Vehicle traveling risk detection method and system, device, and storage medium

Abstract

A vehicle traveling risk detection method and system, a device, and a storage medium. The vehicle traveling risk detection method comprises the following steps: a current traveling vehicle acquires detection area information of the current traveling vehicle by means of a configured information collection module, the detection area information comprising vehicle information in a detection area; determining a vehicle risk area in the detection area on the basis of the vehicle information in the detection area; acquiring current traveling vehicle information; and on the basis of the current traveling vehicle information and the vehicle risk area in the detection area, determining whether the current traveling vehicle has a traveling risk.

Description

Vehicle driving risk detection method, system, device and storage medium Technical Field

[0001] The present invention relates to the field of the Internet, and in particular to a vehicle driving risk detection method, system, device and storage medium. Background Art

[0002] As we all know, large trucks have significant blind spots and risk zones. Furthermore, their immense inertia during driving poses a constant threat and fear. If drivers can be promptly alerted when their vehicle enters a large vehicle's blind spot or risk zone, or even passively enters one, they can be instructed to steer clear or take other evasive actions, effectively avoiding risk areas.

[0003] It should be noted that the information disclosed in the above background technology section is only used to enhance the understanding of the background of the present invention, and therefore may include information that does not constitute prior art known to ordinary technicians in this field.

[0004] Summary of the Invention

[0005] In view of the problems in the prior art, the purpose of the present invention is to provide a vehicle driving risk detection method, system, device and storage medium, which can improve the safety of vehicle driving.

[0006] An embodiment of the present invention provides a vehicle driving risk detection method, comprising the following steps:

[0007] The currently traveling vehicle obtains the detection area information of the currently traveling vehicle through the configured information acquisition module, wherein the detection area information includes the vehicle information within the detection area;

[0008] Determining a vehicle risk area within the detection area based on vehicle information within the detection area;

[0009] Get the current vehicle information;

[0010] It is determined whether the currently traveling vehicle has a driving risk according to the currently traveling vehicle information and the vehicle risk area within the detection area.

[0011] According to some examples of the present invention, the currently traveling vehicle information includes the currently traveling vehicle position information, the currently traveling vehicle model information and the currently traveling vehicle driving information; and / or

[0012] The vehicle information within the detection area includes vehicle position information within the detection area, vehicle model information within the detection area, and vehicle driving information within the detection area.

[0013] According to some examples of the present invention, the step of determining risk area information of vehicles within the detection area based on the vehicle information within the detection area includes:

[0014] Obtaining a vehicle model database, wherein the vehicle model database is a data set of vehicle model information and corresponding risk area information;

[0015] The risk area of ​​vehicles in the detection area is obtained according to the vehicle information in the detection area and the vehicle model database.

[0016] According to some examples of the present invention,

[0017] The current vehicle information includes the current vehicle speed and / or vehicle age, and the step of obtaining the current vehicle information includes: obtaining a first sensitivity coefficient according to the current vehicle speed; and / or

[0018] The currently traveling vehicle information includes the driver information of the currently traveling vehicle, and the step of obtaining the currently traveling vehicle information includes: obtaining a second sensitivity coefficient according to the driver information of the currently traveling vehicle; and / or

[0019] The detection area information includes the detection area road condition information, and the step of obtaining the current driving vehicle information includes: obtaining a third sensitivity coefficient according to the current detection area road condition information; and / or

[0020] The detection area information includes weather information of the detection area, and the step of obtaining the current driving vehicle information includes: obtaining a fourth sensitivity coefficient according to the weather information of the detection area.

[0021] According to some examples of the present invention, before the step of determining whether the currently traveling vehicle has a driving risk based on the currently traveling vehicle information and the risk area, the step further includes:

[0022] The vehicle risk area within the detection area is updated according to one or more of the first sensitivity coefficient, the second sensitivity coefficient, the third sensitivity coefficient, and the fourth sensitivity coefficient.

[0023] According to some examples of the present invention, determining whether the currently traveling vehicle has a driving risk based on the currently traveling vehicle information and the vehicle risk area within the detection area includes the following steps:

[0024] Determining whether the current vehicle is in an updated vehicle risk area within the detection area based on the current vehicle and the vehicle information within the detection area;

[0025] If so, it is considered that the current vehicle is at risk.

[0026] According to some examples of the present invention, after the step of determining that the currently traveling vehicle has a driving risk, the method further includes the following steps:

[0027] Determine the driving risk type of the currently traveling vehicle;

[0028] A corresponding warning signal type is sent to the driver of the current driving vehicle according to the driving risk type.

[0029] According to some examples of the present invention, the vehicle risk area within the detection area includes a vehicle forward risk area within the detection area, a vehicle backward risk area within the detection area, and risk areas on both sides of the vehicle within the detection area;

[0030] The determining of the driving risk type of the currently traveling vehicle and sending a corresponding warning signal type to the driver of the currently traveling vehicle according to the driving risk type includes:

[0031] Get the distance between the current vehicle and the vehicle traveling in the same direction and lane within the detection area;

[0032] If the currently traveling vehicle is in front of a vehicle traveling in the same direction and lane within the detection area, the corresponding first warning signal type is sent to the driver of the currently traveling vehicle based on the ratio of the distance value to the length of the risk area ahead of the vehicle traveling in the same direction and lane within the detection area;

[0033] If the currently traveling vehicle is traveling behind a vehicle traveling in the same direction and lane within the detection area, the corresponding second warning signal type is sent to the driver of the currently traveling vehicle based on the ratio of the distance value to the length of the risk area behind the vehicle traveling in the same direction and lane within the detection area.

