34 results about "Local road" patented technology

The invention relates to the technical field of intelligent traffic, in particular to a local road network flow change prediction method under the influence of traffic events. Comprising the steps of generating a high-quality mixed data set, constructing a road network dynamic graph structure fused with traffic event characteristics, constructing and training an end-to-end space-time prediction model based on self-adaptive dynamic partitioning and real-time data closed-loop and online rolling prediction. Through an innovative data generation strategy of'historical data calibration + high-fidelity traffic event simulation ', a large-scale, high-density, accurate-label and consistent-physical-significance'traffic event-traffic flow' mixed space-time synchronization data set is obtained, and the data bottleneck problem of road network flow prediction under traffic events is effectively solved. And a solid data foundation is laid for training and verification of a complex prediction model.

The invention provides a method for detecting vehicles retained in a highway section, and relates to the technical field of data processing, and the method comprises the steps: obtaining multi-source vehicle passing data in a target space-time scene, and enabling the multi-source vehicle passing data to be fused with each other through a vehicle identifier, and obtaining vehicle track data; constructing a candidate road network topology according to the vehicle trajectory data, and identifying abnormal road sections and abnormal events in the candidate road network topology; optimizing the candidate road network topology according to an identification result to obtain a local road network topology; selecting a target detection time window according to the to-be-detected time, and determining candidate remaining vehicles, missing detection vehicles and accident remaining vehicles of the to-be-detected road section in the target detection time window; and according to the candidate reserved vehicles, the missed vehicles and the accident retained vehicles, determining final reserved vehicles. By adopting the method for detecting the retained vehicles on the highway section, the road network topology can be dynamically updated, the retained vehicles are detected under the updated road network topology, and the accuracy of detecting the retained vehicles on the highway section is improved.

The invention discloses an autonomous positioning method and terminal equipment, and the method comprises the steps: obtaining a first observation position of a mobile body in a world coordinate system at a current moment, the first observation position being determined according to a positioning signal of a positioning sensor of the mobile body at the current moment; matching the first observation position with local road information in a road network map to obtain a target road point where the mobile subject is currently located, the target road point being a projection point of the first observation position in the road network map; calculating road network observation noise at the current moment by taking the position of the target road point as road network observation quantity according to the spatial network attribute information of the target road point; and according to the road network observation noise, determining an estimated position of the mobile body in the world coordinate system at the current moment so as to update the state of the mobile body according to the estimated position.

The application discloses a kind of based on transfer learning's city road network detectorless section flow real-time estimation method, belong to intelligent transportation system field.The method of the application includes city local road network similarity evaluation step and model transfer learning step, city local road network similarity evaluation step is by to the multi-dimension road network characteristics of influence road section flow quantitative analysis and weight assignment, establishes similarity evaluation system, to find the detector road network similar to the detectorless road section road network, model transfer learning step with the detectorless road section road network needing to be estimated as transfer learning target field, with the detector road network found by similarity evaluation system as source field, using fine-tuning method carries out the transfer learning of flow estimation model, to complete the flow real-time estimation of no data road section;The application fully excavates the influence law of city road network to flow, realizes high-precision flow estimation of no data road section with a brand-new data-driven thought.

The utility model provides an asphalt pavement water-permeable leveling stone water collecting system which comprises a non-perforated standing stone, a perforated standing stone, a one-way porous channel water-permeable leveling stone, a two-way porous channel water-permeable leveling stone, a rainwater hopper and a well lid, and the non-perforated standing stone and the one-way porous channel water-permeable leveling stone are sequentially arranged on the edge of an asphalt pavement from top to bottom. At least one first longitudinal water guide hole is formed in the one-way hole channel water-permeable horizontal stone in the length direction, a perforated standing stone is arranged on one side of the rainwater hopper, a two-way hole channel water-permeable horizontal stone is arranged on the side, away from the rainwater hopper, of the perforated standing stone, a water guide hole set is formed in the two-way hole channel water-permeable horizontal stone, and one end of the water guide hole set is communicated with the first longitudinal water guide hole. The other end of the water guide hole set is communicated with the perforated standing stone which is communicated with the rainwater hopper, and the manhole cover is arranged on the rainwater hopper. According to the utility model, the water guide holes are formed in the water-permeable leveling stone for collecting water, so that the water collecting capacity of the asphalt pavement in heavy rainfall is effectively improved; the water collecting system can be arranged along the whole road side of the urban road, and the problem of water accumulation caused by uneven local road surface of the road side of the asphalt road is solved.

