Blind area determination method and apparatus, electronic device, storage medium, and program product

Abstract

The application discloses a blind area determination method and device, electronic equipment, a storage medium and a program product. The method comprises the following steps: selecting a road section with a curvature less than a preset curvature value from a road section set as a curved road section; obtaining driving data of a plurality of vehicles driving on the curved road section; determining a curve score of the curved road section based on the driving data corresponding to the curved road section and road section attribute data; and determining the curved road section as a curve blind area when the curve score is greater than a preset curve score. In this way, for each curved road section with a low road curvature, the curve score of the curved road section can be determined based on the driving data corresponding to the curved road section and the road section attribute data, the curve score represents the possibility of the existence of a blind area road section in the corresponding curved road section, and then the curved road section with a curve score greater than a preset curve score is determined as a curve blind area, so that the curved road section with a visual blind area in the curved road section with a low road curvature can be effectively screened out.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of safe driving, and in particular to a blind area determination method and device, an electronic device, a storage medium and a program product. BACKGROUND

[0002] Automobile driving safety is increasingly concerned by task platforms of automobile enterprises. When the automobile drives on a curved road section, the driver often has a visual blind area. In particular, when the two sides of the curved road are often accompanied by extreme terrains such as valleys and riverbeds, it will bring great challenges to the safe driving of the driver. Therefore, the reminder of the task platform for the curved road section with a visual blind area is particularly important, and the primary task of the blind area reminder is to first determine the curved road section covering the visual blind area from the entire road network section.

[0003] In related technologies, the blind area is often determined by the curvature of the curved road. Specifically, the curved road section with a larger curvature is determined as a curved blind area. However, in a real curved road scene, there is often a curved road section with a smaller curvature but still has a visual blind area, and the related technology cannot determine whether there is a blind area in this scene. The curvature refers to the degree of curvature of a curve at a point; the greater the curvature, the greater the degree of curvature of the curve at the point; the smaller the curvature, the smaller the degree of curvature of the curve at the point.

[0004] It should be noted that the above statements are only used to provide background technical information related to the present application, and do not necessarily constitute prior art. SUMMARY

[0005] The embodiments of the present application provide a blind area determination method, device, electronic device, storage medium and program product to determine the curved road section with a visual blind area in the curved road section with a smaller curvature, so as to assist the driver to drive based on the confirmed curved blind area and improve the driving safety.

[0006] Thus, the problem that the task platform can only determine the blind area existing in the curved road section with a larger curvature in related technologies is alleviated.

[0007] According to an aspect of an embodiment of the present application, a blind area determination method is provided, comprising:

[0008] selecting a road section with a road section curvature less than a preset curvature value from a road section set as a curved road section;

[0009] obtaining driving data of a plurality of vehicles driving on the curved road section;

[0010] determining a curved score of the curved road section based on the driving data corresponding to the curved road section and the road section attribute data;

[0011] determining the curved road section as a curved road blind area when the curved road score is greater than the preset curved road score.

[0012] Optionally, in another embodiment based on the above method of the application, the step of determining the curved road section as a curved road blind area when the curved road score is greater than the preset curved road score comprises:

[0013] obtaining a road section picture corresponding to the curved road section; the road section picture comprises a picture taken when a vehicle travels on the curved road section, and / or a satellite picture of the curved road section collected by a satellite;

[0014] performing occlusion identification on the road section picture and calculating an occlusion rate;

[0015] determining the curved road section as a curved road blind area when the curved road score is greater than the preset curved road score and the occlusion rate is greater than a preset occlusion rate.

[0016] Optionally, in another embodiment based on the above method of the application, the driving data comprises one or more of the following: driving speed and deceleration amplitude; and the road attribute data comprises one or more of the following: curvature value, road section turning angle, road section width, road section paving degree, road section grade, and road section traffic volume.

[0017] Optionally, in another embodiment based on the above method of the application, the step of determining the curved road score of the curved road section based on the driving data and the road attribute data of the curved road section comprises:

[0018] matching the driving data and the road attribute data with a preset mapping table to obtain the curved road score of the curved road section; the preset mapping table records a mapping relationship between preset driving data, preset road attribute data, and a preset curved road score.

[0019] Optionally, in another embodiment based on the above method of the application, the step of selecting a road section with a road section curvature less than a preset curvature value from a road section set as the curved road section comprises:

[0020] selecting a road section with a road section curvature less than a preset curvature value and a road section turning angle less than a preset turning angle from a road section set as the curved road section.

