A computer-implemented method for generating a lane boundary model of a route along which an autonomous vehicle travels

Abstract

A computer-implemented method for generating a lane boundary model of a path along which an autonomous vehicle travels. The present invention provides a computer-implemented method for generating a lane boundary model of a path along which an autonomous vehicle travels. This computer-implemented method includes steps of acquiring a three-dimensional LiDAR point cloud of a path and an image of the path along which the autonomous vehicle travels, detecting lane boundaries in the image of the path using a machine learning model, and generating a lane boundary model based on a plurality of points of the three-dimensional LiDAR point cloud of the path that are positionally corresponding to the lane boundaries detected at that time.

Description

Technical Field

[0001] The subject matter of the present disclosure relates to a computer-implemented method, a non-transitory or transitory computer-readable medium, and an autonomous vehicle for generating a lane boundary model of a path along which the autonomous vehicle travels.

Background Art

[0002] An autonomous vehicle (AV) navigates a path using various sensors. For example, an AV can navigate a path using an image captured by the AV. To properly navigate the path, it is important for the AV to know the position of the lane boundaries on the path.

[0003] A machine learning model can be used to infer the position of lane boundaries in an image. However, the lane boundaries are only useful for that image. It is difficult to transfer the lane boundaries to other lanes of its path or to other modalities.

Summary of the Invention

Problems to be Solved by the Invention

[0004] The subject matter of the present disclosure aims to improve the prior art by reducing such problems.

Means for Solving the Problems

[0005] According to an aspect of the present disclosure, there is provided a computer-implemented method for generating a lane boundary model of a path along which an autonomous vehicle travels, the method including: obtaining a 3D LiDAR point cloud of the path and an image of the path along which the autonomous vehicle travels; detecting lane boundaries in the image of the path using a machine learning model; and generating a lane boundary model based on a plurality of points of the 3D LiDAR point cloud of the path that are positionally corresponding to the detected lane boundaries.

[0006] By generating a lane boundary based on the 3D LiDAR point cloud of the path, the lane boundary model can be easily transferred to other images and other modalities.

[0007] Training a machine learning model to automatically detect lane boundaries from an image of a path on which an autonomous vehicle travels means automatically detecting an occlusionless lane boundary, a lane boundary without occlusions, or a lane boundary from which one or more occluded portions have been removed from an image of a path on which an autonomous vehicle travels. As used herein, the term "occlusion" can be used to mean any portion of a lane boundary that is hidden or obscured from view by another feature on the path. Examples of such features include other vehicles, roadworks, puddles, and the like.

[0008] As used herein, the term "lane boundary" can be used to define an interface between an area where an AV can travel and an area where an AV cannot travel. The interface may be structural, such as a curb between a road and a sidewalk / pavement. The interface may be non-structural, such as a lane marking on a road.

[0009] In one embodiment, the step of obtaining the 3D LiDAR point cloud of the path may include the step of capturing a plurality of LiDAR points of the path by the autonomous vehicle and the step of integrating the plurality of LiDAR points to generate a 3D point cloud of the path.

[0010] In one embodiment, the plurality of LiDAR points and the image may be paired.

[0011] In one embodiment, the step of obtaining the image of the path may include the step of capturing the image of the path by the autonomous vehicle.

[0012] In one embodiment, the step of generating the lane boundary model based on a plurality of points of the 3D LiDAR point cloud of the path that corresponds positionally to the detected lane boundary may include constructing a 3D point cloud of the lane boundary by selecting, from the integrated 3D point cloud, a plurality of points that correspond positionally to the identified lane boundary; clustering the plurality of points of the 3D point cloud of the lane boundary into one or more clusters using the distance between points; and constructing, for each cluster, an optimal spline as the lane boundary model.

[0013] In one embodiment, the distance between points may be calculated by determining the distance between each point and its respective adjacent point and clustering the plurality of points into the cluster if their respective distances are less than a distance threshold.

[0014] In one embodiment, the distance threshold may be weighted according to the direction to its respective adjacent point.

[0015] In one embodiment, the distance threshold may be weighted so as to increase in a first direction and decrease in a second direction, the first direction being parallel to the direction of travel of the autonomous vehicle and the second direction being perpendicular to the direction of travel of the autonomous vehicle.