A feeding device for an asphalt paving and compaction machine head relates to the technical field of asphalt paving and compaction operations, and includes a hinge frame, a support sleeve, a boom, a boom push-pull oil cylinder, and a support sleeve rotation oil cylinder. The support sleeve is horizontally rotatably hinged to the hinge frame, the boom slides back and forth through the support sleeve, the boom push-pull oil cylinder is connected between the boom and the support sleeve, one end of the support sleeve rotation oil cylinder is hinged to the support sleeve, the boom is a hollow structure, a material conveying device is arranged in the inner cavity of the boom, a feed inlet is opened at the upper end of the support sleeve, and a feed groove penetrating through to the inner cavity of the boom is longitudinally opened along the boom at the upper end of the boom. The feed inlet is located above the feed groove during the back-and-forth sliding of the boom. This feeding device for the asphalt paving and compaction machine head can stably convey asphalt materials between a vehicle loaded with an asphalt material container (such as an asphalt hot recycling vehicle) and a paving functional component and flexibly adjust the paving and compaction operation positions on a local road surface.

This invention relates to an autonomous navigation method for unstructured environments based on multi-level trajectory prediction and dynamic spatiotemporal risk assessment, belonging to the field of intelligent vehicles and autonomous driving technology. The method includes the following steps: S1: Extracting the current state of the vehicle, the state of surrounding vehicles, and local road network topology information based on sensor and map data; S2: Constructing a short-term kinematic prediction model based on CTRV; S3: Constructing a background vehicle topology intent assessment model based on the joint weights of map features and motion features; S4: Predicting the long-term trajectory of the background vehicle based on topology intent and target pose; S5: Constructing a dynamic spatiotemporal risk field to identify vehicles in hazardous environments; S6: Constructing a local trajectory planning model based on the dynamic risk assessment results and target task constraints; S7: Constructing an LQR controller that combines feedforward-feedback tracking and continuous TTC monitoring, and realizing vehicle navigation control based on the local trajectory planning results.

The invention discloses a road network crack repairing method based on federated learning, and belongs to the technical field of road network repairing. In order to solve the problems of data islands, privacy leakage risks, poor strategy adaptability and the like in existing road network fracture repair, the method constructs a federated decision architecture comprising local decision nodes, a federated decision center and an environment sensing module, generating a local repair strategy set through multi-objective optimization; the federation decision center integrates the local strategies to form a global restoration strategy pool, and a global optimal restoration strategy set is generated through multi-objective optimization; and the global strategy is dynamically adjusted in combination with real-time data of the environment sensing module and then is issued to the restoration execution unit. According to the method, multi-participant collaborative decision-making is realized on the premise of protecting data privacy, conflict targets are balanced through multi-target optimization, decision-making quality is continuously optimized by means of real-time dynamic adjustment and closed-loop learning, and road maintenance efficiency and quality are remarkably improved.

The application discloses a vehicle positioning method and device, electronic equipment and computer storage medium. The vehicle positioning method comprises the following steps: performing road perception processing on a collected target image to obtain a road center point in a vehicle coordinate system; performing projection processing on the road center point according to a vehicle trajectory to obtain current local road information of the vehicle; performing matching processing on the current local road information and each local road information in a positioning area of the vehicle; and determining a road center point in target local road information matched with the current local road information in the positioning area as a target position of the vehicle. In this way, the positioning accuracy of vehicle position information can be improved.