[0021] Optionally, in another embodiment based on the above method of the application, the step of selecting a road section with a road section curvature less than a preset curvature value from a road section set as the curved road section comprises:

[0022] obtaining each target coordinate point of a target road section in the road section set;

[0023] fitting each target coordinate point to obtain a plurality of target arcs corresponding to the target road section.

[0024] calculate a curvature circle corresponding to the plurality of target arcs, and a curvature radius corresponding to the curvature circle;

[0025] calculate a curvature value based on the curvature radius;

[0026] take a target road segment with a curvature value less than a preset curvature value as a curved road segment.

[0027] According to yet another aspect of the embodiments of the present application, a blind area determination device is provided, comprising:

[0028] The acquisition module is configured to select a road segment with a curvature less than a preset curvature value from a set of road segments as a curved road segment;

[0029] The selection module is configured to acquire driving data of a plurality of vehicles driving on the curved road segment;

[0030] The determination module is configured to determine a curve score of the curved road segment based on the driving data corresponding to the curved road segment and road segment attribute data;

[0031] The determination module is configured to determine the curved road segment as a curve blind when the curve score is greater than a preset curve score.

[0032] According to yet another aspect of the embodiments of the present application, an electronic device is provided, comprising:

[0033] a memory configured to store executable instructions; and

[0034] a processor configured to execute the executable instructions with the memory to complete the operations of any of the above methods.

[0035] According to yet another aspect of the embodiments of the present application, a computer readable storage medium is provided for storing computer readable instructions, which are executed to perform the operations of any of the above methods.

[0036] According to still another aspect of the embodiments of the present application, a computer program product is provided, which includes instructions that, when executed on a computer, cause the computer to perform the operations of any of the above-described methods. By applying the technical solutions of the embodiments of the present application, a set of curved road segments with relatively low curvature can be first selected from a set of curved road segments of the whole road section, and based on the driving data of a plurality of vehicles on each curved road segment and the attribute data of the corresponding curved road segment, the curve score of each vehicle on the corresponding curved road segment is determined. The curve score of the vehicle on any curved road segment can represent the possibility that the curved road segment has a blind area. Based on this, when the curve score is greater than a preset curve score, the curved road segment can be determined as a curve blind area. In this way, for those curved road segments with relatively low curvature, the curved road segments with visual blind area can be effectively screened out.

[0037] The above description is only a summary of the technical solutions of the present application. In order to enable one skilled in the art to better understand the technical means of the present application, the content of the description can be implemented, and in order to enable the above and other effects, features and advantages of the present application to be more apparent and easy to understand, the following specific embodiments of the present application are described. BRIEF DESCRIPTION OF DRAWINGS

[0038] The accompanying drawings, which form a part of the specification, illustrate embodiments of the present application and, together with the description, serve to explain the principles of the present application.

[0039] Referring to the drawings, the present application can be more clearly understood in conjunction with the following detailed description, in which:

[0040] Figure 1 A schematic diagram of a curved road segment is shown according to an embodiment of the present application;

[0041] Figure 2 A schematic diagram of a curved road segment with relatively low curvature is shown according to an embodiment of the present application;

[0042] Figure 3 A schematic diagram of another curved road segment with relatively low curvature is shown according to an embodiment of the present application;

[0043] Figure 4 A schematic diagram of a blind area determination method is shown according to an embodiment of the present application;

[0044] Figure 5 A data flow diagram for calculating the curve score of a curved road segment is shown according to an embodiment of the present application;

[0045] Figure 6 A schematic diagram of a scenario for calculating the curvature of a curved road segment using a circular arc fitting algorithm is shown according to an embodiment of the present application;

[0046] Figure 7 Fig. 1 shows a structural schematic diagram of a blind area determination device according to an embodiment of the present application;

[0047] Figure 8 Fig. 2 shows a structural schematic diagram of an electronic device according to an embodiment of the present application;

[0048] Figure 9 Fig. 3 shows a schematic diagram of a computer readable storage medium according to an embodiment of the present application. DETAILED DESCRIPTION

[0049] The embodiments of the technical solutions of the present application will be described in detail below with reference to the drawings. The following embodiments are only used to more clearly illustrate the technical solutions of the present application, and therefore only serve as examples, and cannot limit the protection scope of the present application.

