Non-motor vehicle road network construction method based on streetscape images

Abstract

The invention discloses a non-motor vehicle road network construction method based on streetscape images. The non-motor vehicle road network construction method comprises the following steps of S1, obtaining a plurality of streetscape images and metadata and track sequence data of the streetscape images; s2, extracting a motor vehicle lane area and a non-motor vehicle lane area from each streetscape image, identifying road infrastructures and states related to non-motor vehicle lanes, and classifying tracks; s3, fusing the obtained road area, road infrastructure and state and track sequence data to obtain non-motor vehicle geometric road section information, and constructing a topological relation between non-motor vehicle road sections; s4, determining the actual width of the non-motor vehicle lane according to the vanishing points in the directions of the motor vehicle lane and the non-motor vehicle lane and the vanishing points in the horizontal direction; and S5, attaching the attributes of the non-motor vehicle lanes to the road network geometric data to form a non-motor vehicle road network. According to the method, the non-motor vehicle road network containing geometric road section information, a road section topological relation and lane attribute information is constructed.

Description

Technical Field

[0001] This invention relates to the fields of computer vision and geographic information technology, specifically to a method for constructing a non-motorized vehicle road network based on street view images. Background Technology

[0002] Against the backdrop of the transformation of urban transportation systems towards sustainability and equity, the construction of non-motorized vehicle lanes is not only an important component of the low-carbon travel system, but also a practical manifestation of the restructuring of urban road rights and the concept of spatial justice. Building a safe, continuous, and accessible non-motorized vehicle road network is not only a key measure to promote the "people-oriented" traffic governance concept, but also an important foundation for alleviating urban carbon emissions and ensuring daily commuting safety.

[0003] Currently, mainstream map platforms and open-source maps have gradually supported non-motorized vehicle navigation functions and introduced auxiliary indicators such as "green coverage" to optimize route recommendations. However, there is currently no complete non-motorized vehicle road network data available. On the one hand, this makes it difficult to meet the construction assessment and spatial analysis needs related to the completion of non-motorized vehicle road networks, thus affecting the formulation of subsequent road network optimization and improvement policies; on the other hand, it makes it difficult to meet the refined navigation needs of non-motorized vehicle travelers. Therefore, a non-motorized vehicle road network construction method based on street view imagery is needed to solve the above problems. Summary of the Invention

[0004] To address the aforementioned problems, the purpose of this invention is to propose a method for constructing a non-motorized vehicle road network based on street view images, which can be used to construct a non-motorized vehicle road network that includes geometric road segment information, road segment topology relationships, and lane attribute information, thereby alleviating urban carbon emissions and ensuring daily commuting safety.

[0005] This was achieved through the following technical solutions: A method for constructing a non-motorized vehicle road network based on street view images includes the following steps: S1. Obtaining multiple street view data, including multiple street view images and their metadata and trajectory sequence data, and cleaning and filtering the obtained street view data to remove abnormal street view images and abnormal trajectory sequence data, retaining the latest street view data; S2. Extracting motor vehicle lane areas and non-motorized vehicle lane areas from each street view image, classifying the non-motorized vehicle lane areas, and identifying the road infrastructure and status related to the non-motorized vehicle lanes; simultaneously, classifying the trajectories into motor vehicle trajectories, non-motorized vehicle trajectories, and pedestrian trajectories; S3. Fusing the road areas and road infrastructure obtained in step S2. The invention obtains non-motorized vehicle geometric segment information from facility and status information and trajectory sequence information. Based on this information and multiple street view images of traffic intersections, it constructs the topological relationships between non-motorized vehicle segments. S4: Using the motorized vehicle lane area and non-motorized vehicle lane area extracted in step S2, the actual width of the non-motorized vehicle lane is determined based on the vanishing points in the motorized vehicle lane and non-motorized vehicle lane directions, as well as the horizontal vanishing point. S5: Based on the obtained attribute information of the non-motorized vehicle lanes, the attributes of the non-motorized vehicle lanes are attached to the road network geometric data, forming a non-motorized vehicle road network containing non-motorized vehicle geometric segment information, non-motorized vehicle segment topological relationships, and non-motorized vehicle lane attribute information. This invention constructs a non-motorized vehicle road network containing geometric segment information, segment topological relationships, and lane attribute information, which can alleviate urban carbon emissions and ensure daily commuting safety.

