Blind zone determination method and device, electronic equipment, storage medium and program product

Abstract

The invention discloses a blind area determination method and device, electronic equipment, a storage medium and a program product. The method comprises the steps that a road section with the curvature smaller than a preset curvature value can be selected from a road section set to serve as a curve road section; acquiring driving data of a plurality of vehicles driving on the curve road section; determining a curve score of the curve road section based on the driving data corresponding to the curve road section and the road section attribute data; and when the curve score is greater than a preset curve score, determining that the curve road section is a curve blind area. Therefore, for each curve road section with relatively low curvature, the curve score of the curve road section can be determined based on the driving data and the road section attribute data corresponding to the curve road section, and the curve score represents the possibility that a blind area road section exists in the corresponding curve road section; and determining the curve road section with the curve score greater than the preset curve score as the curve blind area, thereby effectively screening the curve road section with the view blind area in the curve road sections with low road curvature.

Description

Technical Field

[0001] The present application relates to the field of safe driving technology, and in particular to a blind spot determination method, device, electronic device, storage medium and program product. Background Art

[0002] The driving safety of automobiles is increasingly being paid attention to by task platforms of automobile companies. When a car is driving on a curved road, the driver often has blind spots. In particular, when the two sides of the curve are often accompanied by extreme terrain such as valleys and riverbeds, it will bring great challenges to the driver's safe driving. Therefore, it is particularly important for the task platform to remind the curved road sections with blind spots, and the first task of blind spot reminders is to first determine the curved road sections with blind spots from the entire road network.

[0003] In related technologies, blind spots are often determined by the curvature of a curve. Specifically, a curve section with a larger curvature is determined as a curve blind spot. However, in real curve scenes, there are often curve sections with smaller curvatures but still blind spots, and related technologies cannot determine whether there is a blind spot in such a scene. Curvature refers to the degree of curvature of a curve at a certain point; the larger the curvature, the greater the degree of curvature of the curve at that point; the smaller the curvature, the smaller the degree of curvature of the curve at that 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 of the invention

[0005] The embodiments of the present application provide a blind spot determination method, device, electronic device, storage medium and program product to determine a curved road section with a smaller curvature where a visual blind spot exists, thereby assisting the driver in driving based on the confirmed curved blind spot and improving driving safety.

[0006] This alleviates the problem in related technologies that the task platform can only determine the blind spots existing in curved road sections with larger curvatures.

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

[0008] Selecting a road section whose road section curvature is less than a preset curvature value from the road section set as a curved road section;

[0009] Acquiring driving data of multiple vehicles traveling on the curved road section;

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

[0011] When the curve score is greater than a preset curve score, the curve section is determined to be a curve blind spot.

[0012] Optionally, in another embodiment of the method of the present application, when the curve score is greater than a preset curve score, determining that the curve road section is a curve blind spot includes:

[0013] Obtaining a road section picture corresponding to the curved road section; the road section picture includes: a picture taken when the vehicle is traveling on the curved road section, and / or a satellite picture of the curved road section collected by a satellite;

[0014] Identify occlusion objects on the road section image and calculate the occlusion rate;

[0015] When the curve score is greater than a preset curve score and the occlusion rate is greater than a preset occlusion rate, it is determined that the curve road section is a curve blind spot.

[0016] Optionally, in another embodiment of the above method based on the present application, the driving data includes one or more of the following contents: driving speed and deceleration amplitude; the road section attribute data includes one or more of the following contents: 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 of the method of the present application, determining the curve score of the curve section based on the driving data and the road section attribute data corresponding to the curve section includes:

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

[0019] Optionally, in another embodiment of the method of the present application, the step of selecting a road section whose road section curvature is less than a preset curvature value from the road section set as a curved road section includes:

[0020] A road section whose road section curvature is less than a preset curvature value and whose road section turning angle is less than a preset turning angle is selected from the road section set as a curved road section.

[0021] Optionally, in another embodiment of the method of the present application, selecting a road section whose road section curvature is less than a preset curvature value from the road section set as a curved road section includes:

[0022] Get the target coordinate points of each target road segment in the road segment set;

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

[0024] Calculating the curvature circles corresponding to the multiple target arcs and the curvature radii corresponding to the curvature circles;

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

[0026] The target road section whose curvature value is less than the preset curvature value is regarded as the curved road section.