[0050] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which the present application belongs; the terms used herein are only used to describe the effects of specific embodiments, and are not intended to limit the present application; the terms "include" and "have" and any variations thereof in the specification and claims of the present application and the above description of drawings are intended to cover non-exclusive inclusion.

[0051] In the description of the embodiments of the present application, the technical terms "first", "second", etc. are only used to distinguish different objects, and cannot be understood as indicating or implying relative importance or implicitly indicating the number, specific order or primary and secondary relationship of the indicated technical features. In the description of the embodiments of the present application, the meaning of "a plurality of" is two or more, unless otherwise explicitly and specifically limited.

[0052] In this paper, the reference to "embodiments" means that the specific features, structures or properties described in conjunction with the embodiments can be included in at least one embodiment of the present application. The appearance of this phrase in various places in the specification does not necessarily mean the same embodiment, nor is it an independent or alternative embodiment to other embodiments. The skilled person in the art explicitly and implicitly understands that the embodiments described herein can be combined with other embodiments.

[0053] In the description of the embodiments of the present application, the term "and / or" is only a description of the association relationship between the associated objects, which means that there can be three relationships, for example, A and / or B, which can represent the three cases of A alone, A and B together, and B alone. In addition, the character " / " in this paper generally represents a "or" relationship between the associated objects before and after.

[0054] The following will be described in detail Figures 1-9A blind area determination method according to an example embodiment of the present application will be described. It should be noted that the following application scenarios are only shown for facilitating understanding of the spirit and principles of the embodiments of the present application, and the embodiments of the present application are not limited in this respect. On the contrary, the embodiments of the present application can be applied to any applicable scenario.

[0055] The electronic device disclosed by the embodiments of the present application can be one or more computing devices, or a server or a server cluster composed of multiple servers.

[0056] Since the driving scenarios faced by the driving user are usually complex, for example, the user often drives in mountainous, hilly and other terrain scenarios. It can be understood that these complex terrain often have mountainous bends, and for these bend road sections, there is usually a large bend blind area.

[0057] In the related art, the blind area is often determined by the curvature of the bend, specifically, the bend road section with large curvature is determined as the blind area of the bend. For example, Figure 1 A schematic diagram of a bend road section is shown according to an embodiment of the present application. As Figure 1 shown, the bend road section includes a plurality of bends with large curvature. However, in real bend scenarios, there are often bend road sections with small curvature but still have a blind area of view, and the related art cannot determine whether there is a blind area in this scenario. For example, Figure 2 and Figure 3 shown, are some bend road sections with relatively low curvature in the related art. Among them, Figure 2 is a bend road section with relatively low curvature but no blind area of view, and Figure 3 is a bend road section with relatively low curvature but with a blind area of view (because of the obstruction of the mountain). It can be understood that if the blind area is determined only based on the comparison of the curvature size, the blind area shown in Figure 3 cannot be determined.

[0058] Among them, the curvature refers to the degree of bending of a curve at a point; the greater the curvature, the greater the degree of bending of the curve at the point; the smaller the curvature, the smaller the degree of bending of the curve at the point.

[0059] Therefore, in order to solve the problem of low accuracy of determining the blind area in the related art, the embodiments of the present application provide a blind area determination method, device, electronic device, storage medium and program product.

[0060] Figure 4 A flowchart of a blind area determination method according to an embodiment of the present application is shown schematically. As Figure 4 shown, the method comprises:

[0061] S101, select a road segment with a road segment curvature less than a preset curvature value from the road segment set as a curved road segment.

[0062] In one way, the embodiment of the present application can select a road segment with relatively small curvature from the road segment set as a curved road segment. Wherein, relatively small curvature means less than a preset curvature value, which can be 8% or 6%, of course, not limited to this.

[0063] Wherein, the road segment set can be a set of road segments obtained by pre-mapping. The road segment curvature of the road segments in the road segment set is known or can be obtained by calculation, which is reasonable.

[0064] The road segment curvature is used to describe the degree of bending of the road segment. It can be understood that the greater the road segment curvature (absolute value), the higher the degree of bending (left or right bending) of the road segment.

[0065] As an example, the road segment curvature in the embodiment of the present application can be obtained by the following formula:

[0066] Road segment curvature = (2 * sin(A0)) / L; Wherein, A0 is the included angle of two adjacent tangent lines in the road segment, and L is the straight line distance between the two tangent lines in the curved road segment.