[0006] Preferably, in step S1, the metadata includes the shooting location, shooting date, shooting angle and orientation, and shooting address. Obtaining the metadata of the street view image can provide a basis for subsequent data cleaning and filtering.

[0007] Preferably, in step S2, an image segmentation model is used to extract motor vehicle lane areas and different types of non-motor vehicle lane areas from multiple street view images; and an object recognition model is used to identify road infrastructure and conditions related to non-motor vehicle lanes from multiple street view images. Through the image segmentation model and the object recognition model, different lane areas can be accurately extracted, and road infrastructure and conditions can be identified.

[0008] Preferably, the image segmentation model is trained and adjusted by collecting publicly available street view segmentation datasets and supplementing them with street view data of the study area; the target recognition model is trained and adjusted by collecting publicly available target recognition datasets of road scenes and supplementing them with annotations of targets related to non-motorized vehicle lanes. By collecting publicly available datasets and supplementing them with data of the study area, high-precision image segmentation and target recognition models can be trained, thereby constructing a comprehensive non-motorized vehicle road network.

[0009] Preferably, in step S2, the categories of non-motorized vehicle lane areas include: completely independent, separated by guardrails, separated by painted lines, sharing lanes with pedestrians, and without dedicated bicycle lanes. Classifying the non-motorized vehicle lane areas helps to form a representative image dataset.

[0010] Preferably, in step S3, the process for obtaining the geometric road segment information of non-motorized vehicles is as follows: The first step is to divide the trajectory sequence data into multiple road segments, and associate the results of segmenting and extracting each street view image with multiple road segments. At the same time, a voting method is used to determine the category of each road segment. The second step is to determine the trajectory type of each road segment. If any road segment includes multiple trajectory types, only one trajectory type will be retained. The third step involves generating non-motorized vehicle lanes symmetrically in the left direction based on the right-hand travel direction information contained in each street view image, constructing a two-way lane-level road network, and obtaining non-motorized vehicle geometric road segment information by combining the category and trajectory type of each road segment.

[0011] By following the steps above, geometrically consistent non-motorized vehicle road segments can be obtained, thus providing data support for building a comprehensive non-motorized vehicle road network.

[0012] Preferably, the voting method follows these rules: In multiple street view images collected for any given road segment, the number of detections for each category of non-motorized vehicle lanes is counted and extracted, and the category with the highest frequency is selected as the category for the current road segment. Determining the category of the current road segment through this voting method improves the geometric consistency of non-motorized vehicle lanes.

[0013] Preferably, in the second step, the order of trajectory type retention is: non-motorized vehicle trajectories are preferred over motorized vehicle trajectories, and motorized vehicle trajectories are preferred over pedestrian trajectories. By judging and processing the trajectory types of road segments, a reliable data foundation can be provided for the subsequent construction of a comprehensive and high-precision non-motorized vehicle road network.

[0014] Preferably, in step S4, if the vanishing points in the horizontal direction intersect at infinity, the actual width W of the non-motorized vehicle lane is calculated based on the ratio of the width of the motorized vehicle lane to the width of the non-motorized vehicle lane to the pixel width they occupy in the street view image. NON-MOTOR-LANE The calculation formula is: Among them, W MOTOR-LANE P is the actual width of the motor vehicle lane. NON-MOTOR-LANE and P MOTOR-LANE These represent the pixel widths of the non-motorized vehicle lane and the motorized vehicle lane in the horizontal direction of the street view image, respectively. By using the proportion of these pixel widths, the actual width of the non-motorized vehicle lane can be accurately calculated when the vanishing points intersect at infinity.

[0015] Preferably, in step S4, if the vanishing points in the horizontal direction intersect at a finite distance, then the actual width W of the non-motorized vehicle lane is calculated according to the cross ratio formula. NON-MOTOR-LANE The calculation formula is: Here, cr1 and cr2 are both cross ratios. The cross ratio formula can be used to accurately calculate the actual width of the non-motorized vehicle lane when the vanishing points intersect at a finite distance.