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

[0028] An acquisition module is configured to select a road section whose road curvature is less than a preset curvature value from the road section set as a curved road section;

[0029] A selection module is configured to obtain driving data of multiple vehicles traveling on the curved road section;

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

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

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

[0033] A memory for storing executable instructions; and

[0034] A processor is used to execute the executable instructions with the memory to complete the operation of any of the above methods.

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

[0036] According to another aspect of the embodiment of the present application, a computer program product is provided, including instructions, which, when executed on a computer, enable the computer to perform the operations of any of the above-described methods. By applying the technical solution of the embodiment of the present application, a set of curved road sections with lower curvature values ​​can be first selected from the set of curved roads of all sections, and the curve score of each vehicle on the corresponding curved road section can be determined based on the driving data of multiple vehicles on each curved road section and the attribute data of the corresponding curved road section. Among them, the curve score of a vehicle on any curved road section can characterize the possibility that there is a blind spot section on the curved road section. Based on this, in the present application, when the curve score is greater than the preset curve score, it can be determined that the curved road section is a curve blind spot. In this way, for those curved road sections with lower curvature, the curved road sections with blind spots in vision can be effectively screened out.

[0037] The above description is only an overview of the technical solution of the present application. In order to more clearly understand the technical means of the present application, it can be implemented in accordance with the contents of the specification. In order to make the above and other effects, features and advantages of the present application more obvious and easy to understand, the specific implementation methods of the present application are listed below. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] The accompanying drawings, which constitute 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] The present application can be more clearly understood from the following detailed description with reference to the accompanying drawings, in which:

[0040] Figure 1 A schematic diagram of a curved road section provided by an embodiment of the present application is shown;

[0041] Figure 2 A schematic diagram showing a curved road section with relatively low curvature provided by an embodiment of the present application is shown;

[0042] Figure 3 A schematic diagram showing another curved road section with relatively low curvature provided by an embodiment of the present application;

[0043] Figure 4 A schematic diagram showing a blind spot determination method provided by an embodiment of the present application is shown;

[0044] Figure 5 A schematic diagram showing the data flow for calculating a curve score of a curved road section provided in an embodiment of the present application is shown;

[0045] Figure 6 A schematic diagram of a scenario of calculating the curvature of a curved road section using an arc fitting algorithm provided by an embodiment of the present application is shown;

[0046] Figure 7 A schematic structural diagram of a blind spot determination device provided in an embodiment of the present application is shown;

[0047] Figure 8 A schematic diagram of the structure of an electronic device provided by an embodiment of the present application is shown;

[0048] Fig. 9 A schematic diagram of a computer-readable storage medium provided in an embodiment of the present application is shown. DETAILED DESCRIPTION

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

[0050] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by technicians in the technical field to which this application belongs; the terms used herein are only for describing the effects of specific embodiments and are not intended to limit this application; the terms "including" and "having" in the specification and claims of this application and the above-mentioned figure descriptions and any variations thereof are intended to cover non-exclusive inclusions.

[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 "multiple" is more than two, unless otherwise clearly and specifically defined.

[0052] Reference to "embodiments" herein means that a particular feature, structure, or characteristic described in conjunction with the embodiments may be included in at least one embodiment of the present application. The appearance of the phrase in various locations in the specification does not necessarily refer to the same embodiment, nor is it an independent or alternative embodiment that is mutually exclusive with other embodiments. It is explicitly and implicitly understood by those skilled in the art that the embodiments described herein may 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 of the associated objects, indicating that there may be three relationships. For example, A and / or B can represent: A exists alone, A and B exist at the same time, and B exists alone. In addition, the character " / " in this article generally indicates that the associated objects before and after are in an "or" relationship.

[0054] Combine the following Figure 1-Figure 9To describe the method for determining a blind spot according to an exemplary embodiment of the present application. It should be noted that the following application scenarios are only shown to facilitate understanding of the spirit and principle of the embodiments of the present application, and the embodiments of the present application are not limited in this regard. On the contrary, the embodiments of the present application can be applied to any applicable scenario.