[0067] Specifically, step S101 can include the following steps:

[0068] Obtain each target coordinate point of a target road segment in the road segment set; fit each target coordinate point to obtain a plurality of target arcs corresponding to the target road segment; calculate a curvature circle corresponding to the plurality of target arcs and a curvature radius corresponding to the curvature circle; calculate a curvature value based on the curvature radius; and select a target road segment with a curvature value less than a preset curvature value as a curved road segment.

[0069] Wherein, the target arc corresponding to the target road segment can be obtained by the following way: Figure 6 For example, A, B, and C are three coordinate points in the target road segment, and the embodiment of the present application can use the arc fitting algorithm to fit the three coordinate points to obtain the fitted target arc.

[0070] In one way, the embodiment of the present application can determine the center K of the arc and calculate the approximate curvature radius value of the position of each coordinate point of the curved road segment. Specifically:

[0071] Calculate the curvature radius a between K and coordinate point A, the curvature radius b between K and coordinate point B, and the curvature radius c between K and coordinate point C, respectively. Wherein, K is the center of the arc.

[0072] It can be understood that, since the greater the turning amplitude of a curved road segment is, the greater the curvature is, and thus the smaller the corresponding coordinate point radius of curvature is. Therefore, the curvature of the target road segment can be calculated by the radius of curvature values among a, b and c, and when the curvature is less than the preset curvature value, the target road segment is determined as a curved road segment.

[0073] In another way, the curved road segment set in the embodiment of the present application can also be a curved road segment with relatively small curvature and small turning angle (i.e. the curvature is less than the preset curvature value, and the road segment turning angle is less than the preset turning value).

[0074] The road segment turning angle refers to the included angle between the forward direction of the road and the new direction after turning at a road segment. This angle can be used to describe the degree of turning, which is an important reference index for drivers, pedestrians and road designers.

[0075] In this way, by the above method, the curved road segment can be selected from a large number of roads based on the road segment curvature, reducing the data processing amount of subsequent determination of whether the road segment has a blind area.

[0076] S102, obtaining driving data of a plurality of vehicles driving on the curved road segment.

[0077] Each curved road segment in the embodiment of the present application is associated with one or more corresponding driving data. Each driving data can include data generated by one or more driving user historical periods when driving on the curved road segment.

[0078] As an example, the driving data is used to represent the driving state of the vehicle driving on the corresponding curved road segment. The driving data can include one or more of the following contents: driving speed, deceleration amplitude, average driving speed and driving time length. Specifically, in one way, the driving data can include driving speed and deceleration amplitude; in another way, the driving data can include driving speed or deceleration amplitude; in still another way, the driving data can include average driving speed and driving time length.

[0079] The driving data can be driving data generated by driving users of different vehicle types when driving on the curved road segment in the historical period. The vehicle types can include large trucks, large buses, cars, non-motor vehicles, etc.

[0080] Of course, the driving data can be driving data generated by driving users of different genders when driving on the curved road segment in the historical period.

[0081] S103, determining the curved road score of the curved road segment based on the driving data corresponding to the curved road segment and the road segment attribute data.

[0082] The embodiment of the present application can jointly determine whether the curved road section has a blind area based on driving data generated by each vehicle when driving on each curved road section in a historical period and road section attribute data for representing a traffic state of the corresponding curved road section.

[0083] The road section attribute data can include curvature of the curved road section, road section turning angle, road section width, road section paving degree, road section grade, road section traffic volume, etc. The traffic state of the road section is used to reflect the number of traffic vehicles that can be accommodated by the road section in a unit of time.

[0084] It can be understood that the curvature of the road section, the road section turning angle, the road section width, the road section paving degree, the road section grade, and the road section traffic volume all determine the number of traffic vehicles that can be accommodated by the road section in a unit of time.

[0085] In one way, the traffic state of the road section also affects the driving state of the driving user. Therefore, the embodiment of the present application can combine the driving data and the road section attribute data to jointly determine the driving state of the curved road section from the set of curved road sections, and determine the curved road score of each curved road based thereon.

[0086] It can be understood that, for example, when the road section width is too narrow, the driving speed will be reduced even if the curved road section has no visual blind area. Or, for example, when the road section paving degree is too rough, the driving speed will be reduced even if the curved road section has no visual blind area. Or, for example, the road section width and the paving degree are within a normal range, and the driving speed of the driving user should be within an interval in the normal range, but the actual driving speed of the driving user is much lower than the interval, so the curved road section is likely to have a visual blind area.

