A method and device for alerting a driver of a vehicle when driving on a complex road section

Abstract

The application discloses a warning method and device for a vehicle driving on a complex road section. The warning method for the vehicle driving on the complex road section comprises the following steps: obtaining a historical habit database, which comprises preset complex road condition information and a driver's brake reaction time, one driver's brake reaction time corresponding to one preset complex road condition information; obtaining current road condition information; obtaining the driver's brake reaction time corresponding to the preset complex road condition information which is the same as the current road condition information; obtaining vehicle basic information; generating a warning distance according to the vehicle basic information, the current road condition information and the driver's brake reaction time; obtaining a distance to a front obstacle; and judging whether the warning distance is less than or equal to the distance to the front obstacle, and if yes, generating a warning signal. The warning method for the vehicle driving on the complex road section can more accurately mark a brake distance through the driver's brake habit and reaction time, so as to consider problems caused by other factors such as road conditions affecting driving in detail.

Description

Technical Field

[0001] This application relates to the field of vehicle driving safety technology, and in particular to an alarm method and device for vehicles driving on complex road sections. Background Technology

[0002] As we all know, driving safety has always been a top priority in the automotive industry. During driving, drivers frequently encounter complex road conditions, and data analysis on whether these conditions are safe to drive in is currently lacking. However, within current vehicle data networks, through signal calibration and uploading, we can easily obtain information such as the vehicle's current speed, tire friction coefficient, and the driver's average braking reaction time. Using this information, braking distance can be calculated, and combined with road condition and weather information, a conclusion can be drawn regarding whether driving in those conditions is safe. This is the background for the implementation of this solution.

[0003] Existing technologies offer various methods for calculating braking distance, but most focus on preventing rear-end collisions and determining safe distances. These methods aim to control the vehicle safely and avoid collisions when the braking distance exceeds the safe distance. However, they do not address driving in complex road conditions. Furthermore, existing calculation methods typically use vehicle information and the coefficient of friction to arrive at quantitative values, without precisely considering the driver's braking habits and reaction time.

[0004] Therefore, there is a need for a technical solution to address or at least mitigate the aforementioned shortcomings of existing technologies. Summary of the Invention

[0005] The purpose of this invention is to provide an alarm method for vehicles driving on complex road sections, which at least solves one of the above-mentioned technical problems.

[0006] This invention provides the following solution:

[0007] According to one aspect of the present invention, a method for alarming a vehicle traveling on complex road sections is provided, the method comprising:

[0008] Obtain a historical habit database, which includes at least one preset complex road condition information and driver braking reaction time, with one driver braking reaction time corresponding to one preset complex road condition information.

[0009] Get current traffic information;

[0010] Obtain the driver's braking reaction time corresponding to preset complex road condition information that is the same as the current road condition information;

[0011] Obtain basic vehicle information;

[0012] An alarm distance is generated based on the vehicle's basic information, current road conditions, and the driver's braking reaction time.

[0013] Get the distance to obstacles ahead;

[0014] Determine if the alarm distance is less than or equal to the distance to the obstacle ahead. If so, then...

[0015] Generate an alarm signal.

[0016] Optionally, obtaining current road condition information includes:

[0017] Acquire image information of the front of the vehicle captured by the camera device;

[0018] The system identifies the image information in front of the vehicle to obtain current road condition information.

[0019] Optionally, the step of recognizing the image information ahead of the vehicle to obtain current road condition information includes:

[0020] Obtain a preset road surface material database, which includes at least one preset road surface material information and preset material image information corresponding to each preset road surface material information;

[0021] The similarity calculation is performed between the vehicle's frontal image information and each preset material image to determine whether any similarity value exceeds a preset first similarity threshold. If so, then...

[0022] The preset road surface material information corresponding to the preset material image whose similarity value exceeds the preset first similarity threshold is obtained as the current road condition information.

[0023] Optionally, the step of calculating the similarity between the vehicle front image information and each preset material image to determine whether any similarity value exceeds a preset first similarity threshold includes:

[0024] Get the image size of the preset material image;

[0025] Determine if the image size of the image information in front of the vehicle is the same as the image size of the preset material image; if not, then...

[0026] The image information in front of the vehicle is segmented into multiple individual segmented images of the same size as the preset material image.