[0016] The beneficial effects of this invention compared to the prior art are: The technical solution of this invention proposes a method for constructing a non-motorized vehicle road network based on street view images. This method involves obtaining multiple street view images along with their metadata and trajectory sequence data. Geometric information of non-motorized vehicle road segments is extracted from the street view images and fused with the trajectory sequence data to obtain geometric road segment information for non-motorized vehicles. Then, the topological relationships between non-motorized vehicle road segments are constructed. Finally, based on the obtained attribute information of non-motorized vehicle lanes, it is associated with the non-motorized vehicle road network data to construct a comprehensive non-motorized vehicle road network containing geometric road segment information, road segment topological relationships, and lane attribute information. This network can serve functions such as non-motorized vehicle road network evaluation and non-motorized vehicle navigation, mitigating urban carbon emissions and ensuring daily commuting safety. Attached Figure Description

[0017] Figure 1 A flowchart of a method for constructing a non-motorized road network based on street view images; Figure 2 This is a schematic diagram showing the location of the vanishing point in the horizontal direction. Detailed Implementation

[0018] The following will refer to the appendices in the embodiments of the present invention. Figures 1-2 The technical solutions in the embodiments of the present invention will be described in detail below.

[0019] like Figure 1 The diagram shows a flowchart of a non-motorized vehicle road network construction method based on street view images. After determining the area to be constructed, the method proceeds by... Figure 1 The steps outlined in this paper construct a non-motorized vehicle road network within a given area. First, multiple street view images, their metadata, and trajectory sequence data are acquired. Then, motorized vehicle lane areas and non-motorized vehicle lane areas are extracted from each street view image, and the road infrastructure and status related to the non-motorized vehicle lanes are identified. Simultaneously, the trajectory sequences are classified. Next, the acquired road areas, road infrastructure and status data, and trajectory sequence data are fused to obtain non-motorized vehicle geometric segment information, and the topological relationships between non-motorized vehicle lane segments are constructed. Then, based on the vanishing points of the motorized vehicle lanes and non-motorized vehicle lanes in both directions and horizontally, the actual width of the non-motorized vehicle lanes is determined. Finally, the attributes of the non-motorized vehicle lanes are attached to the road network geometric data to form a non-motorized vehicle road network, thus constructing a non-motorized vehicle road network containing geometric segment information, road segment topological relationships, and lane attribute information.

[0020] The method specifically includes the following steps: S1. Obtain multiple street view data by shooting, including multiple street view images and their metadata and trajectory sequence data, and clean and filter the obtained multiple street view data to remove abnormal street view images and abnormal trajectory sequence data, and retain the latest street view data.

[0021] The metadata includes the shooting location, shooting date, shooting angle and orientation, and shooting address. When cleaning and filtering multiple street view data, the specific cleaning and filtering process includes: removing duplicate data and retaining the latest street view data based on time characteristics; removing abnormal street view images and images with severe obstruction by non-motorized vehicle lanes based on image characteristics; and removing abnormal trajectory sequence data based on trajectory characteristics.

[0022] S2. Extract motor vehicle lane areas and non-motor vehicle lane areas from each street view image, and classify the non-motor vehicle lane areas into categories: completely independent, guardrail-separated, painted-separated, shared with pedestrians, and without dedicated bicycle lanes; then identify the road infrastructure and status related to the non-motor vehicle lanes, including road markings and signs, lane surface conditions, roadside parking spaces, and streetlights, etc., and lane surface conditions include damage forms such as potholes and cracks on the road surface; at the same time, calculate the running speed of each trajectory sequence data, and select appropriate clustering algorithms, such as DBSCAN, K-Means and its variants, to perform cluster analysis on the trajectory sequence data, classifying the trajectories into motor vehicle trajectories, non-motor vehicle trajectories, and pedestrian trajectories.

[0023] In this embodiment, step S2 involves using an image segmentation model to extract motor vehicle lane areas and different categories of non-motor vehicle lane areas from multiple street view images. The image segmentation model is obtained by: firstly collecting publicly available street view segmentation datasets and supplementing them with street view data of the study area; then refining the annotations of road areas, subdividing the road areas into motor vehicle lane areas and non-motor vehicle lane areas; and further annotating the non-motor vehicle lane areas according to their categories as: completely independent lanes, guardrail-isolated lanes, painted lanes, lanes shared with pedestrians, and lanes without dedicated bicycle lanes, thereby forming a representative image dataset; finally, selecting a suitable deep learning network for image segmentation based on computing power, such as DeepLabv3+, Swin Transformer, Segmenter, etc., training and adjusting the image segmentation model to obtain a model with non-motor vehicle segmentation capabilities.