[0087] Therefore, as shown in Figure 5 The embodiment of the present application introduces road section attribute data and driving data, wherein the road section attribute data includes one or more of the following: curvature value, road section turning angle, road section width, road section paving degree, road section grade, and road section traffic volume. The driving data includes one or more of the following: driving speed and deceleration amplitude; the curved road sections are uniformly scored, and the curved road section with a higher score is determined as a curved road section with a blind area.

[0088] It can be understood that the skilled in the art can construct a preset mapping table recording the mapping relationship between preset driving data, preset road section attribute data and preset curve score. For example, the road section width and the paving degree and other attributes are in a normal range, and the driving speed of the driving user in the normal range should be in an interval, but the actual driving speed of the driving user is far below the interval, so the curve road section is likely to have a blind area, and the curve is assigned a higher curve score in the preset mapping table.

[0089] The curve score of the embodiment of the present application is used to reflect the possibility of the curve road section having a blind area. As an example, when the embodiment of the present application detects that the driving state of the vehicle at a certain moment is greater than that at the previous moment, or detects that the vehicle travels at a very low speed in the curve road section for a certain period of time, it is determined that the curve road section has a high possibility of having a blind area (because when there are standing objects such as mountains and lamp poles on both sides of a curve road section, the driving user will feel a blind area, and when driving in the blind area, the driving user will likely slow down or drive at a very low speed), so the corresponding curve score is high.

[0090] S104, when the curve score is greater than the preset curve score, determining that the curve road section is a curve blind area.

[0091] In one way, if the curve score of a curve road section is high, the embodiment of the present application can determine that it has a blind area.

[0092] In summary, the technical solution of the embodiment of the present application can first select a curve road section set with low curvature from the whole road curve set, and determine the curve score of each vehicle in the corresponding curve road section based on the driving data of multiple vehicles in each curve road section and the attribute data of the corresponding curve road section. Since the driving data of any vehicle in any curve road section can represent the driving state of the vehicle in the curve road section, and the attribute data of the curve road section can represent the traffic state of the vehicle in the curve road section, the curve scores of multiple vehicles in the curve road section can reflect the state of each vehicle driving in the curve road section, thereby representing the possibility of the curve road section having a blind area. Based on this, in the present application, if the curve score is greater than the preset curve score, it means that the curve road section has a high possibility of having a blind area, so the curve road section can be determined as a curve blind area. In this way, for those curve road sections with low curvature, the curve road sections with a blind area can be effectively screened out.

[0093] The above description of each embodiment tends to emphasize the differences between each embodiment, and the same or similar parts can be referred to each other, and for brevity, the details are not described herein.

[0094] By applying the technical solutions of the embodiments of the present application, the problem of inaccurate confirmation of blind area of a curved road section caused by the fact that in the related art, the task platform can only determine whether a road section with small curvature exists a visual blind area through manual identification when screening the road section.

[0095] Of course, the driving habits of each driving user in the blind area road section can also be indirectly determined, and different blind area reminding strategies can be formulated for different driving users.

[0096] Optionally, in the process of detecting that when the curved road score is greater than the preset curved road score, the curved road section is determined as a blind area of a curved road, the embodiments of the present application can include the following steps:

[0097] Obtaining a road section picture corresponding to the curved road section; the road section picture includes a picture taken when a vehicle travels on the curved road section, and / or a satellite picture of the curved road section collected by a satellite;

[0098] Performing occlusion object identification on the road section picture and calculating an occlusion rate;

[0099] When the curved road score is greater than the preset curved road score and the occlusion rate is greater than a preset occlusion rate, the curved road section is determined as a blind area of a curved road.

[0100] It can be understood that, in order to further determine whether the curved road section exists a blind area, a road section picture corresponding to the curved road section can also be obtained. Then, based on the road section picture, an occlusion object is identified and an occlusion rate is calculated. When the occlusion rate of the curved road section is large, the possibility of existence of a visual blind area will increase accordingly. When the occlusion rate is greater than a preset occlusion rate and the curved road score is greater than a preset curved road score, it can be determined that the curved road section exists a blind area. In this implementation manner, the existence of a blind area in the curved road section is determined in combination with multiple dimensions, so that the determination result is more accurate.

[0101] After obtaining the road section picture for representing whether an obstacle exists in the candidate curved road section, the embodiments of the present application can also use a preset image detection model to identify the obstacle feature in the road section picture. And based on the identified result, the occlusion rate in each curved road section is calculated. In one way, the larger the obstacle feature, the higher the occlusion rate.