[0016] In one embodiment, the step of constructing the optimal spline may include repeatedly selecting a random set of points from the plurality of points within the cluster; constructing an optimal spline for each repeatedly selected set; calculating, for each set, the distance from the optimal spline to the points within the set; and selecting, as the optimal spline for the cluster, the optimal spline with the smallest distance.

[0017] In one embodiment, the distance may be the total distance.

[0018] In one embodiment, the distance may be the average distance.

[0019] In one embodiment, the machine learning model may include a neural network.

[0020] In one embodiment, the neural network may be a convolutional neural network.

[0021] According to one aspect of the present disclosure, there is provided a temporary or non-temporary computer-readable medium storing instructions that, when executed by a processor, cause the processor to execute the method according to any one of the preceding claims.

[0022] According to one aspect of the present disclosure, there is provided an autonomous vehicle including a non-temporary computer-readable medium. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] The subject matter of the present disclosure is best described with reference to the accompanying drawings.

[0024]

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Best Mode for Carrying Out the Invention

[0025] The embodiments described herein are embodied as a set of instructions stored as electronic data on one or more storage media. Specifically, the instructions may be provided on a transient or non-transient computer-readable medium. When executed by a processor, the processor is configured to execute the various methods described in the following embodiments. Thus, these methods may be computer-implemented methods. In particular, the processor and the storage containing the instructions may be incorporated into a vehicle. The vehicle may be an AV.

[0026] The following embodiments provide specific exemplary examples, but those exemplary examples should not be construed as limiting, and the scope of protection is defined by the claims. The features of a particular embodiment may be used in combination with the features of other embodiments to the extent that they do not extend the subject matter beyond the content of the present disclosure.

[0027] Referring to FIG. 1, the AV 10 may include a plurality of sensors 12, 13. The sensor 12 may be attached to the roof of the AV 10. The sensor 13 may be attached to the front grille at the front of the AV 10. The sensors 12, 13 may be communicably connected to a computer 14. The computer 14 may be mounted on the AV 10. The computer 14 may include a processor 16 and a memory 18. The memory may include the non-transitory computer-readable medium described above. Alternatively, the non-transitory computer-readable medium may be remotely located and communicably linked to the computer 14 via the cloud 20. The computer 14 may be communicably linked to one or more actuators 22 to control the actuators 22 to move the AV 10. The actuators may include, for example, motors, braking systems, power steering systems, and the like.

[0028] The sensors 12, 13 may include various sensor types. Examples of sensor types include LiDAR sensors, RADAR sensors, and cameras. Each sensor type may be referred to as a sensor modality. Each sensor type may record data associated with the sensor modality. For example, a LiDAR sensor may record LiDAR modality data. When the sensor 13 is attached to the front of the AV 10, the sensor 13 may include a LiDAR sensor to more easily detect features such as lane boundaries behind a particular shield (e.g., a vehicle) when attached near the ground.

[0029] The data may capture various scenes encountered by the AV 10. For example, the scene may be a visible scene around the AV 10 and may include roads, buildings, weather, objects (such as other vehicles, pedestrians, animals, etc.).

[0030] Referring to FIG. 2, according to one or more embodiments, a computer-implemented method is provided for training a machine learning model to automatically detect lane boundaries from an image of a path along which an AV travels. As part of this method, a computer-implemented method for generating a lane boundary model may be provided.

[0031] To generate a 3D LiDAR point cloud of the path, the method may include, in step 100, obtaining a plurality of LiDAR points of the path along which the AV 10 is moving. Step 100 may also include obtaining one or more images of the same path. The image may be captured by a sensor 12 attached to a roof in the form of one or more cameras. The LiDAR points may be captured by a sensor 13 in the form of a LiDAR sensor. The LiDAR sensor 13 attached to the front of the AV 10 allows the LiDAR beam to pass under specific objects such as parked vehicles that are blocking the lane boundary. The LiDAR points and the image may be paired. In other words, the LiDAR points and the image captured by the AV 10 may be synchronized because they are linked to the same system clock of the computer 14.

[0032] In step 102, a machine learning model is provided. The machine learning model may include a neural network. The neural network may be a convolutional neural network. The machine learning model may be trained to automatically detect lane boundaries from an image of a path along which an autonomous vehicle travels. First, the machine learning model is trained using manually labeled training data of images having manual labels representing lane boundaries on the path within the image, as described below.

