46 results about "Local road" patented technology

The invention relates to the technical field of unmanned vehicle control, and discloses a smart city environmental sanitation unmanned vehicle path planning and real-time monitoring method. The method comprises the following steps: firstly, acquiring a multi-source environment sensing data set such as laser radar point cloud data, a camera image sequence and real-time traffic flow information; local road network features are extracted based on laser radar point cloud data, a dynamic target motion prediction map is generated according to a camera image sequence, and real-time traffic flow information is processed to generate a regional traffic efficiency evolution curve. And inputting the data into a path optimization model to generate an initial path sequence, dividing cleaning task priorities, fusing related data and generating a final path planning scheme through a reinforcement learning algorithm. In addition, operation state data of the unmanned vehicle are collected in real time, and a path correction instruction set is generated through an anomaly detection model to update the strategy network. According to the method, the rationality and the operation efficiency of the path planning of the environmental sanitation unmanned vehicle can be improved, real-time monitoring is realized, and the operation safety and the management intelligence level are enhanced.

A method and system for managing roadside information and distributing navigation information is disclosed. The embodiments provide a roadside device network configured to collect sensor data and vehicle-originated data about the local roadway. The roadside devices are spaced along the road and are connected to a central server to collect information about the impaired road condition, determine an approximate location of an impaired road condition, calculate an alternate route, and transmit an alternate route to a roadside device to transfer the information to a passing vehicle.

The invention provides a highway pavement disease estimation method and device, and belongs to the technical field of highway disease detection. Comprising a modeling stage and an application stage, wherein the modeling stage specifically comprises the steps of collecting three-dimensional point cloud data of a standard road section asphalt pavement for a long period and preprocessing, identifying a subsidence area by using a deep learning network to obtain morphological characteristic data, collecting monitoring data of the subsidence area, establishing a training data set, and applying the training data set to the subsidence area; training a subsidence disease prediction equation set by using the training data set to obtain a subsidence disease trend analysis model; the application stage specifically comprises the steps of collecting and preprocessing three-dimensional point cloud data of a to-be-evaluated road asphalt pavement in a short period, identifying a subsidence area by using a deep learning network to obtain morphological characteristic data, collecting monitoring data of the subsidence area, obtaining a subsidence prediction result according to the data, and generating an evaluation report. The technical problems that in the prior art, only local road sections can be statically evaluated, the subsidence disease trend of kilometers cannot be comprehensively and dynamically mastered, and appropriate maintenance measures cannot be selected are solved.

The invention relates to the technical field of electric vehicle power control, and provides a real-time dynamic power distribution optimization method for an electric vehicle controller. The method comprises the steps that after an input destination is received, a navigation route is generated according to the real-time position of the electric tricycle and the destination; performing global road condition analysis on the navigation route to obtain M local road sections and road condition information; the interactive load sensor obtains a real-time load; analyzing a power demand according to the real-time load and the M pieces of local road condition information, and outputting M power distribution modes; and when the electric vehicle controller runs along the navigation route, power distribution dynamic optimization and mode smooth switching are executed according to a local road section connection sequence until the electric vehicle arrives at a destination. The technical problems of low power distribution efficiency and insufficient cruising ability of the electric vehicle under complex road conditions and dynamic loads are solved, and the effects of automatically adapting to different road sections and load conditions through global road condition analysis and load sensing and improving the power distribution efficiency and the cruising ability of the electric vehicle are achieved.

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 invention provides a local road topology on-line construction method, system and product, and the method comprises the steps: predicting a lane center line through a training qualified lane center line prediction network based on lane line data, and obtaining a target lane center line set, the lane center line being composed of a series of ordered data points; under the condition that a new lane center line is obtained through prediction, transverse truncation is carried out on all the lane center lines through the end points of the new lane center line; calculating the geometric distance between the terminal point of the lane center line before the transverse cut-off position and the starting point of the lane center line after the transverse cut-off position to obtain a corresponding calculation result; and determining the branch and confluence road topology between the lane center lines according to the calculation result. The method aims to predict road topology in an end-to-end mode, so that the calculation power is saved, and the real-time performance of track and intention prediction is improved at the same time.