[0024] In this embodiment, in step S2, a target recognition model is used to identify road infrastructure and conditions related to non-motorized vehicle lanes from multiple street view images. The target recognition model is obtained by: firstly collecting a target recognition dataset from public road scenes and supplementing it with annotations of targets related to non-motorized vehicle lanes; and secondly, further supplementing and annotating street view data of the study area as needed to form a target recognition dataset for non-motorized vehicle lane recognition. Finally, a suitable deep learning network for target recognition, such as the YOLO series, SSD, and DETR series, is selected based on computing power, and the target recognition model is trained and adjusted on the dataset to obtain a model with the ability to recognize lane road conditions and traffic ancillary facilities.

[0025] S3. By integrating the road area, road infrastructure and status, and trajectory sequence information obtained in step S2, non-motorized vehicle geometric road segment information is obtained. Based on the non-motorized vehicle geometric road segment information and multiple street view images of traffic intersections, multiple intersection nodes are obtained through endpoint clustering, and the association relationship between intersection nodes is established, thereby constructing the topological relationship between non-motorized vehicle road segments.

[0026] In this embodiment, the process for obtaining the geometric road segment information of non-motorized vehicles in step S3 is as follows: The first step is to divide the trajectory sequence data into multiple road segments, and associate the results of segmenting and extracting each street view image with multiple road segments. At the same time, a voting method is used to determine the category of each road segment.

[0027] The second step is to determine the trajectory type of each road segment. If any road segment includes multiple trajectory types, only one trajectory type is retained.

[0028] The third step involves generating non-motorized vehicle lanes symmetrically in the left direction based on the right-hand travel direction information contained in each street view image, constructing a two-way lane-level road network, and obtaining non-motorized vehicle geometric road segment information by combining the category and trajectory type of each road segment.

[0029] The voting method follows these rules: In multiple street view images collected for any given road segment, the number of detections for each category of non-motorized vehicle lanes is counted, and the category with the highest frequency is selected as the category for the current road segment. Specifically, when N street view images are collected for any given road segment, the number of times each category of non-motorized vehicle lanes in the N street view images is segmented using an image segmentation model is (S1, S2, S3, S4, S5), and the number of times related signs and markings for each category of non-motorized vehicle lanes in the N street view images are identified using an object recognition model is (D1, D2, D3, D4, D5). Then, the number of detections for each category of non-motorized vehicle lanes, C, is counted. i =S i +D iThe category that appears most frequently is taken as the category of the current road segment; where S i To separate categories, D i To identify categories, C i For statistical values; i=1,2,3,4,5, where i=1 represents a completely independent lane, i=2 represents a lane separated by guardrails, i=3 represents a lane separated by painted barriers, i=4 represents a lane shared with pedestrians, and i=5 represents a lane without a dedicated bicycle lane; N is a positive integer. This invention determines the category of the current road segment through a voting method, which can improve the geometric consistency of non-motorized vehicle road segments.

[0030] In the second step, the order of trajectory type retention is as follows: non-motorized vehicle trajectories are preferred over motorized vehicle trajectories, and motorized vehicle trajectories are preferred over pedestrian trajectories. Specifically, if non-motorized vehicle trajectories are retained, the current trajectory is smoothed and simplified before being designated as a non-motorized vehicle segment. If motorized vehicle trajectories are retained, the geometric information of the road segment is determined by combining the non-motorized vehicle lane type extracted from the street view image, the motorized vehicle lane data, and the trajectory. Specifically, if the extracted non-motorized vehicle lane type is shared with pedestrians, the motorized vehicle trajectory is a non-motorized vehicle segment; otherwise, the entire motorized vehicle trajectory is shifted based on the extracted motorized vehicle lane data and the lane in which the trajectory is located. If pedestrian trajectories are retained, if the non-motorized vehicle lane type extracted from the current road segment is shared with pedestrians, the pedestrian trajectory is a non-motorized vehicle segment; otherwise, the pedestrian trajectory is shifted based on the relationship between the sidewalk and non-motorized vehicle lane in the street view image. At the same time, considering that pedestrians may walk randomly, the trajectory needs to be smoothed and simplified. Thus, by judging and processing the trajectory type of the road segment, a reliable data foundation can be provided for the subsequent construction of a comprehensive and high-precision non-motorized vehicle road network.