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

[0056] Because the driving scenarios faced by driving users are usually complex, such as often driving in mountainous and hilly areas. Understandably, these complex terrains often have many mountain bends, and for these bends, there are usually large blind spots.

[0057] In the related art, the blind spot is often determined by the curvature of the curve. Specifically, the curve section with a large curvature is determined as the curve blind spot. Figure 1 FIG. 1 is a schematic diagram of a curved road section provided by an embodiment of the present application. Figure 1 As shown in FIG. 1 , the curved road section includes multiple curves with large curvatures. However, in real curved road scenes, there are often curved road sections with small curvatures but still with blind spots, and the relevant technology cannot determine whether there are blind spots in such scenes. Figure 2 and Figure 3 As shown in the figure, some curved road sections with relatively low curvature exist in the related art. Figure 2 For curved road sections with relatively low curvature but no blind spot vision, Figure 3 It is a curved road section with relatively low curvature but with blind spots (because of the obstruction of the mountain). It is understandable that if the blind spot is determined only by comparing the curvature size, it is impossible to determine Figure 3 Blind zone shown.

[0058] The curvature refers to the degree of curvature of a curve at a certain point. The larger the curvature, the greater the curvature of the curve at that point. The smaller the curvature, the smaller the curvature of the curve at that point.

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

[0060] Figure 4 The following schematically shows a flow chart of a blind spot determination method according to an embodiment of the present application. Figure 4 As shown, the method includes:

[0061] S101, selecting a road section whose road curvature is less than a preset curvature value from a road section set as a curved road section.

[0062] In one embodiment, a road section with relatively small curvature can be selected from the road section set as a curved road section, wherein relatively small curvature means less than a preset curvature value, and the preset curvature value can be 8% or 6%, but is not limited thereto.

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

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

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

[0066] Road section curvature = (2*s in(A0)) / L, where A0 is the angle between two adjacent tangents in the road section, and L is the straight-line distance between two tangents in the curved road section.

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

[0068] Acquire each target coordinate point of the target road section in the road section set; fit each target coordinate point to obtain multiple target arcs corresponding to the target road section; calculate the curvature circles corresponding to the multiple target arcs and the curvature radii corresponding to the curvature circles; calculate the curvature value based on the curvature radius; and treat the target road section whose curvature value is less than a preset curvature value as a curved road section.

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

[0070] In one way, the embodiment of the present application can determine the center K of the arc based on this, and calculate the approximate curvature radius value of each coordinate point of the curved road section. 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 I and coordinate point C respectively, where K is the center of the arc.

[0072] It is understandable that, since the greater the turning amplitude of a curved road section, the greater its curvature, the smaller the curvature radius of its corresponding coordinate point. Therefore, the embodiment of the present application can calculate the curvature of the target road section by the curvature radius value between a, b, and c, and when the curvature is less than the preset curvature value, the target road section is determined as a curved road section.

[0073] In another embodiment, the set of curved road sections in the embodiment of the present application may also be curved road sections with relatively small curvature and small turning angle (that is, the curvature is less than a preset curvature value, and the section turning angle is less than a preset turning value).

[0074] The road section turning angle refers to the angle between the forward direction of the road and the new direction after the turn when the road turns at a certain section. This angle can be used to describe the sharpness of the turn and is an important reference indicator for drivers, pedestrians and road designers.

[0075] In this way, through the above method, curved road sections can be screened out from a large number of roads based on the road section curvature, thereby reducing the amount of data processing for subsequent determination of whether there is a blind spot on the road section.

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

[0077] Each curved road section in the embodiment of the present application is associated with one or more corresponding driving data, wherein each driving data may include data generated when one or more driving users drive on the curved road section during historical periods.

[0078] As an example, the driving data is used to characterize the driving state of the vehicle in the corresponding curved road section. The driving data may include one or more of the following: driving speed, deceleration amplitude, average driving speed, and driving duration. Specifically, in one manner, the driving data may include driving speed and deceleration amplitude; in another manner, the driving data may include driving speed or deceleration amplitude; in yet another manner, the driving data may include: average driving speed and driving duration.