[0102] In this way, for the curved road section with a high occlusion rate, it can be relatively accurately determined whether the curved road section exists a blind area road section.

[0103] Among them, the present application does not make specific limitation on the image detection model. For example, it can be a convolutional neural network (Convo lut i ona l Neura l Networks, CNN).

[0104] The convolutional neural network is a kind of feedforward neural network (FNN) containing convolution calculation and having a deep structure, and is one of representative algorithms of deep learning. The convolutional neural network has representation learning capability, and can perform translation-invariant classification on input information according to a hierarchical structure. Due to the powerful feature representation capability of the CNN (convolutional neural network) for images, the CNN has achieved remarkable results in the fields of image classification, target detection, semantic segmentation and the like.

[0105] Further, the application can use a CNN neural network model to identify roadside image information on both sides of a curved road section in a road section picture, and then perform feature recognition on the roadside image information to determine whether there is an obstacle in each curved road section and the size of the obstacle.

[0106] As an example, the embodiment of the application can input the roadside image information into a preset convolutional neural network model, and take the output of the last fully connected layer (FC) of the convolutional neural network model as the recognition result of the obstacle feature data corresponding to the roadside image information.

[0107] As an example, the road section picture can be a photo taken by a car during driving. In the embodiment of the application, various obstructions on both sides of a curved road can be identified through the road section picture, and the visual field condition of the curved road section is determined.

[0108] As another example, the embodiment of the application can use a high-definition satellite picture to identify large obstructions such as mountains, trees and buildings on both sides of a curved road section in the satellite picture, and determine the visual field condition of the curved road section.

[0109] By applying the technical solution of the embodiment of the application, a curved road section set with a lower curvature value is selected from a whole road section curved road set, and then the curved road score of each vehicle in the corresponding curved road section can be determined based on the driving data of a plurality of vehicles in the curved road section and the attribute data of the corresponding curved road section. Since the curved road score of the vehicle in any curved road section can represent the possibility of existence of a blind road section in the curved road section, based on this, when the curved road score is greater than a preset curved road score, the curved road section can be determined as a curved road blind area. In this way, for those curved road sections with a lower curvature, the curved road sections with a visual field blind area can be effectively screened out.

[0110] The technical solution of the present application, on the one hand, calculates the curve score of a plurality of vehicles on the curved road section with a low curvature value, and the curve score can represent the possibility of the existence of a blind area section on the curved road section, so that the curved road section is determined as a curve blind area when the curve score is greater than a preset curve score, thereby providing an effective screening mechanism for determining whether the curved road section with a low curvature value has a blind area section. On the other hand, the technical solution of speculating whether each curved road section has a visual blind area based on the driving data of the driving user can also indirectly determine the driving habits of each driving user in the blind area section, and then different blind area reminding strategies can be formulated for different users.

[0111] The above description of each embodiment tends to emphasize the differences between each embodiment, and the same or similar parts can be referred to each other, and will not be described herein for the sake of brevity.

[0112] Optionally, in another embodiment of the present application, as shown in Figure 7 The present application also provides a blind area determination device. It includes:

[0113] The acquisition module 201 is configured to select a road section with a curvature less than a preset curvature value from a road section set as a curved road section.

[0114] The selection module 202 is configured to acquire driving data of a plurality of vehicles driving on the curved road section.

[0115] The determination module 203 is configured to determine a curve score of the curved road section based on the driving data corresponding to the curved road section and the road section attribute data.

[0116] The determination module 203 is configured to determine the curved road section as a curve blind area when the curve score is greater than a preset curve score.

[0117] By applying the technical solution of the present application, the curved road section set with a low curvature value can be first selected from the whole curved road section set, and based on the driving data of the vehicles on each curved road section, it is determined whether the driving trajectory of each vehicle on the curved road section conforms to the trajectory generated by the driving user driving in the blind area section, and it is determined whether each curved road section with a low curvature value has a blind area section.

[0118] In another embodiment of the present application, the selection module 202 is configured to:

[0119] Obtain a road section picture corresponding to the curved road section; the road section picture includes a picture taken by a vehicle driving on the curved road section, and / or a satellite picture of the curved road section collected by a satellite;

[0120] performing occlusion identification on the road segment picture and calculating an occlusion rate;

[0121] When the curve score is greater than a preset curve score and the occlusion rate is greater than a preset occlusion rate, the curved road segment is determined as a curve blind area.