[0034] According to some examples of the present invention, the method further includes the following steps:

[0035] The information of the currently traveling vehicle, the information of the vehicles in the detection area, and the risk area of ​​the vehicles in the detection area are input into the animation modeling model to obtain a real-time risk position image of the currently traveling vehicle and the vehicles in the detection area.

[0036] An embodiment of the present invention further provides a vehicle driving risk detection system for implementing the vehicle driving risk detection method, the system comprising an information acquisition module, a first calculation module and a second calculation module;

[0037] The information acquisition module is provided on the currently traveling vehicle and is used to obtain the detection area information of the currently traveling vehicle, wherein the detection area information includes the vehicle information within the detection area;

[0038] The first calculation module is used to determine the vehicle risk area within the detection area according to the vehicle information within the detection area;

[0039] The information acquisition module is also used to obtain the current driving vehicle information;

[0040] The second calculation module is used to determine whether the current driving vehicle has a driving risk based on the current driving vehicle information and the vehicle risk area in the detection area.

[0041] According to some examples of the present invention, the information acquisition module includes a sensor unit and an information processing unit connected to the sensor unit;

[0042] The sensor unit includes a forward sensor group, a first lateral sensor group and a second lateral sensor group provided on both sides of the vehicle, and a rearward sensor group;

[0043] The forward sensor group includes one or more of a forward laser radar, a forward camera, a forward wide-angle camera, a forward binocular right camera, a forward binocular left camera, and a forward millimeter-wave radar; and / or

[0044] The first lateral sensor group includes a first lateral camera and / or a first lateral millimeter-wave radar; and / or

[0045] The second lateral sensor group includes a second lateral camera and / or a second lateral millimeter-wave radar; and / or

[0046] The rearward sensor group includes one or more of a rearward laser radar, a rearward camera, a rearward wide-angle camera, a rearward binocular right camera, a rearward binocular left camera and a rearward millimeter-wave radar.

[0047] An embodiment of the present invention further provides an electronic device, including:

[0048] processor;

[0049] a memory storing executable instructions for the processor;

[0050] Wherein, the processor is configured to execute the steps of the vehicle driving risk detection method by executing the executable instructions.

[0051] An embodiment of the present invention further provides a computer-readable storage medium for storing a program, wherein the program, when executed by a processor, implements the steps of the vehicle driving risk detection method.

[0052] The vehicle driving risk detection method of the present invention obtains the current driving vehicle information and detection area information through the information collection module of the driving vehicle itself, determines the risk of the current driving vehicle based on the above information, and reminds the driver accordingly, thereby improving the safety of vehicle driving. BRIEF DESCRIPTION OF THE DRAWINGS

[0053] The accompanying drawings are incorporated into and constitute a part of the specification, illustrate embodiments consistent with the present application, and together with the specification, are used to explain the principles of the present application. Other features, objects, and advantages of the present invention will become more apparent by reading the detailed description of the non-limiting embodiments with reference to the following drawings. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without inventive work.

[0054] FIG1 is a flow chart of a vehicle driving risk detection method according to an embodiment of the present invention;

[0055] FIG2 is a schematic diagram of a vehicle risk area within a detection area according to an embodiment of the present invention;

[0056] FIG3 is a schematic diagram of a vehicle risk area within an updated detection area according to an embodiment of the present invention;

[0057] FIG4 is a module diagram of a vehicle travel risk detection system according to an embodiment of the present invention;

[0058] FIG5 is a schematic structural diagram of an electronic device according to an embodiment of the present invention; and

[0059] FIG6 is a schematic diagram of the structure of a computer-readable storage medium according to an embodiment of the present invention. DETAILED DESCRIPTION

[0060] Example embodiments will now be described more fully with reference to the accompanying drawings. However, example embodiments can be embodied in many forms and should not be construed as limited to the examples set forth herein; rather, these embodiments are provided so that this disclosure will be thorough and complete and will fully convey the concepts of the example embodiments to those skilled in the art. The described features, structures, or characteristics may be combined in any suitable manner in one or more embodiments.

[0061] Throughout this specification, reference to terms such as "one embodiment," "some embodiments," "examples," "specific examples," or "some examples" means that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of this specification. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples. Furthermore, those skilled in the art may combine and integrate different embodiments or examples, and features of different embodiments or examples, described in this specification, unless otherwise mutually incompatible.

[0062] Throughout the specification, when it is said that a device is "connected" to another device, this includes not only the case of "direct connection" but also the case of "indirect connection" by placing other elements therebetween. Terms indicating relative spaces such as "front", "back", "upper", and "lower" may be used to more easily explain the relationship of one device relative to another device illustrated in the accompanying drawings. Such terms refer not only to the meaning indicated in the accompanying drawings, but also to other meanings or operations of the device in use. For example, if the device in the accompanying drawings is turned over, a device that was previously described as being "under" another device is now described as being "over" the other device. Therefore, the exemplary term "under" includes both above and below. The device can be rotated 90° or other angles, and the terms indicating relative spaces are interpreted accordingly.