[0027] Each individual segmented image is compared with each preset material image to obtain the similarity value of each individual. It is then determined whether there is a preset number of individual similarity values ​​greater than the second preset similarity threshold. If so, it is determined that there is one similarity value that exceeds the preset first similarity threshold.

[0028] Optionally, the step of recognizing the image information ahead of the vehicle to obtain current road condition information further includes:

[0029] Obtain the trained weather classifier;

[0030] Extract image feature information from the foreground image information;

[0031] The image feature information is input into a trained weather classifier to obtain a weather classification label. The weather classification label and the preset road surface material information constitute the current road condition information.

[0032] Optionally, generating the alarm distance based on current road condition information and the driver's braking reaction time includes:

[0033] The coefficient of friction is obtained based on current road conditions.

[0034] The driver's braking reaction time is obtained based on the driver's braking reaction time and the current road condition information;

[0035] An alarm distance is generated based on the friction coefficient, the driver's braking reaction time, and basic vehicle information.

[0036] Optionally, obtaining the friction coefficient based on current road condition information includes:

[0037] Obtain a preset friction coefficient database, which includes at least one preset friction coefficient and preset road condition information, with one preset road condition information corresponding to one preset friction coefficient.

[0038] Obtain the preset friction coefficient corresponding to the preset road condition information that is the same as the current road condition information.

[0039] Optionally, the basic vehicle information includes vehicle gravitational acceleration information, vehicle weight information, friction between wheels and road surface information, and vehicle travel distance information after braking.

[0040] Optionally, there may be multiple historical habit databases, with each historical habit database associated with one person's facial image information;

[0041] Prior to acquiring the historical habit database, the alarm method for vehicles driving on complex road sections further includes:

[0042] Acquire driver image information;

[0043] The driver's image information is compared with the similarity of each of the aforementioned face images to determine if any of the similarity values ​​exceeds a third similarity threshold. If so, then...

[0044] Obtain the historical habit database associated with the facial image information that exceeds the third similarity threshold.

[0045] This application also provides an alarm device for vehicles driving on complex road sections, the alarm device for vehicles driving on complex road sections includes:

[0046] The historical habit database acquisition module is used to acquire a historical habit database, which includes at least one preset complex road condition information and driver braking reaction time. One driver braking reaction time corresponds to one preset complex road condition information.

[0047] Current traffic information acquisition module, the current traffic information acquisition module is used to acquire current traffic information;

[0048] The driver's braking reaction time acquisition module is used to acquire the driver's braking reaction time corresponding to preset complex road condition information that is the same as the current road condition information.

[0049] A vehicle basic information acquisition module, which is used to acquire basic vehicle information;

[0050] An alarm distance generation module is used to generate an alarm distance based on basic vehicle information, current road condition information, and the driver's braking reaction time.

[0051] A forward obstacle distance acquisition module, wherein the forward obstacle distance acquisition module is used to acquire the distance of the forward obstacle;

[0052] The judgment module is used to determine whether the alarm distance is less than or equal to the distance to the obstacle ahead;

[0053] An alarm signal generation module is used to generate an alarm signal when the judgment module determines that it is true.

[0054] The alarm method for vehicles driving on complex road sections provided in this application can more accurately indicate the braking distance by incorporating the driver's braking habits and reaction time, thereby allowing for detailed consideration of problems caused by other factors affecting driving, such as road conditions. Attached Figure Description

[0055] Figure 1 This is a flowchart of an alarm method for vehicles driving on complex road sections provided by one or more embodiments of the present invention.

[0056] Figure 2 This is a block diagram of an electronic device for providing an alarm method for vehicles driving on complex road sections, according to one or more embodiments of the present invention. Detailed Implementation

[0057] The technical solution of the present invention will now be clearly and completely described with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0058] Figure 1 This is a flowchart of an alarm method for vehicles driving on complex road sections provided by one or more embodiments of the present invention.

[0059] like Figure 1 The alarm methods shown include:

[0060] Step 1: Obtain the historical habit database, which includes at least one preset complex road condition information and driver braking reaction time. One driver braking reaction time corresponds to one preset complex road condition information.