[0079] The driving data may be driving data generated by drivers of different vehicle types when driving on the curved road section during a historical period. The vehicle types may include large and small trucks, large and small buses, cars, non-motor vehicles, and the like.

[0080] Of course, the driving data may be driving data generated by drivers of different genders when driving on the curved road section during a historical period.

[0081] S103: Determine a curve score of the curve section based on the driving data and the road section attribute data corresponding to the curve section.

[0082] The embodiment of the present application can jointly determine whether the curved road section has a blind spot based on the driving data generated by each vehicle when driving on each curved road section in a historical period, and the section attribute data used to characterize the traffic status of the corresponding curved road section.

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

[0084] It can be understood that the curvature of the road section, the turning angle of the road section, the width of the road section, the degree of paving of the road section, the grade of the road section, the traffic volume of the road section, etc. will determine the number of passing vehicles that the road section can withstand in a unit time.

[0085] In one way, since the traffic status of a road section also affects the driving status of the driver, the embodiment of the present application can combine the driving data with the road section attribute data to jointly determine the driving status of the curved road section from the curved road section set, and thereby determine the curve score of each curve.

[0086] It is understandable that, for example, when the width of a road section is too narrow, the speed will be reduced even if there is no blind spot on the curved road section. Or, for example, when the paving degree of a road section is too rough, the speed will be reduced even if there is no blind spot on the curved road section. Or, for example, when the attributes such as the width and paving degree of a road section are within the normal range, and the driving speed of the driver should be within a range within the normal range, but the actual driving speed of the driver is much lower than the range, then the curved road section is likely to have a blind spot.

[0087] Therefore, if Figure 5 As shown, 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 contents: 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 contents: driving speed and deceleration amplitude; each curved road section is scored uniformly, and the curved road section with a higher score is determined as a curved road section with a blind spot.

[0088] It is understandable that a technician can construct a preset mapping table, which records the mapping relationship between preset driving data, preset road section attribute data and preset curve scores. For example, if the road section width and paving degree and other attributes are within the normal range, and the driving speed of the driver should be within a range within the normal range, but the actual driving speed of the driver is much lower than the range, then the curved road section is likely to have a blind spot, and a relatively high curve score is assigned to the curved road in the preset mapping table.

[0089] The curve score of the embodiment of the present application is used to reflect the possibility of a blind spot section on the curve. As an example, when the embodiment of the present application detects that the speed of the vehicle at a certain moment is attenuated to a greater extent than that of the previous moment based on the vehicle's driving data and the road section attribute data, or detects that the vehicle is traveling on a curve at an extremely low speed during a certain period of time, it is determined that the possibility of a blind spot section on the curve is high (because when there are standing objects such as mountainous areas and lamp posts on both sides of a curved road section, causing the driver to feel a blind spot, the driver is likely to slow down or drive at an extremely low speed when driving on the road section located in the blind spot), which means that the corresponding curve score is high.

[0090] S104: When the curve score is greater than a preset curve score, the curve section is determined to be a curve blind spot.

[0091] In one approach, if a curved road section has a high curve score, then the embodiment of the present application can determine that it has a blind spot.

[0092] In summary, the technical solution of the embodiment of the present application is that a set of curved road sections with lower curvature values ​​can be first selected from the set of curved roads of all sections, and the curve score of each vehicle on the corresponding curved road section can be determined based on the driving data of multiple vehicles on each curved road section and the attribute data of the corresponding curved road section. Since the driving data of any vehicle on any curved road section can characterize the driving state of the vehicle on the curved road section, and the attribute data of the curved road section can characterize the traffic state of the vehicle on the curved road section, then the curve score of multiple vehicles on the curved road section can reflect the state of each vehicle traveling on the curved road section, thereby characterizing the possibility of the existence of a blind spot section on the curved road section. Based on this, in the present application, if the curve score is greater than the preset curve score, it means that the possibility of the existence of a blind spot section on the curved road section is high, and the curved road section can be determined to be a curved blind spot. In this way, for those curved road sections with lower curvature, the curved road sections with blind spots in vision can be effectively screened out.