[0063] Although the terms first, second, etc. are used herein to represent various elements in some instances, these elements should not be limited by these terms. These terms are only used to distinguish one element from another. For example, a first interface and a second interface, etc. are represented. Furthermore, as used herein, the singular forms "a," "an," and "the" are intended to also include the plural forms, unless the context indicates otherwise. It should be further understood that the terms "comprise" and "include" indicate the presence of the described features, steps, operations, elements, components, items, types, and / or groups, but do not exclude the presence, occurrence, or addition of one or more other features, steps, operations, elements, components, items, types, and / or groups. The terms "or" and "and / or" used herein are interpreted as inclusive, or mean any one or any combination. Therefore, "A, B, or C" or "A, B, and / or C" means "any of the following: A; B; C; A and B; A and C; B and C; A, B, and C." Exceptions to this definition only occur when the combination of elements, functions, steps, or operations is inherently mutually exclusive in some way.

[0064] Although not defined differently, all terms used herein, including technical and scientific terms, have the same meanings as those generally understood by those skilled in the art to which this specification belongs. Terms defined in commonly used dictionaries are supplementally interpreted as having meanings consistent with relevant technical literature and current knowledge, and unless otherwise defined, they should not be overly interpreted as ideal or highly formalized meanings.

[0065] In addition, the accompanying drawings are merely schematic illustrations of the present disclosure and are not necessarily drawn to scale. Identical reference numerals in the figures denote identical or similar parts, and thus repetitive descriptions thereof will be omitted. Some of the block diagrams shown in the accompanying drawings are functional entities that do not necessarily correspond to physically or logically separate entities. These functional entities may be implemented in software, in one or more hardware modules or integrated circuits, or in different networks and / or processor devices and / or microcontroller devices.

[0066] FIG1 is a flow chart of a vehicle driving risk detection method according to an embodiment of the present invention. Specifically, the vehicle driving risk detection method according to the present invention includes the following steps:

[0067] S100: The currently traveling vehicle obtains detection area information of the currently traveling vehicle through a configured information collection module. The detection area information includes information about vehicles within the detection area. It should be noted that the detection area is the area within which the information collection module configured for the currently traveling vehicle can collect data, encompassing a certain area in front of, behind, to the left of, and to the right of the currently traveling vehicle. The information collection module may include a sensor unit and an information processing unit. The sensor unit may include various sensors, broadly defined here to include cameras, lidars, millimeter-wave radars, corner sensors, and the like. For example, when the sensor unit in one embodiment includes a lidar or millimeter-wave radar, the detection range of the information collection module is determined by the detection limit of the lidar or millimeter-wave radar. Different currently traveling vehicles may be equipped with different sensors with different detection limits as needed to form a sensor unit that meets the vehicle's usage scenario. Since the vehicle information within the detection area refers to the information of vehicles within the area detectable by the currently traveling vehicle, the number of vehicles within the detection area is determined by the size of the detectable area and the road traffic conditions during driving. Therefore, the vehicle information within the detection area is not limited to information on just one vehicle; it may include information on multiple vehicles. The vehicle information within the detection area may include vehicle position information within the detection area, vehicle model information within the detection area, and vehicle driving information within the detection area.

[0068] S200: Determine the vehicle risk area within the detection area based on the vehicle information within the detection area; step S100 can be regarded as a real-time detection process. When there are no other moving vehicles in the detection area of ​​the current moving vehicle obtained by the information acquisition module, step S100 is repeated; when there is at least one moving vehicle in the detection area of ​​the current moving vehicle obtained by the information acquisition module, step S200 is executed. The information acquisition module M100 may include a sensor unit M110 and an information processing unit M120 connected to the sensor unit; in some embodiments, the sensor unit may include a forward sensor group, a first side sensor group and a second side sensor arranged on both sides of the vehicle, and a rearward sensor group; further, the forward sensor group includes one or more of a forward laser radar, a forward camera, a forward wide-angle camera, a forward binocular right camera, a forward binocular left camera, and a forward millimeter-wave radar; the first side sensor group includes a first side camera and / or a first side millimeter-wave radar; the second side sensor group includes a second side camera and / or a second side millimeter-wave radar; the rearward sensor group includes one or more of a rearward laser radar, a rearward camera, a rearward wide-angle camera, a rearward binocular right camera, a rearward binocular left camera, and a rearward millimeter-wave radar. The step of determining the risk area information of the vehicle in the detection area based on the vehicle information in the detection area in step S200 may include:

[0069] S210: Acquire a vehicle model database, which is a dataset of vehicle model information and corresponding risk area information;

[0070] S220: Obtain risk areas for vehicles within the detection area based on the vehicle information within the detection area and the vehicle model database.