[0061] Step 2: Obtain current traffic information;

[0062] Step 3: Obtain the driver's braking reaction time corresponding to the preset complex road condition information that is the same as the current road condition information;

[0063] Step 4: Obtain basic vehicle information;

[0064] Step 5: Generate an alarm distance based on the vehicle's basic information, current road conditions, and the driver's braking reaction time;

[0065] Step 6: Obtain the distance to obstacles ahead;

[0066] Step 7: Determine if the alarm distance is less than or equal to the distance to the obstacle ahead. If so, then...

[0067] Step 8: Generate an alarm signal.

[0068] The alarm method for vehicles driving on complex road sections provided in this application can more accurately indicate the braking distance by incorporating the driver's braking habits and reaction time, thereby allowing for detailed consideration of problems caused by other factors affecting driving, such as road conditions.

[0069] In this embodiment, obtaining current road condition information includes:

[0070] Acquire image information of the front of the vehicle captured by the camera device;

[0071] It identifies image information in front of the vehicle to obtain current road condition information.

[0072] In this embodiment, identifying the image information ahead of the vehicle to obtain current road condition information includes:

[0073] Obtain a preset road surface material database, which includes at least one preset road surface material information and preset material image information corresponding to each preset road surface material information;

[0074] The similarity calculation is performed between the vehicle's frontal image information and each preset material image to determine whether any similarity value exceeds a preset first similarity threshold. If so, then...

[0075] The preset road surface material information corresponding to the preset material image whose similarity value exceeds the preset first similarity threshold is obtained as the current road condition information.

[0076] By setting preset images, the similarity between the road image captured by the current camera device and the preset road can be obtained through similarity analysis. For example, asphalt roads have similar patterns and shapes, and soil has similar features.

[0077] In this embodiment, the similarity calculation of the vehicle front image information with each preset material image, thereby determining whether any similarity value exceeds a preset first similarity threshold, includes:

[0078] Get the image size of the preset material image;

[0079] Determine if the image size of the image information in front of the vehicle is the same as the image size of the preset material image; if not, then...

[0080] The image information in front of the vehicle is segmented into multiple individual segmented images of the same size as the preset material image.

[0081] Each individual segmented image is compared with each preset material image to obtain the similarity value of each individual. It is then determined whether there is a preset number of individual similarity values ​​greater than the second preset similarity threshold. If so, it is determined that there is one similarity value that exceeds the preset first similarity threshold.

[0082] In real-world road conditions, it's unlikely that the entire captured image will be identical to the preset material image. For example, when shooting a road surface with a camera, you might capture asphalt road on one hand, and surrounding buildings or other objects on the other. In this case, the surrounding buildings, etc., might reduce the similarity value. However, in the captured image, there are at least a few local areas that are asphalt road. Therefore, the size of the preset material image is set to a small size (e.g., 24×24). If the entire captured image is 1024×1024 and is cut into multiple 24×24 individual segmented images, as long as a preset number of individual segmented images have a similarity to the preset material image exceeding a threshold, it is acceptable.

[0083] In this embodiment, identifying the image information ahead of the vehicle to obtain current road condition information further includes:

[0084] Obtain the trained weather classifier;

[0085] Extract image feature information from the foreground image information;

[0086] The image feature information is input into a trained weather classifier to obtain a weather classification label. The weather classification label and the preset road surface material information constitute the current road condition information.

[0087] In this embodiment, the weather classification label may include rainy days, snowy days, foggy days, etc., and the preset road surface material information may include asphalt material, mud, etc.

[0088] By combining the above information, various current road condition information can be generated.

[0089] In this embodiment, the historical habit database contains the historical braking habits of each driver. Specifically, a pressure sensor installed at the brake pedal can record and update the pressure value each time the driver brakes, and radar can be used to obtain the distance to obstacles. For example, a threshold can be set for the distance obtained by the radar, such as 20 meters. Then, by using a fixed distance, the driver's braking reaction time can be known, that is, the time taken from the 20-meter position to obtaining the information transmitted by the pressure sensor.

[0090] It is understandable that each person's historical braking habits can be obtained through experimentation, that is, by setting up test sections and then letting users test them.

[0091] Alternatively, data can be obtained by combining dashcam footage with pressure sensor readings. For example, analysts can compare the recording times from the dashcam with those from the pressure sensor to determine the driver's braking reaction time. If the dashcam shows an obstacle in front of the driver at 10:20:15, and the pressure sensor records pressure information at 10:20:16, then the braking reaction time is 1 second.