[0033] In step 104, the method includes identifying one or more lane boundaries within one or more images captured in step 100 using the machine learning model. The lane boundaries are stored as automatically generated labels as shown in 106.

[0034] Referring briefly to FIG. 3, a three-dimensional LiDAR point cloud of path 200 may be formed by integrating a plurality of LiDAR points.

[0035] Referring further to FIG. 2, in step 108, an annotated map 110 is rendered by annotating the three-dimensional LiDAR point cloud of path 200 at 108. As will be described below, the annotated map 110 includes a lane boundary model.

[0036] Referring to FIG. 4, a three-dimensional LiDAR point cloud of lane boundary 202 may be constructed by selecting a plurality of points from the integrated three-dimensional point cloud of path 200 that are positionally corresponding to the identified lane boundaries. Since the images and LiDAR used to derive the lane boundaries using a machine learning model are paired, the points of the three-dimensional point cloud of the path that are positionally corresponding to the lane boundaries can be selected as the three-dimensional LiDAR point cloud of lane boundary 202. FIG. 5 shows the same method of constructing the three-dimensional point clouds of lane boundaries 202 of different paths.

[0037] Referring to FIG. 6, a plurality of points from the three-dimensional point cloud of the lane boundary are clustered into clusters 202A - D for each specific lane boundary. Since there are four lane boundaries in FIG. 6, there are four clusters 202A - D. The step of clustering the points into clusters is performed using the inter-point distance. More specifically, the inter-point distance may be the distance between each point and its adjacent points. If the respective distances are less than a distance threshold, the plurality of points are clustered into clusters, and the distance threshold is weighted according to the direction to its adjacent points.

[0038] The distance threshold is weighted to increase in the first direction and decrease in the second direction. The first direction may be parallel to the moving direction of the autonomous vehicle, and the second direction may be perpendicular to the moving direction of the autonomous vehicle.

[0039] For example, select the first point. Identify the points adjacent to the first point. Measure the distance between the first point and each identified adjacent point. The distance can be derived from the position information of the LiDAR scan. The distance may be a real-world distance.

[0040] The distance threshold may be weighted to increase non-linearly. For example, in a plan view, the distance threshold may appear elliptical. For example, if the first adjacent point is X mm away from the first point in the longitudinal direction of the AV's movement and the second adjacent point is also X mm away from the first point in the transverse direction of the AV's movement, the first adjacent point may be within the boundary of the distance threshold, while the second adjacent point may not be within the boundary of the distance threshold.

[0041] FIG. 7 shows a view similar to FIG. 6 of the clusters obtained for the lane boundaries of different routes. In FIG. 7, there are five clusters 202A to E.

[0042] Referring to FIG. 8, using a spline generation algorithm, an optimal spline is constructed for each cluster. To construct the optimal spline, first, a random set of points is repeatedly selected from multiple points within the cluster. 3 There may be 10

[0043] Next, calculate the distance from the optimal spline to the points within the set. The distance may be the total distance or the average distance. For example, the unit distance between each point within the set and the spline may be calculated. These unit distances may be the smallest distances between each point and the spline. In other words, the point on the spline that is closest to each respective point is used to measure the unit distance. The total distance is obtained by summing all the unit distances. The average distance is obtained by dividing the total distance by the number of points within the set. Select the optimal spline with the smallest distance as the optimal splines 204A - D of the cluster. Combine all the optimal splines 204A - D of the image to form a lane boundary model. FIG. 9 shows a similar view to FIG. 8, and the same method is used for different routes.

[0044] Referring further to FIG. 2, as described above, at 108, the lane boundary model annotates the 3D LiDAR point cloud (map) of the route to form an annotated map 110.

[0045] The method may also include a computer - implemented method for generating training data for training a machine - learning model to automatically detect lane boundaries from an image of a route along which an autonomous vehicle travels.

[0046] In step 112, based on the 3D LiDAR point cloud (map) of the route, project the lane boundary model onto a plurality of traversals of the route. In this specification, the term "traversal" is used to mean different journeys along the same route. The lane boundary model should be valid for all traversals, but certain parts of the lane boundary may be occluded because an object appears along the route on some traversals but not on others. In particular, dynamic objects may vary between traversals.