[0071] For example, the vehicle information within the detection area in step S100 may include image information of vehicles within the detection area obtained through a forward-facing camera, two side cameras, or a rear-facing camera. The vehicle model within the detection area can be determined based on the image information. The vehicle model database is an empirical database. The risk area of ​​the vehicle within the detection area can be obtained by searching the vehicle model database based on the vehicle model information. In other embodiments, the forward-facing camera or the rear-facing camera may capture the license plate number of the vehicle in front of or behind the moving vehicle. The vehicle model information within the detection area is determined based on an existing database of license plate numbers. The method for determining the vehicle model information within the detection area is not limited to the method described above. In addition, the vehicle model database is not limited to a dataset of vehicle model information and its corresponding risk area information. It can be a dataset of other types of vehicle information and its corresponding risk area information.

[0072] Vehicles of different sizes have different blind spots / risk zones. For example, the blind spot in front of a typical truck is within a range of 2.5 meters. The surrounding areas in front of the truck are relatively dangerous areas, almost all of which are semi-blind spots or risk zones. The area within 10 meters in front of the truck's front can be considered a risk zone for large trucks. The blind spot on the right side of a truck is within a range of 1.5 meters to 2 meters, the blind spot on the left side of a truck is more than 2.5 meters, and the blind spot on the rear side of a truck is 4 meters. In the present invention, there is no distinction between blind spots and risk zones, and both are defined as risk areas. Figure 2 is a schematic diagram of the updated vehicle risk areas within the detection area of ​​one embodiment of the present invention. The vehicle risk areas within the detection area include the forward risk area I of the vehicle within the detection area, the rear risk area III of the vehicle within the detection area, and the risk areas II on both sides of the vehicle within the detection area. In the description of the subsequent embodiments, the vehicle information within the detection area may be based on the information of one vehicle as an example. Accordingly, the risk area may be the risk area of ​​that vehicle. However, in the actual use of the vehicle driving risk detection method, the risk area is the physical superposition of the risk areas of multiple vehicles within the detection area.

[0073] S300: Acquire the current vehicle information; the current vehicle information may include the current vehicle position information, the current vehicle model information, and the current vehicle driving information.

[0074] S400: Determine whether the currently traveling vehicle has a driving risk based on the currently traveling vehicle information and the vehicle risk area within the detection area.

[0075] Compared to existing technical solutions that utilize equipment in the surrounding environment of a moving vehicle, such as fixed roadside equipment, to detect the safety status of vehicles within a certain area and then send targeted warning messages to vehicles that may pose driving risks, the method of the present invention utilizes its own information collection module to actively identify risk areas for other vehicles, proactively avoiding dangerous situations in advance, thereby achieving active risk avoidance and staying away from risk areas. The following text updates the vehicle risk area obtained in step S200 based on information collected by the information collection module of the current moving vehicle, such as the current moving vehicle's location information, the current moving vehicle's model information, the current moving vehicle's driving information, the detection area's road condition information, and the detection area's weather information, thereby avoiding the vehicle itself in the risk area.

[0076] In some embodiments, the current driving vehicle information in step S300 includes the current driving vehicle speed and / or the age of the vehicle, and the step of obtaining the current driving vehicle information includes: obtaining a first sensitivity coefficient based on the current driving vehicle speed; and / or the current driving vehicle information includes the driver information of the current driving vehicle, and the step of obtaining the current driving vehicle information includes: obtaining a second sensitivity coefficient based on the driver information of the current driving vehicle; and / or the detection area information includes the road condition information of the detection area, and the step of obtaining the current driving vehicle information includes: obtaining a third sensitivity coefficient based on the road condition information of the detection area; and / or the detection area information includes the weather information of the detection area, and the step of obtaining the current driving vehicle information includes: obtaining a fourth sensitivity coefficient based on the weather information of the detection area. The step of obtaining the first sensitivity coefficient, the second sensitivity coefficient, the third sensitivity coefficient and / or the fourth sensitivity coefficient included in the step of obtaining the current driving vehicle information is defined as step S310.

[0077] Accordingly, before the step of determining whether the currently traveling vehicle has a driving risk according to the currently traveling vehicle information and the risk area in step S400, the method further includes:

[0078] S320: Update the vehicle risk area within the detection area according to one or more of the first sensitivity coefficient, the second sensitivity coefficient, the third sensitivity coefficient, and the fourth sensitivity coefficient.

[0079] The vehicle risk areas within the detection area obtained in step S200 or shown in FIG3 can be considered as blind spots / risk areas in the database entered when various vehicles leave the factory. Steps S310 and S320 can be considered as further dynamic adjustment of the vehicle risk areas within the detection area based on the current driving vehicle information. The reason for this is:

[0080] The safe distance between vehicles depends on the driver-related parameters of the currently traveling vehicle, the driving parameters of the currently traveling vehicle, and the environmental parameters. The driver-related parameters of the currently traveling vehicle include the age, length of service, personality, fatigue, technical proficiency, etc. of the driver of the currently traveling vehicle. The currently traveling vehicle parameters include the model, condition, and speed of the currently traveling vehicle. The environmental parameters include the detection area or the road conditions of the current driving area (number of lanes, whether it is a bend), weather information (rain, snow or fog, visibility, etc.). The above-mentioned currently traveling vehicle information can be obtained from the currently traveling vehicle system and the configured information acquisition module. In the present invention, the above-mentioned parameters can be expressed by sensitivity coefficients.