[0093] The above description of various embodiments tends to emphasize the differences between the various embodiments. The same or similar aspects can be referenced to each other, and for the sake of brevity, they will not be repeated herein.

[0094] By applying the technical solution of the embodiments of the present application, it is possible to avoid the problem of inaccurate confirmation of blind spots in curves in the related technology, in which the task platform can only determine whether there are sections with blind spots of vision through manual identification when screening sections of curved roads with smaller curvatures.

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

[0096] Optionally, in the embodiment of the present application, when it is detected that the curve score is greater than a preset curve score, the process of determining that the curve section is a curve blind spot may include the following steps:

[0097] Obtaining a road section image corresponding to the curved road section; the road section image includes: an image taken when the vehicle is traveling on the curved road section, and / or a satellite image of the curved road section collected by a satellite;

[0098] Identify occluders in road section images and calculate the occlusion rate;

[0099] When the curve score is greater than a preset curve score and the occlusion rate is greater than a preset occlusion rate, the curve section is determined to be a curve blind spot.

[0100] It can be understood that in order to further determine whether there is a blind spot in the short circuit of the curve, a road section picture corresponding to the curve section can also be obtained. Then, based on the road section picture, the obstruction is identified and the occlusion rate is calculated. When the occlusion rate of the curve section is large, the possibility of its existence of a blind spot in the field of vision will increase accordingly. When the occlusion rate is greater than the preset occlusion rate and the curve score is greater than the preset curve score, it can be determined that there is a blind spot in the curve section. In this implementation method, multiple dimensions are combined to determine whether there is a blind spot in the curve section, so that the determination result is more accurate.

[0101] After obtaining a road segment image for characterizing whether there is an obstacle in a candidate curved road segment, the embodiment of the present application can also use a preset image detection model to identify obstacle features in the road segment image. Based on the recognition result obtained, the occlusion rate in each curved road segment is calculated. In one method, the larger the obstacle feature, the higher its occlusion rate.

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

[0103] The present application does not specifically limit the image detection model, for example, it may be a convolutional neural network (CNN).

[0104] Convolutional neural networks are a type of feedforward neural networks (FNN) that include convolutional calculations and have a deep structure. They are one of the representative algorithms of deep learning. Convolutional neural networks have the ability to represent learning and can classify input information in a translation-invariant manner according to its hierarchical structure. Thanks to the powerful feature representation ability of CNN (convolutional neural network) for images, it has achieved remarkable results in image classification, object detection, semantic segmentation and other fields.

[0105] Furthermore, the present 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 of obstacles on the roadside image information to determine whether there are obstacles in each curved road section and the size of the obstacles.

[0106] As an example, an embodiment of the present application can input the roadside image information into a preset convolutional neural network model, and use 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 picture taken by a car while driving. In the embodiment of the present application, various obstructions on both sides of the curve can be identified through the road section picture, and the visual field condition of the curve section can be determined accordingly.

[0108] As another example, an embodiment of the present application may utilize high-definition satellite images to identify large obstructions such as mountains, trees, and buildings on both sides of a curved road section in the satellite images, and thereby determine the field of view of the curved road section.

[0109] By applying the technical solution of the embodiment of the present application, after selecting a set of curved road sections with lower curvature values ​​from the set of curved roads of all sections, the curve score of each vehicle on the corresponding curved road section can be determined based on the driving data of multiple vehicles on each curved road section and the attribute data of the corresponding curved road section. Since the curve score of a vehicle on any curved road section can represent the possibility of the existence of a blind spot 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 to be a curved blind spot. In this way, for those curved road sections with lower curvature, the curved road sections with blind spots in vision can be effectively screened out.

