245 results about "Road edge" patented technology

The invention relates to the technical field of laser detection, in particular to laser detection equipment for pavement material thickness detection, which comprises a detection table, an arc-shaped hole is formed in one side of the top end of the detection table in a penetrating manner, a stop block is fixedly connected to the outer side, corresponding to the arc-shaped hole, of the top end of the detection table, and an arc-shaped road edge stone is arranged above the top end, corresponding to the arc-shaped hole, of the detection table. A positioning adjusting mechanism is arranged at the bottom end of the detection table, and a scraping cleaning mechanism and a detection mechanism are arranged at the top end of the detection table; the positioning adjusting mechanism comprises a first clamping plate and a second clamping plate which are arranged on the two sides of the inner wall of the arc-shaped hole, and the first clamping plate, the second clamping plate and the check block are matched to be used for positioning and adjusting the position of the arc-shaped road edge stone. Under the cooperation of the deflected first clamping plate, the deflected second clamping plate and the fixed stop block, the position of the arc-shaped road edge stone can be quickly adjusted, so that the adjustment time can be shortened, and the adjustment efficiency is improved.

The invention discloses a road traffic detection control method based on a vehicle-mounted camera module, and belongs to the technical field of traffic detection, and the method specifically comprises the steps: collecting road image data in real time through the vehicle-mounted camera module; preprocessing the acquired road image data, and extracting a road area; performing edge extraction on the preprocessed road image data by using an edge detection algorithm to obtain road edge information; according to the road edge information, detecting straight-line segments in the road through Hough transform, and calculating an included angle between adjacent straight-line segments to determine the line type of the road; calculating the distance between the vehicle and the road boundary and the offset of the vehicle relative to the road center line according to the line type of the road in combination with the speed and position information of the vehicle; according to the distance between the vehicle and the road boundary and the offset of the vehicle relative to the road center line, the driving direction and speed of the vehicle are adjusted through a preset control strategy; according to the invention, real-time safety detection of the vehicle in the driving state is realized.

The invention discloses a multi-target road transportation path planning method and system based on multiple parameters A * and NSGA-II, and belongs to the technical field of road transportation path optimization. The method comprises the following steps: constructing a large-scale road network directed graph structure; establishing a satellite orbit model and a risk distribution model, calculating a dynamic risk value of a roadside, and forming dynamic road network data; a three-stage initialization strategy is adopted to generate an initial population, transportation time and reconnaissance risks are taken as double targets, evolutionary optimization is carried out through an NSGA-II algorithm, and a Pareto optimal path set is output. The system comprises a corresponding road network module, a risk module and a planning module. According to the method, the technical problems of multi-target conflict, low initial population quality and satellite risk avoidance of a traditional method are solved, and the timeliness, the concealment and the algorithm efficiency of path planning are remarkably improved.

The invention belongs to the field of urban traffic management, and provides a hierarchical dynamic routing system and method for a computing power-energy fusion network, and the method comprises the steps: dividing a fusion network graph into a plurality of region sub-graphs, and dividing a path table and a connection point path table based on each region sub-graph; monitoring the running states of the road, the edge computing node and the charging station in real time, and predicting the road congestion intensity, the edge computing node queuing estimation information and the charging constraint; and determining a candidate path set in the partition path table and the connection point path table according to an online request of the vehicle, screening each candidate path in the candidate path set based on the road congestion intensity, the edge computing node queuing estimation information and the charging constraint, and determining an optimal path. According to the method, online retrieval is converted into rapid combination and evaluation of small candidate sets from full-graph traversal; traffic, energy and computing power are incorporated into the same decision framework, and systematicness deviation caused by evaluation of the future at present is avoided.