[0081] For example, the braking of a vehicle mainly depends on the frictional force between the tires and the ground. The magnitude of the frictional force depends on the friction coefficient. Assuming the friction coefficient is μ, the braking distance A = V * V / 2gμ (g = 9.8 m / s²). That is, for different vehicle driving speeds, the braking distances are different, as shown in Table 1. The first sensitivity coefficient A is obtained based on the current driving vehicle speed. The greater the vehicle driving speed, the higher the first sensitivity coefficient A (A1 < A2 < A3 <...... < A15).

[0082] Table 1

[0083] Similarly, the first sensitivity coefficient A is obtained based on the current driving vehicle age. The first sensitivity coefficient B can be an empirical value. For example, in the first few years of the vehicle, the first sensitivity coefficient B can increase linearly. As the vehicle age increases, the first sensitivity coefficient B can change non-linearly, and the higher the vehicle age, the higher the first sensitivity coefficient B, and the faster the coefficient increases. (B1 < B2 < B3 < …… < B5), as shown in Table 2. The current driving vehicle age can be input by the driver into the current driving vehicle system, or it can be automatically saved by the current driving vehicle system. Table 2 can be preset in the current driving vehicle system.

[0084] Table 2

[0085] In Table 1, the driver of the current driving vehicle is assumed to be a young and concentrated driver, with good braking performance of the vehicle, and a comparison table of vehicle speed and braking distance under good climate and road conditions. The driver's reaction time consists of the following four parts:

[0086] 1. Perception time, that is, the time when the driver sees the obstacle, with an average of 0.2 seconds;

[0087] 2. Judgement time. After seeing the obstacle, it is necessary to judge whether this obstacle is worthy of braking. In an emergency, the driver will immediately brake due to nervousness without considering whether it is necessary or not. 0.1 - 0.2 seconds is enough time;

[0088] 3. Reaction initiation. After deciding to brake, the brain will notify the right foot to step on the brake, which will take about 0.2 seconds. At this point, from seeing the obstacle to stepping on the brake, 0.5 - 2 seconds have been used;

[0089] 4. Reaction execution time, that is, the time to step on the brake to the bottom. In an emergency, some drivers will step on it to the bottom immediately, while others step on it more slowly. This stage will take about 0.2 seconds again. Driver reaction time

[0090] Therefore, during braking, the driver's reaction time is related to factors such as the driver's age, working years, personality, fatigue level, technical proficiency, and familiarity with the vehicle being driven. As shown in Table 3, which shows the relationship between the driver's age and reaction time. Similarly, the second sensitivity coefficient can be an empirical value and can be preset in the current driving vehicle system.

[0091] Table 3

[0092] In some embodiments, the steepness of the road in the detection area is obtained by detecting the inclination of the body of the current driving vehicle. When the detection area information includes the road slope of the detection area road condition information, the third sensitivity coefficient E can be obtained according to the detection area road condition information. The higher the body inclination, the steeper the road, and the greater the inertia, so the third sensitivity coefficient E is greater, (E1 < E2 < E3 <.......En), as shown in Table 4.

[0093] Table 4

[0094] In some embodiments, through the information collection system of the current driving vehicle, such as an optical sensor, the weather information of the detection area can be obtained, including the light brightness. The system obtains the fourth sensitivity coefficient C according to the detection area weather information. The lower the light brightness, the higher the fourth sensitivity coefficient C, as shown in Table 4.

[0095] Table 4

[0096] Similarly, when the detection area weather information includes the weather condition, the fourth sensitivity coefficient F can be obtained, as shown in Table 5.

[0097] Table 5

[0098] The step of updating the vehicle risk area in the detection area according to one or more of the first sensitivity coefficient, the second sensitivity coefficient, the third sensitivity coefficient, and the fourth sensitivity coefficient in step S320 can be to multiply the size of the vehicle risk area in the detection area obtained in step S200 by one or more of the first sensitivity coefficient, the second sensitivity coefficient, the third sensitivity coefficient, and the fourth sensitivity coefficient. That is, the updated area S' of the vehicle risk area in the detection area is equal to the area S of the vehicle risk area in the detection area obtained in step S200 multiplied by the first sensitivity coefficient, the second sensitivity coefficient, the third sensitivity coefficient, and / or the fourth sensitivity coefficient, S' = S*(A*B*E*F). Figure 3 is a schematic diagram of the updated vehicle risk area in the detection area according to an embodiment of the present invention.

[0099] The step S400 of determining whether the current vehicle is at risk based on the current vehicle information and the vehicle risk area within the detection area may further include the following steps:

[0100] S410: Determine whether the current vehicle is in an updated vehicle risk area within the detection area based on the current vehicle and the vehicle information within the detection area;

[0101] If so, then S420: it is considered that the current driving vehicle has a driving risk.

[0102] After determining that the current vehicle is at risk in step S420, the method may further include the following steps:

[0103] S500: Determine the driving risk type of the currently traveling vehicle and send a corresponding warning signal type to the driver of the currently traveling vehicle based on the driving risk type. The warning signal type may be a voice reminder, such as a voice message indicating that the currently traveling vehicle has entered a blind spot / risk area within a detection area.