The utility model provides a weep hole cleaning device, which relates to the technical field of bridge maintenance equipment and specifically comprises a suction assembly, a collection assembly and a negative pressure mechanism, the negative pressure generating piece generates high-speed airflow and is connected to the suction pipe through the negative pressure amplifying piece, so that the high-speed airflow facing the containing space is generated in the suction pipe, negative pressure is formed at the end, away from the collecting assembly, of the suction pipe, the suction pipe is inserted into the water drainage hole, and the suction pipe can suck deposits in the water drainage hole into the suction pipe; the negative pressure amplification piece can increase the flow speed of the air flow so as to further improve the strength of the generated negative pressure and ensure that the suction pipe can smoothly suck the deposits into the containing space, so that the weep hole is cleaned, the cleaning efficiency is high, the cleaning effect is good, the pipeline is not prone to being damaged, the deposits can be collected, and the practicability is high. And environmental pollution caused by the fact that deposits easily fall into local roads or water source protection areas again is avoided.

The invention discloses a pavement disease data generation method and device and electronic equipment. The method comprises the following steps: acquiring an aerial expressway pavement image and a local pavement disease area image; performing lane line identification on the aerial expressway surface image to obtain a point set of lane lines; according to the point set of the lane line, selecting a to-be-mapped area, and determining a perspective transformation matrix between the to-be-mapped area and the local pavement disease area image; determining a segmentation mask image corresponding to the local pavement disease area image; performing perspective transformation on the local pavement disease area image according to the perspective transformation matrix to obtain a target disease area image, and performing perspective transformation on the segmentation mask image according to the perspective transformation matrix to obtain a target segmentation mask image; and determining a target image according to the target disease area image, the target segmentation mask image and the aerial highway surface image. The technical problem that it is difficult to use an unmanned aerial vehicle to shoot pavement disease data during pavement disease recognition in the prior art is solved.

This invention provides an intelligent operation and maintenance scheduling method for charging stations based on a large language model and multi-starting-point policy reinforcement learning. The main steps include: collecting alarm and latitude / longitude data of target charging stations, and constructing a symmetrical global time-distance matrix by combining macro and local road networks; initializing the large language model decision center to generate macro control parameters (including weight parameters and penalty coefficients for each region, and the number of clusters) containing the number of operation and maintenance personnel; using a clustering algorithm based on these parameters to divide all stations into several controlled operation and maintenance areas; extracting real two-dimensional coordinates, using a multi-starting-point policy reinforcement learning network to perform path planning for stations within the operation and maintenance areas, and implementing real-time posterior arbitration; calculating the total maintenance time and workload variance of the planned routes; if the target is not met, generating a natural language diagnostic report and feeding it back to the large language model to readjust the weight coefficients, penalty coefficients, and number of clusters, and iterating until the solution meets the workload balance satisfaction threshold; and outputting the final optimal path planning routes and total time consumption for all target operation and maintenance charging stations.

The invention relates to the technical field of hazardous waste transportation management, in particular to a hazardous waste transfer abnormal behavior self-sensing early warning method based on a transportation track, which comprises the following steps: acquiring transportation information, acquiring a regional local map according to a transportation site, identifying a path in combination with a transportation direction, marking an intersection point, and setting a local road section; determining a local transfer time period and calculating the steady-state flow of the target road section by taking each local road section as a target and combining the predicted transfer time and historical data; according to the number of local road sections extending from the intersection, passing road sections are screened out and connected to generate a transfer transportation track, finally, the track serves as an actual transfer path to be used for dangerous waste transfer, and a precise path basis is provided for abnormal behavior sensing and early warning; through automatic road section screening and track generation, the manual intervention cost is reduced, meanwhile, the clear track generation logic is convenient for supervisors to quickly trace the transportation process, and the convenience and efficiency of hazardous waste transfer supervision are improved.

The application discloses a highway automatic driving commercial vehicle trajectory prediction and decision method and system based on complex network theory, which utilizes a complex network encoder in a prediction model to encode a high-definition map and an agent context into node coding of a directed weighted complex network to obtain the accessibility of each node.A target point predictor predicts target points of each agent according to historical trajectory information and local road features of the agent, and obtains a joint distribution of reference trajectories in combination with lane node accessibility and agent node criticality.A trajectory decoder inputs historical trajectories of the agent and kinematics or dynamics constraints to decode and generate a predicted trajectory and optimize the predicted trajectory under the condition of the reference trajectory.A decision model inputs a navigation route provided by an upstream global path planning layer, judges whether there is a lane changing requirement, and determines a priority of a lane.Combining a joint distribution probability of the reference trajectory, a complex network risk value and the lane priority, a cost function is designed to select an optimal trajectory as a decision result.