The invention belongs to the technical field of intelligent traffic, and provides a distributed optical fiber road sensor on-site manufacturing and road risk monitoring and early warning method. Comprising the steps of optical fiber unit manufacturing, concave shallow slot cutting, sensor construction, data acquisition, road surface state data conversion, scattered light time domain signal interval division, frequency domain conversion and filtering processing, multi-mode feature extraction and road surface condition recognition. According to the invention, the concave shallow slot is cut at the edge of the road, and the flat ribbon-shaped optical fiber unit is fixed in the shallow slot by using polyurea, so that the multifunctional distributed optical fiber road sensor which is integrated with the road surface, high in sensitivity, small in size, simple to install, convenient and rapid to maintain and repair and low in cost is constructed; through a zero crossing point and a noise threshold value, normal time domain signal interval judgment is optimized, and the effect of subsequent filtering processing is improved; a road surface condition identification data model is constructed through a multi-modal fusion technology, and the identification precision of multiple road surface abnormal conditions and the positioning precision of abnormal events are improved.

A method for creating a guidance trajectory for a first transportation vehicle which includes receiving swarm data for a route section that represent at least one motion trajectory of at least one second transportation vehicle traveling through this route section; determining a distance from the at least one motion trajectory represented by the swarm data to a determined roadway edge for the route section; and using an electronic computing device to apply the at least one motion trajectory to ascertain the guidance trajectory for the first transportation vehicle in response to the distance associated with the respective motion trajectory being greater than or equal to a predefined limit value for the route section, and to provide the guidance trajectory for the first transportation vehicle, as a result of which the first transportation vehicle is transversely controlled using the guidance trajectory.

The invention relates to a road information automatic extraction method based on a multi-modal remote sensing image, and the method comprises the steps: obtaining a multi-modal remote sensing image training sample set which comprises a plurality of groups of multi-modal remote sensing image training samples, and each group of multi-modal remote sensing image training samples comprises a multi-modal remote sensing image and a corresponding road label image; the multi-mode remote sensing image comprises an optical remote sensing image and an SAR image; the road label image comprises a road surface segmentation label and a road edge label; constructing an initial road extraction model, and training by using the multi-modal remote sensing image training sample set until a loss function converges to obtain a training road extraction model; and performing road extraction on the input multi-modal remote sensing image data based on the training road extraction model, and performing vectorization to obtain road data. According to the method and the device, the problem of insufficient road extraction precision in a road extraction task based on a remote sensing image in the prior art is solved, and the accuracy and connectivity of a road extraction result based on the remote sensing image are improved.

The utility model relates to the technical field of expressway guardrails, in particular to an expressway anti-collision guardrail which comprises a fixed stand column, a movable stand column and an anti-collision device. The wave-shaped beam plates are continuously arranged along the edge of the road and are parallel to the fixed stand columns; and the horizontal connecting beam stretches across the corrugated beam plate and the fixed stand column, one end of the horizontal connecting beam is rotationally connected with the corrugated beam plate through a rotary connecting sleeve, and the other end of the horizontal connecting beam is rotationally connected with the fixed stand column through a buffer connecting sleeve. Through the arrangement of the rotary connecting sleeve and the buffer connecting sleeve, the wave-shaped beam plate can generate plastic deformation to absorb energy during collision and can also translate towards one side of the fixed stand column, and the translating motion drives the buffer connecting sleeve to rotate around the fixed stand column, so that the fixed limiting block sequentially collides with the limiting buffer plates; therefore, the energy absorption capability of the whole system is greatly enhanced.

The invention discloses a road surface point cloud extraction method and system based on facade point detection, and belongs to the technical field of point cloud processing, and the method comprises the steps: obtaining a road point cloud, and carrying out the preprocessing of the road point cloud based on a driving track; facade points in the road point cloud are detected and removed; screening the road point clouds with the facade points removed based on normal vector features to form preliminary road surface point clouds; and performing pavement point cloud and interference point separation on the preliminary pavement point cloud through density clustering, further optimizing the clustered pavement point cloud by applying a cloth simulation filtering algorithm, and outputting a clean pavement point cloud. According to the method, the road edge point cloud can be effectively reserved, the integrity and precision of the road point cloud are improved, and the requirements of applications such as road high-precision modeling and automatic driving are met.

The display system includes a lane line recognition unit configured to recognize a lane line of a traveling lane of the host vehicle based on a detection result of a camera of the host vehicle, a road edge structure recognition unit configured to recognize a road edge structure positioned outside the lane line based on a detection result of a sensor of the host vehicle, and a display control unit configured to control a display of the in-vehicle display based on recognition results of the lane line recognition unit and the road edge structure recognition unit. The display control unit is configured to display a road edge structure icon corresponding to the road edge structure on the in-vehicle display when the lane line is not recognized and the road edge structure is recognized.