[0031] S4. Using the motor vehicle lane area and non-motor vehicle lane area extracted in step S2, the edge lines of the lanes, lane markings, and other feature lines in the street view image are obtained by using straight line detection and clustering methods. Then, based on the vanishing points of the motor vehicle lane and non-motor vehicle lane in the direction and the vanishing point in the horizontal direction, the actual width of the non-motor vehicle lane is determined.

[0032] like Figure 2 The diagram shows the horizontal vanishing point. Points p1, p2, p3, and p4 are collinear, and the straight line formed by the four points is perpendicular to the direction of travel and converges at the horizontal vanishing point vp. Among them, p1 and p2 are the edge points of the motor vehicle lane, and p3 and p4 are the edge points of the non-motor vehicle lane.

[0033] In this embodiment, in step S4, if the vanishing points in the horizontal direction intersect at infinity, the actual width W of the non-motorized vehicle lane is calculated based on the ratio of the width of the motorized vehicle lane and the non-motorized vehicle lane to the pixel width they occupy in the street view image. NON-MOTOR-LANE The calculation formula is: Among them, W MOTOR-LANE P is the actual width of the motor vehicle lane. NON-MOTOR-LANE and P MOTOR-LANE These represent the pixel widths of the non-motorized vehicle lanes and motorized vehicle lanes in the horizontal direction of the street view image, respectively.

[0034] Meanwhile, if the vanishing points in the horizontal direction intersect at a finite distance, the actual width W of the non-motorized vehicle lane can be calculated using the intersection ratio formula. NON-MOTOR-LANE The calculation formula is: Where cr1 and cr2 are cross ratios, , d(a,b) represents the pixel distance between any two points, where a and b represent any two points among p1, p2, p3, p4, and vp. Based on the position of the vanishing point in the horizontal direction, this invention can accurately calculate the actual width of the non-motorized vehicle lane by using the ratio or cross-ratio formula of the width of the motorized vehicle lane and the non-motorized vehicle lane to the pixel width they occupy on the street view image.

[0035] S5. Based on the obtained attribute information of non-motorized vehicle lanes, including lane type, lane width, whether there are roadside parking spaces, whether there are streetlights, and the condition of road surface damage, the attributes of non-motorized vehicle lanes are attached to the road network geometric data to form a non-motorized vehicle road network containing non-motorized vehicle geometric segment information, non-motorized vehicle segment topology relationship, and non-motorized vehicle lane attribute information.

[0036] In summary, this invention proposes a method for constructing a non-motorized vehicle road network based on street view images. It obtains multiple street view images along with their metadata and trajectory sequence data, extracts geometric information of non-motorized vehicle road segments from the street view images, and fuses this information with the trajectory sequence data to obtain geometric information of non-motorized vehicle road segments. Then, it constructs the topological relationships between non-motorized vehicle road segments. Finally, based on the obtained attribute information of non-motorized vehicle lanes, it associates this information with the non-motorized vehicle road network data to construct a comprehensive non-motorized vehicle road network containing geometric information, road segment topological relationships, and lane attribute information. This network can serve functions such as non-motorized vehicle road network evaluation and non-motorized vehicle navigation, mitigating urban carbon emissions and ensuring daily commuting safety, demonstrating significant advancements.

[0037] The above embodiments are merely illustrative of the technical concept of the present invention and should not be construed as limiting the scope of protection of the present invention. Any modifications made to the technical solutions based on the technical concept proposed in this invention shall fall within the scope of protection of this invention.