[0092] In this embodiment, generating the alarm distance based on current road condition information and the driver's braking reaction time includes:

[0093] The coefficient of friction is obtained based on current road conditions.

[0094] The driver's braking reaction time is obtained based on the driver's braking reaction time and the current road condition information;

[0095] An alarm distance is generated based on the friction coefficient, the driver's braking reaction time, and basic vehicle information.

[0096] In this embodiment, obtaining the friction coefficient based on current road condition information includes:

[0097] Obtain a preset friction coefficient database, which includes at least one preset friction coefficient and preset road condition information, with one preset road condition information corresponding to one preset friction coefficient.

[0098] Obtain the preset friction coefficient corresponding to the preset road condition information that is the same as the current road condition information.

[0099] In this embodiment, the basic vehicle information includes vehicle gravitational acceleration information, vehicle weight information, friction between the wheels and the road surface information, and vehicle travel distance information after braking.

[0100] In this embodiment, the braking distance is calculated using the following formula:

[0101] Definitions: v = vehicle speed, t = reaction time, u = coefficient of friction, g = gravitational acceleration, M = weight, F = friction between the wheel and the road surface, S = braking distance, S1 = distance traveled by the vehicle after the brakes are applied.

[0102] Braking distance and vehicle speed should be determined by the work done by friction equaling the vehicle's kinetic energy, i.e., FS1 = Mv^2 / 2, F = Mgu. Therefore, S1 = v^2 / 2gu, and S = vt + v^2 / 2gu.

[0103] The road condition needs to be matched with the image recognition algorithm in the system to obtain the friction coefficient.

[0104] In this embodiment, there are multiple historical habit databases, and each historical habit database is associated with one person's facial image information;

[0105] Prior to acquiring the historical habit database, the alarm method for vehicles driving on complex road sections further includes:

[0106] Acquire driver image information;

[0107] The driver's image information is compared with the similarity of each of the aforementioned face images to determine if any of the similarity values ​​exceeds a third similarity threshold. If so, then...

[0108] Obtain the historical habit database associated with the facial image information that exceeds the third similarity threshold.

[0109] In actual vehicle use, it may not be the same driver driving the vehicle. Therefore, each driver has their own associated historical habit database.

[0110] In another embodiment, an intelligent frontal camera (IFC) can be used to implement various ADAS functions, such as lane departure warning (LDW) and forward collision warning (FCW). For example, when the driver is driving and there is a possibility of a collision with the vehicle in front, an alarm will be issued, and the braking reaction time can be obtained through a pressure sensor.

[0111] The following examples further illustrate this application in detail. It is understood that these examples do not constitute any limitation on this application.

[0112] Assuming the driver is A, facial recognition is used to obtain a database of driver A's historical habits. This database contains A's braking reaction time under different preset complex road conditions. For example, when the preset complex road conditions are asphalt + slippery road surface, the driver's braking reaction time is 0.5s (assuming the reaction time is tested after the obstacle is 20 meters away from the car). When the preset complex road conditions are mud + snowy road surface, the driver's braking reaction time is 1 second (assuming the reaction time is tested after the obstacle is 20 meters away from the car).

[0113] Obtain current road condition information. Assuming the current road condition is asphalt + slippery surface, the driver's braking reaction time is 0.5 seconds.

[0114] The system obtains basic vehicle information, such as a vehicle weight of 1500kg, a speed of 60km / h, a friction coefficient of 0.6 (which can be obtained from a preset friction force table in this embodiment), and a gravitational acceleration of 9.8m / s². The system uses the formula: S=vt+v² / 2gu=32m. This means the alarm distance is 32m. The distance sensor can detect the distance to obstacles, and an alarm is triggered if the distance is less than or equal to 32m. In other embodiments, the alarm distance can be increased by adding a certain amount of distance, for example, 32+5, making it 37m, thus providing the driver with more reaction time.

[0115] This application also provides an alarm device for vehicles driving on complex road sections. The alarm device includes a historical habit database acquisition module, a current road condition information acquisition module, a driver's braking reaction time acquisition module, a vehicle basic information acquisition module, an alarm distance generation module, a forward obstacle distance acquisition module, a judgment module, and an alarm signal generation module.

[0116] The historical habit database acquisition module is used to acquire the historical habit database, which includes at least one preset complex road condition information and the driver's braking reaction time. One driver's braking reaction time corresponds to one preset complex road condition information.