The invention discloses a municipal road monitoring method and system based on an edge computing technology, and relates to the technical field of signal processing. The method comprises the steps that road edge nodes collect data through optical, vibration and infrared sensors, and the pavement crack index and the real-time traffic flow are calculated in parallel; fusing environment temperature and humidity data, weighting to generate a road health initial judgment value, and triggering a dynamic threshold alarm; exchanging initial judgment values at adjacent nodes through a low-power-consumption Mesh network, jointly calculating the regional-level traffic flow density and road surface damage association degree, and generating an encrypted regional road state feature vector; the central management platform aggregates the regional feature vectors, calculates a road comprehensive health score and an early warning weight coefficient in combination with a historical baseline, generates a self-adaptive dredging strategy and issues the self-adaptive dredging strategy to an edge node; according to node load and energy consumption data, the sampling frequency of the sensor is dynamically adjusted, and the crack recognition model is updated. According to the invention, the road state monitoring real-time performance and the early warning accuracy are obviously improved, and the data transmission energy consumption is reduced.

The invention relates to a method for constructing a lane model, and the method comprises the steps: carrying out the processing of a detected lane line, and carrying out the parallelization processing of the lane line; adaptively determining a lane center line coefficient by using the processed lane line according to the identified lane scene; and sampling points on the lane center line according to the determined lane center line coefficient under the condition of keeping a safe distance from the road edge so as to construct the lane model. The application also relates to a computer program product and an electronic device.

The invention provides an expressway stake mark positioning method and equipment based on point cloud data element extraction, and the method comprises the steps: carrying out the preprocessing of original point cloud data through employing a slope method ground filtering and statistical outlier elimination filtering mode, and extracting a complete road surface; on the basis of the extracted complete road surface data, performing point cloud alignment, slicing and extracting road edges, and preliminarily solving a road center point by utilizing the geographic coordinate center of each point of the extracted road edges; skeleton points are randomly initialized, the positions of the skeleton points are iteratively optimized, then the skeleton points are subjected to sparse processing, and fitting is achieved through minimum spanning tree connection to generate a continuous center line of a road; and matching and positioning kilometer stake points to realize accurate assignment of stake numbers. According to the method, the expressway elements can be efficiently and accurately extracted from massive point cloud data and converted into stake number positioning information, the defects of a traditional method in efficiency and positioning precision are overcome, and the accuracy and working efficiency of expressway positioning are remarkably improved.

The invention discloses a vehicle meeting control method, electronic equipment, a storage medium, a vehicle and a vehicle networking system, and the vehicle meeting control method comprises the steps: receiving a vehicle meeting control instruction which is generated by a cloud platform according to road side sensing information and vehicle sensing information; according to the meeting control instruction, at least one of the following items is executed: the position of an avoiding wheel of the vehicle in the vehicle width direction is adjusted, the relative position of a vehicle body of the vehicle and the road edge is controlled, the avoiding wheel is the wheel, close to the road edge, of the vehicle, and the avoiding wheel can move in the width direction of the vehicle; adjusting the height of the vehicle body; and alarming to prompt that vehicles cannot pass. According to the method, on a narrow road which does not meet normal meeting conditions, the meeting control instruction is generated through the cloud platform, the vehicle is controlled to realize automatic meeting, or an alarm is given to prompt that the vehicle cannot pass, the meeting intelligence of the automatic driving vehicle is ensured, the vehicle travel efficiency and safety are improved, and the traffic jam problem caused by meeting is avoided.