Claims

1. A method for constructing a non-motorized vehicle road network based on street view images, characterized in that, The method includes the following steps: S1. Obtain multiple street view data by shooting, including multiple street view images and their metadata and trajectory sequence data, and clean and filter the obtained multiple street view data to remove abnormal street view images and abnormal trajectory sequence data, and retain the latest street view data; S2. Extract motor vehicle lane areas and non-motor vehicle lane areas from each street view image, classify the non-motor vehicle lane areas, and then identify the road infrastructure and status related to the non-motor vehicle lanes; at the same time, divide the trajectory into motor vehicle trajectory, non-motor vehicle trajectory and pedestrian trajectory. S3. By integrating the road area, road infrastructure and status information and trajectory sequence information obtained in step S2, non-motorized vehicle geometric road segment information is obtained. Based on the non-motorized vehicle geometric road segment information and multiple street view images of traffic intersections, the topological relationship between non-motorized vehicle road segments is constructed. S4. Based on the motor vehicle lane area and non-motor vehicle lane area extracted in step S2, determine the actual width of the non-motor vehicle lane according to the vanishing points in the direction of the motor vehicle lane and the non-motor vehicle lane and the vanishing point in the horizontal direction. S5. Based on the obtained attribute information of non-motorized vehicle lanes, attach the attributes of non-motorized vehicle lanes to the road network geometric data to form a non-motorized vehicle road network that includes non-motorized vehicle geometric segment information, non-motorized vehicle segment topological relationships, and non-motorized vehicle lane attribute information.

2. The method for constructing a non-motorized vehicle road network based on street view images according to claim 1, characterized in that, In step S1, the metadata includes the shooting location, shooting date, shooting angle and orientation, and shooting address.

3. The method for constructing a non-motorized vehicle road network based on street view images according to claim 1, characterized in that, In step S2, an image segmentation model is used to extract motor vehicle lane areas and different types of non-motor vehicle lane areas from multiple street view images; an object recognition model is used to identify the road infrastructure and status related to non-motor vehicle lanes from multiple street view images.

4. The method for constructing a non-motorized vehicle road network based on street view images according to claim 3, characterized in that, By collecting publicly available street scene segmentation datasets and supplementing them with street scene data from the study area, we trained and adjusted the image segmentation model; by collecting publicly available target recognition datasets from road scenes and supplementing them with annotations of targets related to non-motorized vehicle lanes, we trained and adjusted the target recognition model.

5. The method for constructing a non-motorized vehicle road network based on street view images according to claim 1, characterized in that, In step S2, the categories of non-motorized vehicle lane areas include: completely independent, guardrail-separated, painted-separated, shared with pedestrians, and no dedicated bicycle lane.

6. The method for constructing a non-motorized vehicle road network based on street view images according to claim 1, characterized in that, In step S3, the process for obtaining the geometric road segment information for non-motorized vehicles is as follows: The first step is to divide the trajectory sequence data into multiple road segments, and associate the results of segmenting and extracting each street view image with multiple road segments. At the same time, a voting method is used to determine the category of each road segment. The second step is to determine the trajectory type of each road segment. If any road segment includes multiple trajectory types, only one trajectory type is retained. The third step involves generating non-motorized vehicle lanes symmetrically in the left direction based on the right-hand travel direction information contained in each street view image, constructing a two-way lane-level road network, and obtaining non-motorized vehicle geometric road segment information by combining the category and trajectory type of each road segment.

7. The method for constructing a non-motorized vehicle road network based on street view images according to claim 6, characterized in that, The voting method follows these rules: In multiple street view images collected for any given road segment, the number of detections for each category of non-motorized vehicle lanes is counted and extracted, and the category that appears most frequently is selected as the category for the current road segment.

8. The method for constructing a non-motorized vehicle road network based on street view images according to claim 6, characterized in that, In the second step, the order of trajectory type retention is: non-motorized vehicle trajectory is preferred over motorized vehicle trajectory, and motorized vehicle trajectory is preferred over pedestrian trajectory.

9. The method for constructing a non-motorized vehicle road network based on street view images according to claim 1, characterized in that, In step S4, if the horizontal vanishing points intersect at infinity, the actual width W of the non-motorized vehicle lane is calculated based on the width of the motorized vehicle lane and the non-motorized vehicle lane and their pixel width ratio in the street view image. NON-MOTOR-LANE The calculation formula is: Among them, W MOTOR-LANE P is the actual width of the motor vehicle lane. NON-MOTOR-LANE and P MOTOR-LANE These represent the pixel widths of the non-motorized vehicle lanes and motorized vehicle lanes in the horizontal direction of the street view image, respectively.

10. A method for constructing a non-motorized vehicle road network based on street view images according to claim 1, characterized in that, In step S4, if the vanishing points in the horizontal direction intersect at a finite distance, then the actual width W of the non-motorized vehicle lane is calculated according to the cross ratio formula. NON-MOTOR-LANE The calculation formula is: , where cr1 and cr2 are cross ratios.

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