[0047] Accordingly, in step 114, one or more parts of one or more lane boundaries may be removed to delete the occluded parts of the lane boundary. Automatically remove one or more parts of the lane boundary.

[0048] Referring to FIG. 10, the automatic removal of one or more portions of the lane boundary may include generating a free space model based on an image using a further machine learning model. The free space model includes one or more free space regions and one or more occluded regions. The further machine learning model may include a neural network. The neural network may be a convolutional neural network.

[0049] The further machine learning model is initially trained using pairs of manually annotated free space boundaries and images. The boundary 208 separates one or more free space regions 210 and one or more occluded regions 212. Thus, the further machine learning model is trained to generate a free space boundary 208 between one or more free space regions 210 and one or more occluded regions 212.

[0050] Referring to FIGS. 2 and 10, the method may include, in step 114, deleting one or more portions that overlap with one or more occluded regions of the lane boundary and retaining one or more portions that overlap with one or more free space regions of the lane boundary. Thus, the method generates a lane boundary without occlusions that includes only one or more portions of the retained lane boundary in the form of ground truth 116. Also, a training example 118 including an image annotated with the lane boundary without occlusions is generated. In step 120, the raw image of the driving lane and the image annotated with the lane boundary without occlusions may be used as a training pair for training the machine learning model 102.

[0051] Referring further to FIG. 2, in step 122, as described above, the path image may be manually annotated initially to train the machine learning model 102.

[0052] In step 124, the method may include capturing new images by the AV10 during different runs of the path. Since point clouds are not required during inference, there is no need to capture LiDAR data at this stage.

[0053] In step 126, the machine learning model 102 is used to perform an inference on a new image to identify a new lane boundary. The new lane boundary is indicated by 128.

[0054] In step 130, the new lane boundary 128 is compared with the ground truth 116 to evaluate the performance of the machine learning model 102. Based on the comparison, an accuracy score of the new lane boundary may be determined. More specifically, the accuracy score may be determined by one or more of the following methods. In other words, the accuracy score may be equivalent to any of a precision score, a recall score, or an F1 score. Based on the comparison, a precision score may be determined. Based on the comparison, a recall score may be determined. Based on the comparison, an F1 score may be determined.

[0055] The aforementioned scores are calculated by different formulas using false negatives (FN), false positives (FP), true negatives (TN), and true positives (TP) of the results obtained by comparison. These different formulas may be as follows.

[0056] Accuracy score = (TP + TN) / (TP + FP + FN + TN) Simply put, the accuracy score is the ratio of the correctly predicted observations to the total observations.

[0057] Precision score = TP / (TP + FP) Therefore, the precision score is the ratio of the correctly predicted positive observations to the total predicted positive observations.

[0058] Recall score = TP / (TP + FN) Therefore, the recall score is the ratio of the correctly predicted positive observations to all the observations within the actual class.

[0059] F1 score = 2 × (recall score × precision score) / (recall score + precision score) Therefore, the F1 score is the weighted average of the precision score and the recall score.

[0060] In step 130, if the accuracy score is greater than or equal to the accuracy threshold, the machine learning model 102 is considered to have made an accurate prediction. In other words, the machine learning model is considered to be accurate. If the accuracy score is less than the accuracy threshold, the machine learning model 102 is considered to have not made an accurate prediction. In other words, the machine learning model is considered to be inaccurate.

[0061] When the machine learning model 102 is considered to have not made an accurate prediction, during further iterations of the method, step 112 is repeated using a new image as the above image, and the lane boundary model is projected onto the new image 124. Then, steps 114, 118, and 120 are executed to improve the accuracy of the machine learning model 102 for the acquired driving line of the new image. In 116, a new ground truth may be generated for the new image.

[0062] Summarizing the above, referring to FIG. 11, a computer-implemented method for generating a lane boundary model of a path along which an autonomous vehicle travels, the method including: a step (S300) of acquiring a 3D LiDAR point cloud of the path and an image of the path along which the autonomous vehicle travels; a step (S302) of detecting a lane boundary in the image of the path using a machine learning model; and a step (S304) of generating a lane boundary model based on a plurality of points of the 3D LiDAR point cloud of the path that are positionally corresponding to the detected lane boundary, is provided.

[0063] The foregoing embodiments have been described for the purpose of explaining the subject matter of the present disclosure, but the features of the embodiments should not be construed as limiting the scope of protection. To avoid doubt, the scope of protection is defined by the appended claims.