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 invention is applicable to the field of civil engineering equipment, and provides civil engineering road arc cutting equipment which comprises a cart, a dust collector and a cutting assembly, and the cutting assembly is mounted on the cart; the dust collector is mounted on the cart; the cutting assembly is composed of a dust removal and sound insulation cylinder and an arc-shaped cutting knife assembly. The dust removal and sound insulation cylinder is mounted on the cart in a lifting manner through a cylinder telescopic rod arranged on the dust removal and sound insulation cylinder; the arc-shaped cutting knife assembly is installed on the cart in a lifting mode through a hydraulic cylinder arranged on the arc-shaped cutting knife assembly. The arc-shaped cutting knife assembly is composed of an arc-shaped cutting knife and a driving structure. The arc-shaped cutting knife assembly is lowered to the ground through the hydraulic cylinder, the driving structure is started, the arc-shaped cutting knife is driven to do reciprocating arc-shaped movement for cutting, precise local road surface cutting is achieved, and the cutting accuracy is high; and the dust removal and sound insulation cylinder is lowered to the cutting position of the arc-shaped cutting knife through the air cylinder telescopic rod, raised dust generated in the cutting process is removed, and noise generated in the cutting process is isolated.

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 local road repairing equipment for roads and bridges, and relates to the technical field of road and bridge repairing, the local road repairing equipment comprises a device body, the device body comprises a machine base, the top of the machine base is fixedly connected with a machine body, the top of the machine base is provided with a uniform guide mechanism, and the uniform guide mechanism comprises an internal guide unit; through the arrangement of the internal guide unit and the surface guide unit, the insertion piece is inserted into asphalt, meanwhile, the roller presses the asphalt on the surface, and the internal guide unit and the surface guide unit are matched with each other to guide heat in the asphalt and take away the heat through a heat exchange technology; the guiding treatment can effectively guide and take away most of heat in the asphalt, the asphalt solidification speed can be increased, the construction period is further shortened, the development of the construction progress is facilitated, meanwhile, the road closing time can be shortened, traffic jam can be relieved in advance, and the possibility of accidents is reduced.

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.

The present invention discloses a method for extracting road point clouds based on scan lines and sliding least squares fitting, comprising the following steps: scan line reconstruction; accurate estimation of the height of the POS system from the road surface; robust extraction of road surface seed points; complete and robust extraction of road surface points; an efficient method for extracting road surface points based on scan lines. The present invention extracts road surface point clouds scan line by scan line, improving the efficiency of road surface point extraction; making full use of prior knowledge, laying a foundation for the robust extraction of road surface points; the robust extraction of road surface seed points, clustering the seed point candidates of the critical rows of the alternative points in the scan lines, effectively improving the robustness of the road surface seed point extraction; using sliding weighted least squares to fit local road points, determining the fitting method and the weight determination method, which can effectively achieve the local optimal fitting of road surface points in the scan lines and achieve the robust and complete extraction of road surface points.

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 invention relates to the technical field of intelligent driving, in particular to an automatic driving control method and device, computer equipment and a storage medium. The method comprises the steps that in the process of automatic driving of a target vehicle based on global road planning, if it is detected that a first interference vehicle exists in a driving track of the target vehicle, local road planning for the first interference vehicle is established from a first planning point to a second planning point of the global road planning; and controlling the target vehicle to bypass the first interference vehicle through local road planning. The flexibility of the target vehicle in the automatic driving process is guaranteed, the target vehicle can cope with a more complex driving environment, the safety of the target vehicle in the driving process is improved, and collision and friction between the target vehicle and the first interference vehicle are prevented.

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.