[0104] Taking the vehicle risk areas within the detection area as an example, which include a vehicle forward risk area I within the detection area, a vehicle rearward risk area III within the detection area, and a vehicle side risk area II within the detection area, the step S500 may include:

[0105] S510: Obtaining the distance between the current vehicle and a vehicle traveling in the same direction and lane within the detection area;

[0106] If the currently traveling vehicle is in front of a vehicle traveling in the same direction and lane within the detection area, then S521: adjusting the type of the corresponding first warning signal to be sent to the driver of the currently traveling vehicle based on the ratio of the distance value to the length D of the forward risk area of ​​the vehicle traveling in the same direction and lane within the detection area; for example:

[0107] When 0.8D<distance<=D, voice reminder will be given every 5 seconds;

[0108] When 0.6D<distance<=0.8D, voice reminder will be given every 4 seconds;

[0109] When 0.4D<distance<=0.6D, voice reminder will be given every 3 seconds;

[0110] When 0.2D<distance<=0.4D, voice reminder will be given every 2 seconds;

[0111] When 0D<distance<=0.2D, a voice reminder will be given every 1 second.

[0112] If the currently traveling vehicle is behind a vehicle traveling in the same direction and lane within the detection area, then S522: Based on the ratio of the distance value to the length L of the rearward risk zone for the vehicle traveling in the same direction and lane within the detection area, a corresponding second warning signal type is sent to the driver of the currently traveling vehicle. The second warning signal type may be similar to the first warning signal type and will not be further described here.

[0113] In some embodiments, the method further comprises the following steps:

[0114] S600: Input the currently traveling vehicle information, the vehicle information within the detection area, and the vehicle risk area within the detection area into an animation model to obtain a real-time risk position image of the currently traveling vehicle and the vehicles within the detection area. Using this real-time risk position image, the driver can intuitively determine the location and potential risk of the currently traveling vehicle. An embodiment of the present invention also provides a vehicle driving risk detection system for implementing the vehicle driving risk detection method. FIG4 is a block diagram of a vehicle driving risk detection system according to an embodiment of the present invention. The system includes an information acquisition module M100, a first calculation module M200, and a second calculation module M300.

[0115] The information acquisition module M100 is provided on the currently traveling vehicle and is used to obtain the detection area information of the currently traveling vehicle, wherein the detection area information includes the vehicle information within the detection area;

[0116] The first calculation module M200 is used to determine the vehicle risk area within the detection area according to the vehicle information within the detection area;

[0117] The information acquisition module M100 is also used to obtain the current driving vehicle information;

[0118] The second calculation module M300 is used to determine whether the current driving vehicle has a driving risk based on the current driving vehicle information and the vehicle risk area in the detection area.

[0119] The functional implementation methods of each functional module in the vehicle driving risk detection system of the embodiment can be implemented by adopting the specific implementation methods of each step in the vehicle driving risk detection method described above. For example, the information acquisition module M100, the first calculation module M200, and the second calculation module M300 can respectively implement their functions by adopting the specific implementation methods of steps S100 to S600 described above, which will not be detailed here.

[0120] The electronic device 600 according to this embodiment of the present invention is described below with reference to Figure 5. The electronic device 600 shown in Figure 5 is only an example and should not limit the functions and scope of use of the embodiment of the present invention.

[0121] As shown in FIG5 , electronic device 600 is implemented as a general-purpose computing device. Components of electronic device 600 may include, but are not limited to, at least one processing unit 610, at least one storage unit 620, a bus 630 connecting various platform components (including storage unit 620 and processing unit 610), and a display unit 640.

[0122] The storage unit stores program code, which can be executed by the processing unit 610, so that the processing unit 610 performs the steps according to various exemplary embodiments of the present invention described in the above method section of this specification. For example, the processing unit 610 can perform the steps shown in Figure 1.

[0123] The storage unit 620 may include a readable medium in the form of a volatile storage unit, such as a random access memory unit (RAM) 6201 and / or a cache memory unit 6202 , and may further include a read-only memory unit (ROM) 6203 .

[0124] The storage unit 620 may also include a program / utility 6204 having a set (at least one) of program modules 6205, such program modules 6205 including but not limited to: an operating system, one or more application programs, other program modules, and program data, each of which or some combination may include an implementation of a network environment.

[0125] Bus 630 may represent one or more of several types of bus structures, including a memory bus or memory controller, a peripheral bus, an accelerated graphics port, a processing unit, or a local bus using any of a variety of bus architectures.

[0126] The electronic device 600 can also communicate with one or more external devices 700 (e.g., a keyboard, a pointing device, a Bluetooth device, etc.), one or more devices that enable a user to interact with the electronic device 600, and / or any device that enables the electronic device 600 to communicate with one or more other computing devices (e.g., a router, a modem, etc.). Such communication can occur via an input / output (I / O) interface 650. Furthermore, the electronic device 600 can communicate with one or more networks (e.g., a local area network (LAN), a wide area network (WAN), and / or a public network such as the Internet) via a network adapter 660. The network adapter 660 can communicate with other modules of the electronic device 600 via the bus 630. It should be understood that, although not shown in the figures, other hardware and / or software modules can be used in conjunction with the electronic device 600, including but not limited to microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data backup storage platforms.