The invention discloses a ground pit detection method, device and equipment and a storage medium, and the method comprises the following steps: collecting ground 3D point cloud data through a laser radar, and obtaining the size and position information of an obstacle; road surface image information is collected through a binocular camera, the width and the position of a road edge are obtained, and current pit negative obstacle information is judged and output in combination with a point cloud negative obstacle recognition type; and ground pits are recognized through a YOLO target detection algorithm, when the pits are recognized, position and size information of the pits is calculated through a binocular positioning algorithm, and the position and size information and a negative obstacle judgment result are subjected to multi-modal fusion to obtain final pit information. Different pit sizes can be detected and distinguished, the vehicle body posture and the running speed of the aerial work platform vehicle can be adjusted in a diversified mode according to the different pit sizes, and the flexibility and safety of the aerial work platform for dealing with ground pit conditions are remarkably improved.

Embodiments of the present disclosure relate to ground surface estimation using localized surface fitting. A three-dimensional (3D) surface structure (e.g., a road surface profile) may be estimated using a nonlinear optimization to fit height values to (e.g., accumulated, bias-corrected) LiDAR detections (e.g., sampled in localized regions along one or more predicted trajectories). For example, LiDAR data (e.g., detected 3D point clouds) may be ego-motion compensated, corrected for measurement bias, accumulated, and sampled along one or more predicted trajectories, and the height of each trajectory point may be fitted to the heights of the corresponding sampled points using a nonlinear optimization. As such, the resulting road surface profile (e.g., modeled along the wheel track(s)) may be provided to an adaptive suspension control system to modulate the damping characteristic of the suspension system to counteract indentations (e.g., potholes) or protrusions (e.g., speed bumps) represented in the road surface profile.

A double-cavity electronic device comprises a base unit and a mounting seat unit. The base unit is used for being arranged in a mounting groove of a road edge stone and comprises a lower cavity piece which defines a lower cavity and extends in the vertical direction and two connecting parts fixedly connected with the lower cavity piece. The mounting base unit is arranged on the upper side of the base unit and comprises an upper cavity piece which defines an upper cavity and extends in the vertical direction, an upper sealing cover arranged on the upper side of the upper cavity piece and two joined parts fixedly connected with the upper cavity piece. The lower chamber and the upper chamber are respectively defined by the base unit and the mounting seat unit, and the joined part can be movably arranged on the joining part to enable the upper bottom wall to close or be far away from the lower chamber, so that components in different chambers are easy to check or replace.

The utility model relates to the technical field of road line drawing, in particular to an automatic cruise pre-marking line calibration device and a pay-off robot system, which comprise a driving calibration mechanism and an automatic cruise mechanism, and the driving calibration mechanism comprises a driving robot, a positioning assembly and a positioning display assembly. The positioning assembly and the positioning display assembly are both arranged on the driving robot, and the driving robot drives on an area where a pre-line needs to be calibrated; the automatic cruise mechanism comprises a road condition acquisition element, a driving control module and a mark control module; the road condition acquisition element is used for acquiring the road edge condition of a specified road area, and the driving control module is used for controlling the driving robot to drive on one side of the road edge and controlling the positioning assembly to make a pre-line marking point on one side of the road edge through the marking control module in the driving process. According to the utility model, the problems of large workload and precision deviation in the existing manual pre-line calibration process are solved.

The application relates to a kind of road information automatic extraction methods based on multi-modal remote sensing image, comprising: obtaining multi-modal remote sensing image training sample set, including multiple groups of multi-modal remote sensing image training sample, each group of multi-modal remote sensing image training sample contains multi-modal remote sensing image and corresponding road label image;Multi-modal remote sensing image includes optical remote sensing image and SAR image;Road label image includes road surface segmentation label and road edge label;Initial road extraction model is constructed, and is trained using multi-modal remote sensing image training sample set until loss function converges, obtains training road extraction model;Based on training road extraction model, the input multi-modal remote sensing image data is extracted on road, and vectorization is carried out, and road data is obtained.The application solves the problem of insufficient road extraction precision in the prior art for remote sensing image road extraction task, improves the accuracy and connectivity of remote sensing image road extraction result.