[0110] The technical solution of the present application, on the one hand, for any selected curved road section with a lower curvature value, calculates the curve score of multiple vehicles on the curved road section, and the curve score can represent the possibility of the existence of a blind spot section on the curved road section, so that when the curve score is greater than the preset curve score, the curved road section is determined to be a curved blind spot, providing an effective screening mechanism for determining whether there is a blind spot section on a curved road section with a lower curvature. On the other hand, the technical solution of inferring whether there is a visual blind spot on each curved road section based on the driving data of the driving user can also indirectly determine the driving habits of each driving user on the blind spot section, and then different blind spot reminder strategies can be formulated for different users in a targeted manner.

[0111] The above description of various embodiments tends to emphasize the differences between the various embodiments. The same or similar aspects can be referenced to each other, and for the sake of brevity, they will not be repeated herein.

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

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

[0114] A selection module 202 is configured to obtain driving data of multiple vehicles traveling on the curved road section;

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

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

[0117] By applying the technical solution of the embodiment of the present application, a set of curved road sections with lower curvature values ​​can be first selected from the set of curved road sections of all sections, and based on the driving data of the vehicles on each curved road section, it can be determined whether the driving trajectory of each vehicle on the curved road section conforms to the trajectory generated by the driving user when driving in the blind spot section, and based on this, it can be determined whether there is a blind spot section on each curved road section with lower curvature values.

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

[0119] Obtaining a road section picture corresponding to the curved road section; the road section picture includes: a picture taken when the vehicle is traveling on the curved road section, and / or a satellite picture of the curved road section collected by a satellite;

[0120] Identify occlusion objects on the road section image and calculate the 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, it is determined that the curve road section is a curve blind spot.

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

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

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

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

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

[0127] A road section whose road section curvature is less than a preset curvature value and whose road section turning angle is less than a preset turning angle is selected from the road section set as a curved road section.

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

[0129] Get the target coordinate points of each target road segment in the road segment set;

[0130] Fitting each target coordinate point to obtain a plurality of target arcs corresponding to the target road section;

[0131] Calculating the curvature circles corresponding to the multiple target arcs and the curvature radii corresponding to the curvature circles;

[0132] Calculating a curvature value based on the curvature radius;

[0133] The target road section whose curvature value is less than the preset curvature value is regarded as the curved road section.

[0134] The above description of various embodiments tends to emphasize the differences between the various embodiments. The same or similar aspects can be referenced to each other, and for the sake of brevity, they will not be repeated herein.

[0135] The present application also provides an electronic device to perform the above blind spot determination method. Figure 8, which shows a schematic diagram of an electronic device provided by some embodiments of the present application. Figure 8 As shown, the electronic device 3 includes: a processor 300, a memory 301, a bus 302 and a communication interface 303, wherein 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 be run on the processor 300, and when the processor 300 runs the computer program, the blind spot determination method provided in any of the aforementioned embodiments of the present application is executed.

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

[0137] The bus 302 may be an ISA bus, a PCI bus, or an EISA bus, etc. The bus may be divided into an address bus, a data bus, a control bus, etc. The memory 301 is used to store a program, and the processor 300 executes the program after receiving an execution instruction. The blind spot determination method disclosed in any implementation of the embodiment of the present application may be applied to the processor 300, or implemented by the processor 300.

[0138] The processor 300 may be an integrated circuit chip with signal processing capabilities. In the implementation process, each step of the above method can be completed by the hardware integrated logic circuit in the processor 300 or the instructions in the form of software. The above processor 300 can be a general processor, including a processor (Central Processing Unit, referred to as CPU), a network processor (Network Processor, referred to as NP), etc.; it can also be a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field programmable gate array (FPGA) or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components. The disclosed methods, steps and logic block diagrams in the embodiments of the present application can be implemented or executed.

[0139] By applying the technical solution of the embodiment of the present application, firstly, a set of curved road sections with lower curvature values ​​is selected from the set of curved roads of all sections, 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 a vehicle on any curved road section can represent the possibility of the existence of a blind spot 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 to be a curved blind spot. In this way, for those curved road sections with lower curvature, the curved road sections with blind spots in vision can be effectively screened out.

[0140] The electronic device provided in the embodiment of the present application and the blind spot determination method provided in the embodiment of the present application are based on the same inventive concept and have the same beneficial effects as the methods adopted, operated or implemented therein.