[0122] In another embodiment of the present application, the selecting module 202 is configured to:

[0123] The driving data includes one or more of the following: driving speed and deceleration amplitude; and the road segment attribute data includes one or more of the following: curvature value, road segment turning angle, road segment width, road segment paving degree, road segment grade, and road segment traffic volume.

[0124] In another embodiment of the present application, the selecting module 202 is configured to:

[0125] The driving data and the road segment attribute data are matched with a preset mapping table to obtain a curve score of the curved road segment; the preset mapping table records a mapping relationship among preset driving data, preset road segment attribute data, and a preset curve score.

[0126] In another embodiment of the present application, the selecting module 202 is configured to:

[0127] The road segment with a road segment curvature less than a preset curvature value and a road segment turning angle less than a preset turning angle is selected from the road segment set as the curved road segment.

[0128] In another embodiment of the present application, the selecting module 202 is configured to:

[0129] Each target coordinate point of a target road segment in the road segment set is obtained;

[0130] Each target coordinate point is fitted to obtain a plurality of target arcs corresponding to the target road segment;

[0131] A curvature circle corresponding to the plurality of target arcs is calculated, and a curvature radius corresponding to the curvature circle is calculated;

[0132] The curvature value is calculated based on the curvature radius;

[0133] The target road segment with a curvature value less than a preset curvature value is taken as the curved road segment.

[0134] The above description of each embodiment tends to emphasize the differences between each embodiment, and the same or similar parts can be referred to each other, which will not be described herein for brevity.

[0135] The present application also provides an electronic device for executing the above blind area determination method. Please refer to Figure 8This illustrates a schematic diagram of an electronic device provided by some embodiments of this application. For example... Figure 8 As shown, the electronic device 3 includes: a processor 300, a memory 301, a bus 302, and a communication interface 303. The processor 300, the communication interface 303, and the memory 301 are connected via the bus 302. The memory 301 stores a computer program that can run on the processor 300. When the processor 300 runs the computer program, it executes the blind zone determination method provided in any of the foregoing embodiments of this application.

[0136] The memory 301 may include high-speed random access memory (RAM) or non-volatile memory, such as at least one disk storage device. Communication between this device network element and at least one other network element is achieved through at least one communication interface 303 (which may be wired or wireless), such as the Internet, wide area network, local area network, metropolitan area network, etc.

[0137] Bus 302 can be an ISA bus, PC I bus, or EIA bus, etc. The bus can be divided into address bus, data bus, control bus, etc. Memory 301 is used to store programs. After receiving an execution instruction, processor 300 executes the program. The blind zone determination method disclosed in any of the foregoing embodiments of this application can be applied to processor 300, or implemented by processor 300.

[0138] The processor 300 may be an integrated circuit chip with signal processing capabilities. In implementation, each step of the above method can be completed through integrated logic circuits in the hardware of the processor 300 or through software instructions. The processor 300 may be a general-purpose processor, including a central processing unit (CPU), a network processor (NP), etc.; it may also be a digital signal processor (DSP), an application-specific integrated circuit (ASIC), an off-the-shelf programmable gate array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components. It can implement or execute the methods, steps, and logic block diagrams disclosed in the embodiments of this application.

[0139] By applying the technical solutions of the embodiments of the present application, firstly, a set of curved road sections with low curvature values is selected from the set of curved road sections of the whole section, and based on the driving data of multiple vehicles on each curved road section and the attribute data of the corresponding curved road section, the curve score of each vehicle on the corresponding curved road section is determined. Among them, the curve score of the vehicle on any curved road section can represent the possibility of the existence of a blind area section on the curved road section. Based on this, in the present application, when the curve score is greater than the preset curve score, the curved road section can be determined as a curve blind area. In this way, for those curved road sections with low curvature, the curved road sections with visual blind area can be effectively screened out.

[0140] The electronic device provided by the embodiments of the present application and the blind area determination method provided by the embodiments of the present application are based on the same inventive concept and have the same beneficial effects as the methods they adopt, run or implement.

[0141] The above description of each embodiment tends to emphasize the differences between each embodiment, and the same or similar parts can be referred to each other. For the sake of brevity, the same or similar parts will not be described here.

[0142] The present application also provides a computer readable storage medium corresponding to the blind area determination method provided by the preceding embodiments. Please refer to Figure 9 The computer readable storage medium shown is an optical disc 40, and a computer program (i.e. program product) is stored on the optical disc 40. When the computer program is run by a processor, the blind area determination method provided by any of the preceding embodiments will be executed.

