294 results about "Road edge" patented technology

The invention relates to the technical field of image processing, in particular to a road area intelligent extraction system for unmanned aerial vehicle aerial images, and the system comprises an image noise filtering module which analyzes and inputs frequency domain information of the unmanned aerial vehicle aerial images, recognizes a target noise mode, and filters the unmanned aerial vehicle aerial images. According to the method, frequency domain noise analysis and notch filtering are combined, the sharpness of the road edge is reserved, meanwhile, high-frequency noise and periodic interference are restrained, and the visibility of a low-contrast road area is improved; local pixel spectrum dispersion calculation is introduced and is dynamically fused with morphological parameters, and direction consistency and length-width ratio constraints are combined, so that the connectivity perception capability of the fragmented non-paved road is enhanced, and the fracture misjudgment under the form-changeable terrain is reduced; a normalized vegetation index and multi-band reflectivity are combined for modeling, water reflection interference is eliminated through a blue light band standard deviation, and a space mask of a non-road surface feature interference area is constructed.

A unified boundary machine learning model is capable of processing perception data received from various types of perception sensors on an autonomous vehicle to generate perceived boundaries of various semantic boundary types. Such perceived boundaries may then be used, for example, to control the autonomous vehicle, e.g., by generating a trajectory therefor. In some instances, the various semantic boundary types detectable by a unified boundary machine learning model may include at least a virtual construction semantic boundary type associated with a virtual boundary formed by multiple spaced apart construction elements, as well as an additional semantic boundary type associated with one or more other types of boundaries such as boundaries defined by physical barriers, painted or taped lines, road edges, etc.

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.

Systems, methods, and other embodiments described herein relate to estimating lane boundaries using a slicing model with road data for generating maps. In one embodiment, a method includes computing three-dimensional (3D) representations that are discretized from acquired data about road edges associated with driving lanes. The method also includes deriving discrete and lateral slices of the road edges using a slicing model, the road edges are connected in a road graph that describes a mapped area. The method also includes extracting features from the lateral slices individually using a neural model for forming a histogram to estimate lane boundaries about the driving lanes. The method also includes generating a map by linking the lane boundaries individually along the road edges.

The invention relates to the technical field of road construction, in particular to a repairing and trimming device for road base paving construction, which comprises a trimming scraper, a first rotating shaft is rotatably arranged on the front side of the top of the trimming scraper, the bottom end of the first rotating shaft is connected with a second rotating shaft through a universal transmission assembly, and a material scattering assembly is arranged at one end, away from the universal transmission assembly, of the second rotating shaft. A shaking generation assembly is arranged on the trimming scraper and used for controlling the side, facing the road, of the material dredging assembly to incline and the front side of the material dredging assembly to tilt. The device has the beneficial effects that when the rotating disc rotates by a half circle, the multiple fixing columns not only knock lump materials in a shaking state, but also bring the materials located at the slope bottom to the position of the slope top, and when the rotating disc rotates by another half circle, the materials are uniformly spread downwards from the slope top, so that the materials are fully scattered and uniformly spread; and the problem that the thickness of the edge of a road is not uniform or the compactness is greatly different due to material caking or non-uniform thickness is avoided.

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.

Lidar produces three-dimensional point clouds. From the point clouds, objects need to be detected and tracked. For example, from the point clouds, embodiments detect what kind of objects is detected, what shape the object is, and the objects current location and trajectory. Each lidar sensor on a vehicle produces a point cloud periodically. The point cloud is input into a deep learning neural network that outputs road geometry, such as road edges and lane dividers. In some embodiments, the other network can also output information about other objects in the environment, such as other vehicles and pedestrians.

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.

The invention discloses a real-time road edge fusion method for driving of an automatic driving vehicle. The method comprises the following steps: step 1, initializing a data source; the data source comprises a visual perception data source S, a millimeter wave radar data source L and a vehicle driving information data source Z; 2, updating the road edge line based on the visual perception data source S to obtain a visual road edge line yS (x); updating the road edge line based on the millimeter wave radar data source L to obtain a radar road edge line yL (x); the driving state and the conversion matrix RT are updated based on the vehicle driving information data source Z, and a turning road edge line yZ (x) under the vehicle turning state is obtained; 3, performing fusion processing on the visual road edge line yS (x), the radar road edge line yL (x) and the turning road edge line yZ (x) to obtain a fused road edge line yR (x) after fusion; and 4, taking the fused road edge line yR (x) after fusion as one input value of the road edge line at the next moment, and updating the road edge line y (x) at the next moment. The parking lot autonomous valet parking system and the parking lot autonomous valet parking method have the advantages of obtaining a more robust road edge through fusion of multi-source data, improving safety and reliability of automatic driving and the like.

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 relates to a vehicle control apparatus and a vehicle control method. A vehicle control device includes a light detection and ranging device (LiDAR), a memory storing map information, and a processor. The processor may filter a data set including a road edge portion related to a position of the vehicle from the map information according to a first specified condition, obtain a plurality of first partial line segments, identify a plurality of second partial line segments from a plurality of line segments formed by contour points corresponding to the road edge portion through LiDAR, pairing of the plurality of second partial line segments with the plurality of first partial line segments is identified, a position of the vehicle in a second coordinate system different from the first coordinate system is output based on applying a specified algorithm to each pairing, and the vehicle is controlled based on the position of the vehicle in the second coordinate system.

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.