[0127] An embodiment of the present invention further provides a computer-readable storage medium for storing a program that, when executed, implements the steps of the vehicle driving risk detection method. In some possible implementations, various aspects of the present invention may also be implemented in the form of a program product, which includes program code. When the program product is executed on a terminal device, the program code is configured to cause the terminal device to execute the steps according to the various exemplary embodiments of the present invention described in the method section above.

[0128] Referring to FIG6 , a program product 800 for implementing the above-described method according to an embodiment of the present invention is described. The program product 800 may be implemented as a portable compact disk read-only memory and include program code, and may be executed on a terminal device, such as a personal computer. However, the program product of the present invention is not limited thereto. In this document, a readable storage medium may be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, apparatus, or device.

[0129] The program product may employ any combination of one or more readable media. The readable medium may be a readable signal medium or a readable storage medium. The readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device, or component, or any combination thereof. More specific examples (a non-exhaustive list) of readable storage media include: an electrical connection with one or more wires, a portable disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination thereof.

[0130] Computer-readable storage media may include a data signal propagated in baseband or as part of a carrier wave, which carries readable program code. Such propagated data signals may take a variety of forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. The readable storage medium may also be any readable medium other than a readable storage medium, which may send, propagate, or transmit a program for use by or in conjunction with an instruction execution system, apparatus, or device. The program code contained on the readable storage medium may be transmitted using any appropriate medium, including but not limited to wireless, wired, optical cable, RF, etc., or any suitable combination thereof.

[0131] The program code for performing the operations of the present invention may be written in any combination of one or more programming languages, including object-oriented programming languages ​​such as Java, C++, and the like, as well as conventional procedural programming languages ​​such as "C" or similar programming languages. The program code may be executed entirely on the user computing device, partially on the user device, as a stand-alone software package, partially on the user computing device and partially on a remote computing device, or entirely on a remote computing device or server. In cases involving a remote computing device, the remote computing device may be connected to the user computing device via any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computing device (e.g., via the Internet using an Internet service provider).

[0132] In summary, the present invention provides a vehicle driving risk detection method, system, device, and storage medium. The vehicle driving risk detection method includes the following steps: the currently traveling vehicle obtains detection area information of the currently traveling vehicle through a configured information acquisition module, the detection area information including vehicle information within the detection area; determines a vehicle risk area within the detection area based on the vehicle information within the detection area; obtains the currently traveling vehicle information; and determines whether the currently traveling vehicle has a driving risk based on the currently traveling vehicle information and the vehicle risk area within the detection area. The vehicle driving risk detection method of the present invention obtains the currently traveling vehicle information and detection area information through the traveling vehicle's own information acquisition module, determines the risk of the currently traveling vehicle based on the above information, and alerts the driver accordingly, thereby improving the safety of vehicle driving.

[0133] The above content is a further detailed description of the present invention in conjunction with specific preferred embodiments, and it cannot be considered that the specific implementation of the present invention is limited to these descriptions. For those skilled in the art, it is obvious that the present application is not limited to the details of the above-mentioned exemplary embodiments, and the present application can be implemented in other specific forms without departing from the spirit or basic characteristics of the present application. Therefore, no matter from which point of view, the embodiments should be regarded as exemplary and non-restrictive. The scope of the present application is limited by the appended claims rather than the above description, and it is intended that all changes that fall within the meaning and scope of the equivalent elements of the claims are included in the present application. Any figure mark in the claims should not be regarded as limiting the claims involved. In addition, it is obvious that the word "comprising" does not exclude other units or steps, and the singular does not exclude the plural. Multiple units or devices stated in the device claim can also be implemented by one unit or device through software or hardware. Words such as first and second are used to indicate names and do not indicate any specific order.

Claims

1. A vehicle driving risk detection method, characterized in that: The following steps are involved: The currently traveling vehicle obtains the detection area information of the currently traveling vehicle through the configured information acquisition module, and the detection area information includes the vehicle information in the detection area; Determine a vehicle risk area within the detection area according to the vehicle information within the detection area; Get the current vehicle information; It is determined whether the currently traveling vehicle has a driving risk according to the currently traveling vehicle information and the vehicle risk area in the detection area.

2. The vehicle driving risk detection method according to claim 1, characterized in that: The current vehicle information includes the current vehicle position information, the current vehicle model information and the current vehicle driving information; and / or The vehicle information within the detection area includes vehicle position information within the detection area, vehicle model information within the detection area, and vehicle driving information within the detection area.

3. The vehicle driving risk detection method according to claim 1, characterized in that: The step of determining risk area information of vehicles within the detection area according to the vehicle information within the detection area comprises: Acquire a vehicle model database, wherein the vehicle model database is a data set of vehicle model information and corresponding risk area information; The risk area of ​​vehicles in the detection area is acquired according to the vehicle information in the detection area and the vehicle model database.