[0143] It should be noted that examples of the computer readable storage medium can also include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other optical, magnetic storage medium, which will not be described one by one here.

[0144] The computer readable storage medium provided by the above embodiments of the present application and the blind area determination method provided by the embodiments of the present application are based on the same inventive concept and have the same beneficial effects as the methods adopted, run or implemented by the application programs stored therein.

[0145] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present application, rather than limit them. Although the present application has been described in detail with reference to the foregoing embodiments, it should be understood by those skilled in the art that the technical solutions recorded in the foregoing embodiments can be modified, or some or all of the technical features can be replaced equivalently. Such modifications or replacements do not change the essence of the corresponding technical solutions, which should be covered in the scope of the claims and the specification of the present application. In particular, the technical features mentioned in each embodiment can be combined in any manner as long as there is no structural conflict. The present application is not limited to the specific embodiments disclosed herein, but includes all technical solutions falling within the scope of the claims.

Claims

1. A method of blind area determination, characterized in that, The method comprises the following steps: selecting a road segment with a curvature less than a preset curvature value from a set of road segments as a curved road segment; obtaining driving data of a plurality of vehicles driving on the curved road segment; determining a curve score of the curved road segment based on the driving data corresponding to the curved road segment and road segment attribute data, wherein the road segment attribute data comprises one or more of the following: curvature value, road segment turning angle, road segment width, road segment paving degree, road segment grade and road segment traffic volume; and the driving data comprises one or more of the following: driving speed and deceleration amplitude; when the curve score is greater than a preset curve score, determining that the curved road segment is a curve blind area.

2. The method of claim 1, wherein, The step of determining that the curved road segment is a curve blind area when the curve score is greater than a preset curve score comprises: obtaining a road segment picture corresponding to the curved road segment; the road segment picture comprises a picture taken when a vehicle drives on the curved road segment, and / or a satellite picture of the curved road segment collected by a satellite; performing occlusion identification on the road segment picture and calculating an occlusion rate; when the curve score is greater than a preset curve score and the occlusion rate is greater than a preset occlusion rate, determining that the curved road segment is a curve blind area.

3. The method according to claim 1 or 2, characterized in that, The step of determining a curve score of the curved road segment based on the driving data corresponding to the curved road segment and road segment attribute data comprises: matching the driving data and the road segment attribute data with a preset mapping table to obtain the curve score of the curved road segment; the preset mapping table records the mapping relationship between preset driving data, preset road segment attribute data and a preset curve score.

4. The method of claim 1, wherein, The step of selecting a road segment with a curvature less than a preset curvature value from a set of road segments as a curved road segment comprises: selecting a road segment with a curvature less than a preset curvature value and a road segment turning angle less than a preset turning angle from a set of road segments as a curved road segment.

5. The method of claim 1, wherein, The step of selecting a road segment with a curvature less than a preset curvature value from a set of road segments as a curved road segment comprises: obtaining each target coordinate point of a target road segment in the set of road segments; fitting each target coordinate point to obtain a plurality of target arcs corresponding to the target road segment; calculating a curvature circle corresponding to the plurality of target arcs and a curvature radius corresponding to the curvature circle; calculating a curvature value based on the curvature radius; selecting a target road segment with a curvature value less than a preset curvature value as a curved road segment.

6. A blind area determination apparatus characterized by comprising: The method comprises the following steps: a selection module configured to select a road segment with a curvature less than a preset curvature value from a set of road segments as a curved road segment; an obtaining module configured to obtain driving data of a plurality of vehicles driving on the curved road segment; a determination module configured to determine a curve score of the curved road segment based on the driving data corresponding to the curved road segment and road segment attribute data, wherein the road segment attribute data comprises one or more of the following: curvature value, road segment turning angle, road segment width, road segment paving degree, road segment grade and road segment traffic volume; and the driving data comprises one or more of the following: driving speed and deceleration amplitude; the determination module is configured to determine that the curved road segment is a curve blind area when the curve score is greater than a preset curve score.

7. An electronic device, comprising: The method comprises the following steps: a memory for storing executable instructions; and a processor for executing the executable instructions to perform the operations of the method of any of claims 1-5.

8. A computer-readable storage medium having stored thereon a computer program, characterized in that, The program is executed by a processor to implement the method of any of claims 1-5.

9. A computer program product, characterised in that, instructions which, when run on a computer, cause the computer to perform the method of any of claims 1-5.

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