The invention discloses an automatic driving control method based on vehicle-road cloud cooperation, and the method comprises the steps: transmitting obstacle, weather and local road condition information to a vehicle end and a cloud end through a road end, and giving a local speed curve of the road end; through vehicle-road cloud global information sharing, the vehicle end marks timestamps on data sent by the cloud end and the road end so as to match corresponding sensing results, and a vehicle end speed curve is generated according to the vehicle end sensing results and road end road condition information; the cloud end gives a cloud end global speed curve according to a navigation planning route of the vehicle and a sensing result of a corresponding road end on the planning route in combination with global traffic flow, historical data and high-precision map information; and adjusting weights corresponding to the cloud end, the road end and the vehicle end, and planning a final speed curve, so that the vehicle performs self-vehicle tracking execution according to the final speed curve. The intelligent level of automatic driving of the vehicle can be improved, and the use comfort of a user is improved.

The invention discloses a trajectory prediction method based on dual-view fusion, which belongs to the technical field of trajectory prediction, and comprises the following steps: respectively acquiring road data and proxy data under a global view and a local view; obtaining global road node features and global agent node features according to the road data and the agent data under the global perspective, and obtaining local road node features and local agent node features according to the road data and the agent data under the local perspective; based on the sparse graph structure, splicing local road node features and local agent node features to obtain time sensitive features of agent nodes; screening the global road node features and the global agent node features to obtain target road node features and target agent node features; fusing the time sensitive feature, the target road node feature and the target agent node feature to obtain an agent node depth feature; and predicting a plurality of candidate end points according to the depth features of the proxy node, and splicing the depth features of the proxy node and the plurality of candidate end points to obtain a prediction trajectory.

The present disclosure provides a determination method and device for gate passing state, and relates to the technical field of artificial intelligence such as large model, deep learning, natural language processing, automatic driving and map navigation. The determination method for gate passing state comprises: determining a target local road network according to a target gate, and obtaining a gate entry track and a gate exit track corresponding to the target gate according to the target local road network; generating a gate entry track graph according to the target local road network and the gate entry track, and generating a gate exit track graph according to the target local road network and the gate exit track; using a multi-modal large model, generating a first answer according to the gate entry track graph, and generating a second answer according to the gate exit track graph; and obtaining the passing state of the target gate according to the first answer and the second answer. The present disclosure can enhance the robustness when judging the gate passing state, and improve the efficiency and accuracy of obtaining the gate passing state.

1. Name of the product in this design: Integrated entrance overload control equipment. 2. Purpose of this design: For the management of overloaded vehicles and toll collection on highways and local roads. 3. The key design feature of this product is its shape. 4. The image or photograph that best illustrates the design's key points: a 3D model.

The invention provides a navigation information generation method, and relates to the technical field of artificial intelligence, in particular to the technical fields of map navigation, automatic driving, intelligent transportation and the like. The method comprises the steps of determining whether an illumination intensity value in front of a target vehicle is within a preset light intensity range or not; in response to determining that the illumination intensity value is within the preset light intensity range, acquiring point cloud data of a road in front of the target vehicle by using a radar sensor on the target vehicle; and generating local road condition information according to the road point cloud data, and overlapping and displaying the local road condition information on global navigation information displayed by the target vehicle. According to the method, the road condition in front of the vehicle at night is reconstructed by using the point cloud data collected by the radar sensor, and driving safety is guaranteed.

The present invention discloses a local road repair device for roads and bridges, which relates to the technical field of road and bridge repair, including a device body, wherein the device body includes a machine base, the top of the machine base is fixedly connected to the machine body, and the top of the machine base is provided with a uniform guiding mechanism, wherein the uniform guiding mechanism includes an internal guiding unit, and the internal guiding unit is provided on the top of the machine base. The present invention provides an internal guiding unit and a surface guiding unit, inserts an insert into the interior of the asphalt while a roller presses the asphalt on the surface, and the two cooperate with each other to guide out the heat in the asphalt and take it away through the heat exchange technology. This guiding treatment can effectively guide away most of the heat in the asphalt, accelerate the solidification speed of the asphalt, thereby shortening the construction period and facilitating the development of the construction progress, and at the same time can reduce the time of road closure, alleviate traffic congestion in advance and reduce the possibility of causing accidents.