The invention discloses an unstructured road lane division method based on triangulation, a medium and equipment, and the method mainly depends on prior high-precision map road boundary information, builds a road boundary discrete point set, and generates a triangulation network based on the road boundary discrete point set; and performing optimization and deletion processing on the triangulation network through the boundary constraint matrix to generate key nodes of a lane center line, taking the key nodes of the lane center line as end points of segments, generating a smooth lane center line in combination with a cubic spline curve, obtaining a cubic spline equation, and determining a lane center line of a target road. According to the method, only the road boundary information is input, the lane-level center line capable of being directly used for automatic driving path planning is output, complex post-processing is not needed, integration to a high-precision map production pipeline is facilitated, and the method has high engineering usability. Compared with a traditional method which depends on regular road side lines and is only suitable for a structured road environment, the method can be compatible with an unstructured road environment.

A system for navigating a host vehicle relative to a road segment may comprise at least one processor comprising circuitry and a memory. the memory may include instructions that when executed by the circuitry cause the at least one processor to: receive at least one image captured by at least one camera from an environment of the host vehicle, wherein the at least one image includes representations of at least two objects with offset edges in the environment of the host vehicle and located on one side of the road segment; analyze the at least one image to determine a free space boundary relative to the at least two objects, wherein the free space boundary represents an edge of at least one region of free space and follows a path different from a path represented by the offset edges of the at least two objects; determine at least one navigational action based on the free space boundary; and cause the host vehicle to implement the at least one navigational action.

The invention provides a municipal road BIM edge computing real-time control method and system based on an OPC UA protocol, and the method comprises the steps: monitoring the communication state of each node in an edge computing network and the continuity of sensor data, recognizing a data missing region, and determining the specific position of the data missing region; according to the position of the data missing area, a depth-first search algorithm is adopted to traverse the connection relation of adjacent nodes, and a fault boundary corresponding to the data missing area is constructed; and extracting redundant storage data from the fault boundary, analyzing whether the coverage capability of the redundant storage data meets the requirement or not, and if so, filling the data missing region to generate a preliminary repair result.

This external environment recognition device is provided with: a travel road surface estimation unit that estimates the travel road surface on which a host vehicle is traveling from sensing results output from in-vehicle sensing equipment; a surrounding environment height estimation unit that estimates height information of the surrounding environment, including travel roads and road edges, from the sensing results and the travel road surface estimation results; a road edge boundary position estimation unit that estimates a road edge boundary position on the basis of the sensing results, the travel road surface estimation results, and the height information of the surrounding environment; a road edge boundary position left / right side height extraction unit that extracts height information of the travel road side and the road edge side to the left and right of the estimated road edge boundary position from the height information of the surrounding environment; and a puddle boundary position estimation unit that searches travel roads that are closer to the host vehicle than to the estimated road edge boundary position, and estimates puddle boundary positions of puddles on the travel roads on the basis of the height information.

The invention discloses a system and method for adaptively controlling the hedging function of a sanitation cleaning vehicle, and the method comprises the steps: a vehicle-mounted intelligent terminal senses whether there is a pedestrian or a target distance of the type of a pedestrian at two sides of the vehicle through a visual sensor disposed on the vehicle; calculating the transverse distance and the longitudinal distance between the vehicle and the traveler, the rider, the road edge and the guardrail according to a visual algorithm; according to the transverse distance of the pedestrian or the riding pedestrian and the opposite-punching distance table corresponding to the rotating speed, a control message is sent to the uploading control screen, the uploading control screen issues a control instruction to the uploading controller, the uploading controller executes the control instruction, and the opposite-punching operation coverage distance and the opposite-punching function are automatically adjusted in real time. The hedging operation process of the cleaning vehicle is optimized, the intelligent level is improved, the loading controller receives a hedging self-adaptive control command of the vehicle-mounted intelligent terminal through the loading control screen, the left and right hedging electric control ball valves are controlled to be opened or closed in real time, and finally real-time dynamic control over the hedging distance is achieved.

The utility model relates to the technical field of road construction, in particular to a spraying mechanism of a line drawing robot, which comprises a frame, a running device, a main spraying device and a side spraying device, the running device comprises running wheel sets arranged on two sides of the frame, the main spraying device is arranged on the frame, and the side spraying device is positioned on the outer side of the running wheel sets. In practical application, spraying operation of road edges, narrow sidewalk boundaries or parking space lines in parking lots is often difficult to efficiently complete in a traditional mode due to space limitation. The side edge spraying device can be tightly attached to the road edge, so that the spraying range is flexible, and the spraying precision is precise. The side edge spraying device improves the operation efficiency and the spraying quality. And the time and cost consumption caused by manual adjustment or repeated spraying for covering the narrow edge area is reduced. According to the utility model, the limitation of the traditional spraying mode in narrow space operation is solved, and the spraying device has efficient and accurate spraying capability.