4. The vehicle driving risk detection method according to claim 3, characterized in that: The current driving vehicle information includes the current driving vehicle speed and / or the vehicle age, and the step of obtaining the current driving vehicle information includes: obtaining a first sensitivity coefficient according to the current driving vehicle speed; and / or The current driving vehicle information includes the driver information of the current driving vehicle, and the step of obtaining the current driving vehicle information includes: obtaining a second sensitivity coefficient according to the driver information of the current driving vehicle; and / or The detection area information includes the detection area road condition information, and the step of obtaining the current driving vehicle information includes: obtaining a third sensitivity coefficient according to the current detection area road condition information; and / or The detection area information includes detection area weather information, and the step of obtaining current driving vehicle information includes: obtaining a fourth sensitivity coefficient according to the current detection area weather information.

5. The vehicle driving risk detection method according to claim 4, characterized in that: Before the step of determining whether the currently traveling vehicle has a driving risk according to the currently traveling vehicle information and the risk area, the following step further includes: The vehicle risk area within the detection area is updated according to one or more of the first sensitivity coefficient, the second sensitivity coefficient, the third sensitivity coefficient and the fourth sensitivity coefficient.

6. The vehicle driving risk detection method according to claim 5, characterized in that: Determining whether the currently traveling vehicle has a driving risk according to the currently traveling vehicle information and the vehicle risk area in the detection area comprises the following steps: Determine whether the current driving vehicle is in the updated vehicle risk area within the detection area according to the current driving vehicle and the vehicle information within the detection area; If so, it is considered that the current driving vehicle has driving risks.

7. The vehicle driving risk detection method according to claim 6, characterized in that: After the step of considering that the currently traveling vehicle has a driving risk, the method further includes the following steps: determining the driving risk type of the currently traveling vehicle and sending a corresponding warning signal type to the driver of the currently traveling vehicle according to the driving risk type.

8. The vehicle driving risk detection method according to claim 7, characterized in that: The vehicle risk area within the detection area includes the vehicle forward risk area within the detection area, the vehicle rearward risk area within the detection area, and the vehicle risk areas on both sides of the detection area; The determining of the driving risk type of the currently driving vehicle and sending a corresponding warning signal type to the driver of the currently driving vehicle according to the driving risk type includes: Get the distance between the current vehicle and the vehicle traveling in the same direction and lane in the detection area; If the currently traveling vehicle is traveling in front of a vehicle traveling in the same direction and lane within the detection area, the corresponding first warning signal type is sent to the driver of the currently traveling vehicle according to the ratio of the value of the distance to the length of the forward risk area of ​​the vehicle traveling in the same direction and lane within the detection area; If the current vehicle is traveling behind a vehicle traveling in the same direction and lane within the detection area, then the vehicle is detected based on the value of the distance and the rear wind speed of the vehicle traveling in the same direction and lane within the detection area. The ratio of the length of the dangerous area is adjusted to send a corresponding second warning signal type to the driver of the current driving vehicle.

9. The vehicle driving risk detection method according to claim 6, characterized in that: The method further comprises the following steps: The information of the currently traveling vehicle, the information of the vehicles in the detection area and the risk area of ​​the vehicles in the detection area are input into the animation modeling model to obtain the real-time risk position images of the currently traveling vehicle and the vehicles in the detection area.

10. A vehicle driving risk detection system, used to implement the vehicle driving risk detection method according to claim 1, characterized in that: It includes an information collection module, a first calculation module and a second calculation module; The information acquisition module is arranged on the currently traveling vehicle and is used to obtain the detection area information of the currently traveling vehicle, wherein the detection area information includes the vehicle information within the detection area; The first calculation module is used to determine the vehicle risk area within the detection area according to the vehicle information within the detection area; The information acquisition module is also used to obtain the current driving vehicle information; The second calculation module is used to determine whether the current driving vehicle has a driving risk according to the current driving vehicle information and the vehicle risk area in the detection area.

11. The vehicle driving risk detection system according to claim 10, characterized in that: The information acquisition module includes a sensor unit and an information processing unit connected to the sensor unit; The sensor unit includes a forward sensor group, a first lateral sensor group and a second lateral sensor arranged on two sides of the vehicle, and a rearward sensor group; The forward sensor group includes one or more of a forward laser radar, a forward camera, a forward wide-angle camera, a forward binocular right camera, a forward binocular left camera, and a forward millimeter-wave radar; and / or The first lateral sensor group includes a first lateral camera and / or a first lateral millimeter-wave radar; and / or The second lateral sensor group includes a second lateral camera and / or a second lateral millimeter-wave radar; and / or The rearward sensor group includes a rearward laser radar, a rearward camera, a rearward wide-angle camera, One or more of a rear-facing binocular right camera, a rear-facing binocular left camera, and a rear-facing millimeter-wave radar.

12. An electronic device, characterized in that: include: processor; a memory storing executable instructions of the processor; Wherein, the processor is configured to execute the steps of the vehicle driving risk detection method according to any one of claims 1 to 9 by executing the executable instructions.

13. A computer-readable storage medium for storing a program, characterized in that: When the program is executed by a processor, the steps of the vehicle driving risk detection method according to any one of claims 1 to 9 are implemented.

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