[0064] The following includes one or more items providing information related to the subject matter of the present disclosure.

[0065] Item Item 1. A computer-implemented method for generating training data for training a machine learning model to automatically detect lane boundaries from an image of a path on which an autonomous vehicle travels, the method comprising: obtaining an image captured by the autonomous vehicle while traveling on the path; projecting a lane boundary model onto the image based on a 3D LiDAR point cloud of the path; and generating a training data example for training the machine learning model by automatically removing one or more portions of the lane boundary corresponding to one or more respective occluded portions in the image.

[0066] Item 2. The step of automatically removing one or more portions of the lane boundary includes: generating a free space model including one or more free space regions and one or more occluded regions based on the image using a further machine learning model; deleting one or more portions of the lane boundary that overlap with one or more of the occluded regions; and retaining one or more portions of the lane boundary that overlap with one or more of the free space regions. The computer-implemented method according to Item 1.

[0067] Item 3. The computer-implemented method according to Item 2, further comprising generating a ground truth including one or more of the retained portions of the lane boundary.

[0068] Item 4. The computer-implemented method according to Item 2 or 3, wherein the further machine learning model includes a neural network.

[0069] Item 5. The computer-implemented method according to Item 4, wherein the neural network is a convolutional neural network.

[0070] Item 6. The computer-implemented method according to any one of Items 1 to 5, further comprising: obtaining a new image captured by the autonomous vehicle during different travels on the path; and using the machine learning model to perform an inference on the new image to determine a new lane boundary.

[0071] Item 7. The computer-implemented method according to item 6 when dependent on item 3, further comprising: comparing the new lane boundary with the ground truth; and determining a correct rate score of the new lane boundary based on the comparison.

[0072] Item 8. The computer-implemented method according to item 7, wherein determining the correct rate score is determined as one or more of a precision score based on the comparison, and / or a recall score based on the comparison, and / or an f1 score based on the comparison.

[0073] Item 9. The computer-implemented method according to item 7 or 8, further comprising: determining that the machine learning model is accurate when the correct rate score is greater than or equal to a correct rate threshold; and determining that the machine learning model is not accurate when the correct rate score is less than the correct rate threshold.

[0074] Item 10. When it is determined that the machine learning model is not accurate, based on the 3D LiDAR point cloud of the path, projecting the lane boundary model onto an image; and generating a training data example for training the machine learning model by automatically removing one or more portions of the lane boundary corresponding to one or more respective occluded portions in the image, and repeating the steps using a new image as the image.

[0075] Item 11. The computer-implemented method according to any one of items 1 to 10, further comprising: acquiring a plurality of LiDAR points of the path by the autonomous vehicle; and integrating the plurality of LiDAR points to generate a 3D point cloud of the path.

[0076] Item 12. The computer-implemented method according to item 11, wherein the plurality of LiDAR points and the image are paired.

[0077] Item 13. A step of identifying a lane boundary from a captured image using a machine learning algorithm, a step of constructing a three-dimensional point cloud of the lane boundary by selecting a plurality of points corresponding in position to the identified lane boundary from an integrated three-dimensional point cloud, a step of clustering a plurality of points of the three-dimensional point cloud of the lane boundary into one or more clusters using an inter-point distance, and a step of constructing an optimal spline for each cluster as the lane boundary model. The computer-implemented method according to Item 11 or 12 further includes the above steps.

[0078] Item 14. The computer-implemented method according to Item 13, wherein the inter-point distance is calculated by determining a distance between each point and its adjacent point and clustering the plurality of points into the cluster if their respective distances are less than a distance threshold.

[0079] Item 15. The computer-implemented method according to Item 134, wherein the distance threshold is weighted according to the direction to its respective adjacent point.

[0080] Item 16. The computer-implemented method according to Item 15, wherein the distance threshold is weighted so as to increase in a first direction and decrease in a second direction, the first direction is parallel to the moving direction of the autonomous vehicle, and the second direction is perpendicular to the moving direction of the autonomous vehicle.

[0081] Item 17. The step of constructing the optimal spline includes repeatedly selecting a random set of points from a plurality of points within the cluster, constructing an optimal spline for each repeatedly selected set, calculating a distance from the optimal spline to the points within the set for each set, and selecting the optimal spline with the smallest distance as the optimal spline of the cluster. The computer-implemented method according to any one of Items 13 to 16 includes the above steps.