[0117] The current traffic information acquisition module is used to acquire current traffic information;

[0118] The driver's braking reaction time acquisition module is used to acquire the driver's braking reaction time corresponding to preset complex road condition information that is the same as the current road condition information;

[0119] The vehicle basic information acquisition module is used to acquire basic vehicle information;

[0120] The alarm distance generation module is used to generate an alarm distance based on the vehicle's basic information, current road conditions, and the driver's braking reaction time.

[0121] The obstacle distance acquisition module is used to acquire the distance to obstacles ahead;

[0122] The judgment module is used to determine whether the alarm distance is less than or equal to the distance to the obstacle ahead;

[0123] The alarm signal generation module is used to generate an alarm signal when the judgment module determines that it is true.

[0124] Figure 2 This is a block diagram of an electronic device structure provided by one or more embodiments of the present invention.

[0125] like Figure 2As shown, this application also discloses an electronic device, including: a processor, a communication interface, a memory, and a communication bus, wherein the processor, the communication interface, and the memory communicate with each other through the communication bus; the memory stores a computer program, and when the computer program is executed by the processor, the processor performs the steps of an alarm method for vehicles driving on complex road sections.

[0126] This application also provides a computer-readable storage medium storing a computer program executable by an electronic device, which, when run on the electronic device, causes the electronic device to perform the steps of an alarm method for a vehicle traveling on complex road sections.

[0127] The communication bus mentioned in the above electronic devices can be a Peripheral Component Interconnect (PCI) bus or an Extended Industry Standard Architecture (EISA) bus, etc. This communication bus can be divided into address bus, data bus, control bus, etc. For ease of illustration, only one thick line is used to represent it in the diagram, but this does not indicate that there is only one bus or one type of bus.

[0128] The electronic device includes a hardware layer, an operating system layer running on top of the hardware layer, and an application layer running on the operating system. The hardware layer includes hardware such as a central processing unit (CPU), a memory management unit (MMU), and memory. The operating system can be any one or more computer operating systems that control the electronic device through processes, such as Linux, Unix, Android, iOS, or Windows. Furthermore, in this embodiment of the invention, the electronic device can be a smartphone, tablet computer, or other handheld device, or a desktop computer, portable computer, or other electronic device; there is no particular limitation in this embodiment.

[0129] In this embodiment of the invention, the executing entity for electronic device control can be an electronic device itself, or a functional module within an electronic device capable of calling and executing a program. The electronic device can obtain the firmware corresponding to the storage medium. This firmware is provided by the supplier, and different storage media may have the same or different firmware; no limitation is made here. After obtaining the firmware corresponding to the storage medium, the electronic device can write this firmware into the storage medium; specifically, it burns the firmware corresponding to the storage medium into the storage medium. The process of burning the firmware into the storage medium can be implemented using existing technology, and will not be elaborated upon in this embodiment of the invention.

[0130] Electronic devices can also obtain reset commands corresponding to the storage media. The reset commands corresponding to the storage media are provided by the supplier. The reset commands corresponding to different storage media can be the same or different, and no restrictions are imposed here.

[0131] At this time, the storage medium of the electronic device is a storage medium on which the corresponding firmware has been written. The electronic device can respond to the reset command corresponding to the storage medium on which the corresponding firmware has been written, thereby resetting the storage medium on which the corresponding firmware has been written according to the reset command. The process of resetting the storage medium according to the reset command can be implemented by existing technology and will not be described in detail in this embodiment of the invention.

[0132] For ease of description, the above devices are described separately by function as various units and modules. Of course, in implementing this application, the functions of each unit and module can be implemented in one or more software and / or hardware.

[0133] It will be understood by those skilled in the art that, unless otherwise defined, all terms used herein (including technical and scientific terms) have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains. It should also be understood that terms such as those defined in general dictionaries should be understood to have the meaning consistent with their meaning in the context of the prior art, and should not be interpreted in an idealized or overly formal sense unless specifically defined.

[0134] For the sake of simplicity, the method embodiments are described as a series of actions. However, those skilled in the art should understand that the embodiments of the present invention are not limited to the described order of actions, because according to the embodiments of the present invention, some steps can be performed in other orders or simultaneously. Furthermore, those skilled in the art should also understand that the embodiments described in the specification are preferred embodiments, and the actions involved are not necessarily essential to the embodiments of the present invention.