The invention provides an intelligent traffic management method and system, and relates to the technical field of intelligent traffic. The method comprises the following steps: a vehicle terminal determines whether a traffic event exists at present according to collected current road environment data; if yes, responding to the traffic event, and controlling the vehicle to execute vehicle emergency operation; the vehicle terminal sends the execution result of the vehicle emergency operation and the type of the traffic event to the corresponding roadside equipment; the roadside equipment performs local road traffic coordination control according to the execution result of the vehicle emergency operation, the type of the traffic event and the vehicle driving data of each driving vehicle in the control range of the roadside equipment; and the cloud device performs global road traffic coordination control according to the reported data of each vehicle-mounted terminal and the reported data of each road side device. According to the method, through cooperative processing of the vehicle terminal, the roadside equipment and the cloud equipment, an efficient traffic decision-making mechanism considering real-time performance, safety and globality is realized.

This invention discloses a robot scheduling method and system for sand and gravel stacking sites based on inventory heat, belonging to the field of intelligent control technology. By establishing a road network topology including inventory areas and road units, the time axis is discretized into scheduling time slices. Inventory heat is calculated and graded based on inventory quantity, inbound / outbound frequency, and task time limits, forming an inventory heat distribution. Based on inventory heat and a preset maximum travel distance threshold, a subset of candidate tasks is generated for the robot. Under the constraints of empty travel distance threshold and road unit throughput parameters, the robot collaboratively completes target task selection and travel path planning. Operating status, task results, and inventory inbound / outbound information are collected to calculate empty travel, congestion, and inventory response indicators. The inventory heat calculation rules and task priority rules are adjusted to adaptively update the inventory heat distribution. This invention can reduce empty travel distance, alleviate local road congestion, and improve the operational efficiency of stacking sites.

The utility model relates to the technical field of road safety facilities, and discloses a wave-shaped guardrail installation device which comprises a shell, a base is arranged above the shell, and a stand column is detachably arranged on the base. The supporting assembly is arranged at the bottom of the base and used for supporting the base; the supporting assembly comprises a mounting seat plate and a bottom plate, the mounting seat plate is fixedly connected with the base, the bottom plate is fixedly connected with the stand column, and the bottom plate is in threaded connection with the base and is locked with the mounting seat plate through bolts. Therefore, the feasibility of arranging the roadside wave-shaped guardrail under the problems of land acquisition, agricultural protection land and the like of local road sections is realized, the safety of vehicle driving is realized, accident consequences are reduced, the technology can be widely applied to road sections with limited roads, the design is simple, the construction is convenient and rapid, and the requirements of road grades of three levels and below are met.

Method for determining local weather conditions (41), local precipitation (42) and / or local road surface conditions (43) in the immediate vicinity of a vehicle (1) by evaluating signals (23-30) from at least one vehicle environment sensor (2; 3), wherein at least one interference signal (24; 26; 28; 30) of the at least one vehicle environment sensor is taken into account for determining the local weather conditions (41), local precipitation (42) and / or the local road surface conditions (43), wherein the at least one vehicle environment sensor (2; 3) comprises a radar, lidar sensor and / or a laser scanner.

The invention belongs to the technical field of logistics transportation evaluation, and provides a logistics transportation path evaluation method and system based on atlas analysis, electronic equipment and a storage medium. The method comprises the steps of fusion data set construction, map construction, effective road network map construction, effective path information extraction, local prediction map sequence construction, multi-dimensional evaluation and grade matching. According to the invention, through construction of the dynamic road network map and the historical road network map database and multi-time-step prediction, the perspectiveness of an evaluation result is improved; by constructing a local road network set, comprehensive assessment of path passing efficiency and risk is realized, and the integrity of an assessment result is improved; and by integrating dynamic influence data and utilizing a multi-rule and multi-dimensional processing mechanism for correction, the reliability and robustness of an evaluation result are enhanced.