[0141] The above description of various embodiments tends to emphasize the differences between the various embodiments. The same or similar aspects can be referenced to each other, and for the sake of brevity, they will not be repeated herein.

[0142] The present application also provides a computer-readable storage medium corresponding to the blind spot determination method provided in the above embodiment. Fig. 9 The computer-readable storage medium shown is a CD 40 on which a computer program (ie, a program product) is stored. When the computer program is run by a processor, the blind spot determination method provided by any of the aforementioned embodiments will be executed.

[0143] It should be noted that examples of the computer-readable storage medium may 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 or magnetic storage media, which are not listed here one by one.

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

[0145] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them; although the present application has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or replace some or all of the technical features therein by equivalents; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the scope of the technical solutions of the embodiments of the present application, and they should all be included in the scope of the claims and specification of the present application. In particular, as long as there is no structural conflict, the various technical features mentioned in the various embodiments can be combined in any way. The present application is not limited to the specific embodiments disclosed herein, but includes all technical solutions that fall within the scope of the claims.

Claims

1. A blind spot determination method, characterized in that: include: Selecting a road section whose road section curvature is less than a preset curvature value from the road section set as a curved road section; Acquiring driving data of multiple vehicles traveling on the curved road section; Determining a curve score of the curve section based on the driving data and the road section attribute data corresponding to the curve section; When the curve score is greater than a preset curve score, the curve section is determined to be a curve blind spot.

2. The method according to claim 1, characterized in that: When the curve score is greater than a preset curve score, determining that the curve section is a curve blind spot includes: Obtaining a road section picture corresponding to the curved road section; the road section picture includes: a picture taken when the vehicle is traveling on the curved road section, and / or a satellite picture of the curved road section collected by a satellite; Identify occlusion objects on the road section image and calculate the occlusion rate; When the curve score is greater than a preset curve score and the occlusion rate is greater than a preset occlusion rate, it is determined that the curve road section is a curve blind spot.

3. The method according to claim 1, characterized in that: The driving data includes one or more of the following contents: driving speed and deceleration amplitude; the road section attribute data includes one or more of the following contents: curvature value, road section turning angle, road section width, road section paving degree, road section grade and road section traffic volume.

4. The method according to any one of claims 1 to 3, characterized in that The determining of the curve score of the curve section based on the driving data and the road section attribute data corresponding to the curve section includes: The driving data and the road section attribute data are matched with a preset mapping table to obtain a curve score of the curved road section; the preset mapping table records: a mapping relationship between preset driving data, preset road section attribute data and preset curve score.

5. The method according to claim 1, characterized in that: The step of selecting a road section whose road section curvature is less than a preset curvature value from the road section set as a curved road section comprises: A road section whose road section curvature is less than a preset curvature value and whose road section turning angle is less than a preset turning angle is selected from the road section set as a curved road section.

6. The method according to claim 1, characterized in that: A road segment whose curvature is less than a preset curvature value is selected from the road segment set as a curved road segment, including: Get the target coordinate points of each target road segment in the road segment set; Fitting each target coordinate point to obtain a plurality of target arcs corresponding to the target road section; Calculating the curvature circles corresponding to the multiple target arcs and the curvature radii corresponding to the curvature circles; Calculating a curvature value based on the curvature radius; The target road section whose curvature value is less than the preset curvature value is regarded as the curved road section.

7. A blind spot determination device, characterized in that: include: An acquisition module is configured to select a road section whose road curvature is less than a preset curvature value from the road section set as a curved road section; A selection module is configured to obtain driving data of multiple vehicles traveling on the curved road section; a determination module configured to determine a curve score of the curve section based on the driving data and the road section attribute data corresponding to the curve section; The determination module is configured to determine that the curved road section is a blind curve when the curve score is greater than a preset curve score.

8. An electronic device, characterized in that: include: A memory for storing executable instructions; as well as, A processor is used to execute the executable instructions to complete the operation of any method described in claims 1-6.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: The program is executed by a processor to implement the method according to any one of claims 1 to 6.

10. A computer program product, characterized in that The method comprises instructions, which, when executed on a computer, cause the computer to execute the method according to any one of claims 1 to 6.

Images