The tunnel pavement foreign matter intelligent detection early warning system comprises a processing terminal, the processing terminal is electrically connected with a clock circuit, the input end of the processing terminal is connected with a plurality of distance measuring terminals, and the output end of the processing terminal is connected with a plurality of alarm terminals; the distance measuring terminal comprises a distance measuring instrument and a reflecting plate which are oppositely arranged on the road edge in the tunnel, and a plurality of distance measuring terminals are arranged on the road edge at intervals in the length direction; the alarm terminal comprises an alarm lamp arranged at the top of the tunnel, and the alarm lamp is located on the side, close to the tunnel entrance, of the adjacent distance measuring terminal. According to the device, the distance measuring terminal is used for detection, when the reading of the distance measuring terminal is separated from a threshold value and the timing of the clock circuit exceeds a certain time period, the existence of a foreign matter can be judged, a driver can be reminded in an alarm mode, and the situation that the foreign matter does not exist in time and an alarm is caused by vehicle parking is avoided; and positive feedback can be generated, namely, a subsequent driver is reminded to slow down, and a negative effect cannot be caused.

The embodiment of the invention provides a road edge detection method and device, electronic equipment and a computer readable storage medium, and the method comprises the steps: segmenting original point cloud data collected by a laser radar according to a preset segmentation threshold value, and obtaining local point cloud data; performing sampling processing on the local point cloud data according to a preset three-dimensional voxel grid size to obtain sampling point cloud data; classifying three-dimensional points in the sampling point cloud data through a point cloud convolutional neural network model, and determining to obtain curb three-dimensional points; according to the curb three-dimensional points, curb marker facades are obtained; and determining curb position information according to the facade of the curb marker. In this way, the original point cloud data is segmented and sampled to obtain the sampling point cloud data, the point cloud convolutional neural network model is used to determine the facade of the road edge marker for the sampling point cloud data, and the position information of the road edge is determined according to the position information of the road edge marker, so that the stability and precision of the road edge detection result can be improved.

The application provides a remote sensing image road edge detection method and system, and the method comprises the following steps: extracting a predicted candidate vertex set Q from a road image; selecting a candidate vertex from the predicted candidate vertex set Q as a predicted starting vertex v i *, generating a local feature input agent model thereof, outputting a next predicted vertex v i+1 *, and updating a generated road edge graph g i+1 * based on the next predicted vertex v t *, continuing prediction until the road edge graph g t stops growing; and traversing each candidate vertex in the predicted candidate vertex set Q, performing prediction based on the agent model to obtain a road edge detection result. The method mainly adopts an end-to-end iterative graph generation method, can directly obtain linear object information, is less affected by segmentation performance, can solve the influence caused by partial occlusion, and can obtain vector data of the road edge without complicated post-processing operations.

The utility model belongs to the technical field of road traffic stone transportation, and particularly relates to road traffic stone transportation equipment which comprises a frame. The two ends of the vehicle frame are rotationally connected with walking wheels. The middle part of the frame is fixedly connected with a bearing rod; a pair of hanging cables is mounted at one end, close to the frame, of the bearing rod; a handrail is fixedly connected to one end, far away from the frame, of the bearing rod; the bottom ends of the pair of lifting cables are fixedly connected with a bearing plate; the middle part of the bearing plate is in sliding connection with a connecting seat; the two sides of the connecting base are rotationally connected with clamping plates through torsional springs. The bottoms of the opposite sides of the pair of clamping plates are rotationally connected with extrusion plates. Pulleys are rotationally connected to the two sides of the bearing plate; by means of the structure, the road edge stone can be lifted and transported and transferred in a lever mode, physical output of operators is reduced, and transportation convenience during construction of the road edge stone is improved.