The invention discloses a navigation method and device for a lane-level local map and an electronic device. The method comprises the following steps: in response to a received local map navigation starting signal, starting a local map navigation mode; acquiring standard precise map data and perception data; constructing a lane-level local road map based on the standard precise map data and the perception data; rendering a lane-level navigation guide line segment in the lane-level local road map based on the standard precise map data, the navigation route information and the current lane information of the vehicle; and navigating the vehicle based on the lane-level local road map and the lane-level navigation guiding line segment. According to the invention, the technical problems that the high-precision map acquisition and updating cost is high and the standard-precision map cannot complete lane-level guidance in the prior art are solved.

The invention discloses a vehicle driving assistance control method and device, electronic equipment and a readable storage medium. The method comprises the steps that a navigation path of a vehicle and road information of a road on which the vehicle is currently driven are obtained in response to the fact that a driving assistance function of the vehicle is in an open state, and the road information is used for representing local road characteristics of the road on which the vehicle is currently driven; based on the position information of the current position of the vehicle and the navigation path, navigation map information is obtained, and the navigation map information is used for representing global road features of the vehicle within a preset distance range; based on the navigation path, the navigation map information and the road information, a driving strategy of the vehicle is determined, and the driving strategy is used for representing a driving rule of the vehicle along the navigation path; and controlling the vehicle to run along the navigation path according to the driving strategy. According to the invention, the technical problem of low application efficiency of a driving assistance function in a complex or long-distance scene in the related art is solved.

The invention discloses an unmanned aerial vehicle road property inspection image recognition method and system based on semantic segmentation, and the method comprises the steps: carrying out the spatial partitioning of a single-source road property inspection image, which is aerially photographed by an unmanned aerial vehicle, according to a size label of a road property target, after obtaining a plurality of road property inspection sub-images containing complete local road property areas, inputting the road property inspection sub-images into a feature pyramid semantic segmentation model, adjusting the robustness loss weight of the model based on shooting angle deviation and illumination non-uniform features, and outputting abnormal shooting correction sub-images; adjusting the feature extraction weight of the small target attention mechanism module, extracting the features of the small target road property region, and splicing and integrating the features to obtain a semantic segmentation result; extracting a shape texture feature set according to a semantic segmentation result, matching the shape texture feature set with a preset road property target classification library to obtain classification information, extracting state features, and performing evolution analysis to determine an abnormal state; and performing spatial geographical coupling analysis by using the classification information, the abnormal state and the unmanned aerial vehicle flight log, and generating an inspection report carrying road property position marks.

The application relates to the technical field of artificial intelligence, and provides a vehicle violation identification method and system based on three-dimensional posture perception. The method comprises the following steps: obtaining a vehicle violation detection point through violation site positioning; pre-disposing equipment at the detection site to obtain a high-position image collection array; setting a trigger distance to determine a trigger range; when a target vehicle enters the range, activating the image collection array to obtain a real-time image set; realizing three-dimensional posture perception based on the image set to obtain real-time three-dimensional posture parameters; taking the trigger range as a constraint to obtain local road geometric parameters; and combining the posture parameters and the road geometric parameters to identify the violation to obtain a vehicle violation identification result. The application realizes three-dimensional posture perception on the real-time images collected by the high-position image collection array, solves the technical problem that a traditional monocular camera cannot construct a three-dimensional scene, and ensures high-precision identification of the vehicle position.

The application discloses a kind of electric heavy truck dynamics parameter estimation and driving power prediction method and system and readable storage medium, belong to new energy automobile energy management technical field.The method collects vehicle operation data and matches local road topology network;Utilize state and parameter joint estimation algorithm interactive transfer covariance, real-time decoupling output vehicle mass and road slope estimated value, automatically reset covariance matrix when loading and unloading;Based on topology network, construct topological graph, integrate space-time characteristics and use speed prediction neural network to output speed and acceleration sequence;The estimated value is substituted into longitudinal dynamics inverse equation to solve reference driving power, and the error caused by the fluctuation of resistance coefficient is compensated by using Gaussian process regression model, and the power demand with confidence interval is output.The system includes data acquisition, parameter estimation, working condition prediction and power joint prediction module, and the corresponding program is stored in the medium.The application realizes high-precision decoupling and fast convergence of parameters.