evaluation on the long-term annual total risk loss of the large-scale bridge and tunnel network of a certain city. The method overcomes the defect that the current bridge and tunnel network evaluation method only concerns the safety loss and lacks consideration on the function loss, and is suitable for efficient management and maintenance of the large-scale bridge and tunnel network.

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.

The invention discloses a dynamic emergency path planning method based on graph correlation analysis, and the method comprises the steps: carrying out the road network matching of obtained vehicle track points, and obtaining the actual road network speed; obtaining the maximum speed limit according to the road grade, and comparing the maximum speed limit with the actual speed of the vehicle to obtain the actual congestion value of the road network; the congestion value of each intersection in the road network is calculated, then the actual congestion value of each intersection is calculated through an IPR algorithm to obtain the congestion sorting result of each intersection in the current road network, and the intersection with the low congestion value is preferentially recommended to the vehicle for driving; when the actual congestion value of the intersection is smaller than the congestion threshold value, the NCIRW is recommended to be used for calculating intersection selection in a non-congestion state, and a non-congestion path is planned for the vehicle according to the result of the NCIRW; and when the actual congestion value of the intersection is greater than or equal to the congestion threshold value, recommending CIRW to the vehicle to calculate the intersection which needs to be passed by the next step, and planning a non-congestion path for the vehicle according to the result of the CIRW.

The invention discloses a man-machine interaction city block outdoor space efficiency diagnosis and optimization method, which is suitable for block updating and reconstruction and new development and construction projects in a high-density city environment. Aiming at the problems that the efficiency of the outdoor space in the existing urban environment is easily neglected and a special evaluation and optimization system is lacked, the method comprises the following steps: dividing the outdoor space into space units according with human behavior perception based on a convex space division method; a multi-dimensional evaluation index system including space unit depth, connectivity and accessibility and plot configuration diversity and accessibility is constructed, and a man-machine interaction measurement and calculation program is built based on a Grasshopper platform. The program can feed back dynamic changes of indexes in real time, automatically adjust according to factors such as road grades, entrance and exit positions and building layout, assist a designer in rapidly adjusting a scheme in a design stage and obtaining a real-time evaluation result, realize accurate diagnosis and optimization of outdoor space efficiency, and improve the efficiency of the outdoor space. And a scientific decision basis is provided for improving the utilization efficiency of the urban block space and promoting intensive development.

The invention relates to the field of vehicle control, in particular to a road grading method of a vehicle, electronic equipment and the vehicle. The method comprises the following steps: acquiring road environment information, and performing road risk grading according to the road environment information to obtain a first grading result; road passing information is obtained, road passing difficulty grading is carried out according to the road passing information, and a second grading result is obtained; and determining a final road grading result according to the first grading result and the second grading result. In this way, the obtained final road grading result is more accurate in combination with the road environment condition and the road traffic condition, and then the accuracy of route planning according to the final road grading result is also 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.

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.

Embodiments of the present invention provide a method and apparatus for vehicle assisted driving. The method comprises: upon detecting that a target vehicle has activated assisted driving, obtaining the map status of one or more associated maps for the assisted driving; determining the road grade of the target vehicle's current road; determining a target assisted driving mode for the target vehicle based on the map status and / or road grade; and initiating assisted driving according to the target assisted driving mode. Through embodiments of the present invention, assisted driving can be safely initiated in map overlap areas, avoiding vehicle accidents.