[0082] Item 18. The computer-implemented method according to Item 17, wherein the distance is the total distance.

[0083] Item 19. The computer-implemented method according to item 17, wherein the distance is an average distance.

[0084] Item 20. The computer-implemented method according to any one of items 1 to 19, wherein the machine learning model includes a neural network.

[0085] Item 21. The computer-implemented method according to item 20, wherein the neural network is a convolutional neural network.

[0086] Item 22. A computer-implemented method for training a machine learning model to automatically detect lane boundaries from an image of a path on which an autonomous vehicle travels, the method including: obtaining a plurality of training data examples of an image of a path driving line in which lane boundaries without occlusion are identified; and training a machine learning model to automatically detect lane boundaries from an image of a path on which an autonomous vehicle travels.

[0087] Item 23. The computer-implemented method according to item 22, wherein the step of obtaining a plurality of training data examples includes the computer-implemented method according to any one of items 1 to 20.

[0088] Item 24. A transient or non-transient computer-readable medium storing instructions that, when executed by a processor, cause the processor to execute the method of any one of the preceding items.

[0089] Item 25. An autonomous vehicle including the non-transient computer-readable medium of item 24.

Claims

1. A computer-implemented method for generating a lane boundary model of a path on which an autonomous vehicle travels, comprising: obtaining a 3D LiDAR point cloud of the path and an image of the path on which the autonomous vehicle travels; detecting lane boundaries in the image of the path using a machine learning model; generating a lane boundary model based on a plurality of points of the 3D LiDAR point cloud of the path that are positionally corresponding to the detected lane boundaries; and a computer-implemented method.

2. The step of obtaining the 3D LiDAR point cloud of the path comprises: capturing a plurality of LiDAR points of the path by the autonomous vehicle; and integrating the plurality of LiDAR points to generate a 3D point cloud of the path. The computer-implemented method according to claim 1.

3. The computer-implemented method according to claim 2, wherein the plurality of LiDAR points and the image are paired.

4. The step of obtaining the image of the path comprises capturing the image of the path by the autonomous vehicle. The computer-implemented method according to any one of claims 1 to 3.

5. The step of generating the lane boundary model based on a plurality of points of the 3D LiDAR point cloud of the path that are positionally corresponding to the detected lane boundaries comprises: constructing a 3D point cloud of the lane boundary by selecting a plurality of points that are positionally corresponding to the identified lane boundary from the integrated 3D point cloud; clustering a plurality of points of the 3D point cloud of the lane boundary into one or more clusters using an inter-point distance; and constructing an optimal spline for each cluster as the lane boundary model. The computer-implemented method according to any one of claims 1 to 4.

6. The inter-point distance is calculated by: determining a distance between each point and its adjacent point; and clustering the plurality of points into the cluster if their respective distances are less than a distance threshold. The computer-implemented method according to claim 5.

7. The computer-implemented method according to claim 6, wherein the distance threshold is weighted according to a direction to its respective adjacent point.

8. The distance threshold is weighted so as to increase in a first direction and decrease in a second direction, the first direction being parallel to the moving direction of the autonomous vehicle and the second direction being perpendicular to the moving direction of the autonomous vehicle, the computer-implemented method according to claim 7.

9. The step of constructing the optimal spline comprises: repeatedly selecting a random set of points from a plurality of points within the cluster; constructing an optimal spline for each repeatedly selected set; calculating, for each set, the distance from the optimal spline to the points within the set; selecting as the optimal spline for the cluster the optimal spline with the smallest distance The computer-implemented method according to any one of claims 5 to 8, comprising:

10. The distance is the total distance, the computer-implemented method according to claim 9.

11. The distance is the average distance, the computer-implemented method according to claim 9.

12. The machine learning model includes a neural network, the computer-implemented method according to any one of claims 1 to 11.

13. The neural network is a convolutional neural network, the computer-implemented method according to claim 12.

14. A temporary or non-temporary computer-readable medium storing instructions that, when executed by a processor, cause the processor to execute the method according to any one of claims 1 to 13.

15. An autonomous vehicle comprising the temporary computer-readable medium according to claim 14.

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