[0135] As can be seen from the above description of the embodiments, those skilled in the art can clearly understand that this application can be implemented by means of software plus necessary general-purpose hardware platforms. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods described in various embodiments or some parts of the embodiments of this application.

[0136] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some or all of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for issuing an alarm when a vehicle is driving on complex road sections, characterized in that, The alarm methods for vehicles driving on complex road sections include: Obtain a historical habit database, which includes at least one preset complex road condition information and driver braking reaction time, with one driver braking reaction time corresponding to one preset complex road condition information. Get current traffic information; Obtain the driver's braking reaction time corresponding to preset complex road condition information that is the same as the current road condition information; Obtain basic vehicle information; An alarm distance is generated based on the vehicle's basic information, current road conditions, and the driver's braking reaction time. Get the distance to obstacles ahead; Determine if the alarm distance is less than or equal to the distance to the obstacle ahead. If so, then... Generate an alarm signal; The acquisition of current traffic information includes: Acquire image information of the front of the vehicle captured by the camera device; The system identifies the image information in front of the vehicle to obtain current road condition information. The step of recognizing the image information ahead of the vehicle to obtain current road condition information includes: Obtain a preset road surface material database, which includes at least one preset road surface material information and preset material image information corresponding to each preset road surface material information; The similarity calculation is performed between the vehicle's frontal image information and each preset material image to determine whether any similarity value exceeds a preset first similarity threshold. If so, then... Obtain the preset road surface material information corresponding to the preset material image whose similarity value exceeds the preset first similarity threshold as the current road condition information; The step of calculating the similarity between the vehicle front image information and each preset material image to determine whether any similarity value exceeds a preset first similarity threshold includes: Get the image size of the preset material image; Determine if the image size of the image information in front of the vehicle is the same as the image size of the preset material image; if not, then... The image information in front of the vehicle is segmented into multiple individual segmented images of the same size as the preset material image. Each individual segmented image is compared with each preset material image to obtain the similarity value of each individual. It is then determined whether there is a preset number of individual similarity values ​​greater than the second preset similarity threshold. If so, it is determined that there is one similarity value that exceeds the preset first similarity threshold.

2. The alarm method for vehicles driving on complex road sections as described in claim 1, characterized in that, The step of recognizing the image information ahead of the vehicle to obtain current road condition information further includes: Obtain the trained weather classifier; Extract image feature information from the foreground image information; The image feature information is input into a trained weather classifier to obtain a weather classification label. The weather classification label and the preset road surface material information constitute the current road condition information.

3. The alarm method for vehicles driving on complex road sections as described in claim 2, characterized in that, The method of generating the alarm distance based on current road conditions and the driver's braking reaction time includes: Obtain the coefficient of friction based on current road conditions; The driver's braking reaction time is obtained based on the driver's braking reaction time and the current road condition information; An alarm distance is generated based on the friction coefficient, the driver's braking reaction time, and basic vehicle information.

4. The alarm method for vehicles driving on complex road sections as described in claim 3, characterized in that, The process of obtaining the friction coefficient based on current road condition information includes: Obtain a preset friction coefficient database, which includes at least one preset friction coefficient and preset road condition information, with one preset road condition information corresponding to one preset friction coefficient. Obtain the preset friction coefficient corresponding to the preset road condition information that is the same as the current road condition information.

5. The alarm method for vehicles driving on complex road sections as described in claim 4, characterized in that, The basic vehicle information includes vehicle gravitational acceleration information, vehicle weight information, friction between the wheels and the road surface information, and vehicle travel distance information after the brakes are applied.

6. The alarm method for vehicles driving on complex road sections as described in claim 5, characterized in that, There are multiple historical habit databases, and each historical habit database is associated with one person's facial image information; Prior to acquiring the historical habit database, the alarm method for vehicles driving on complex road sections further includes: Acquire driver image information; The driver's image information is compared with the similarity of each of the aforementioned face images to determine if any of the similarity values ​​exceeds a third similarity threshold. If so, then... Obtain the historical habit database associated with the facial image information that exceeds the third similarity threshold.

7. An alarm device for vehicles traveling on complex road sections, used in the alarm method for vehicles traveling on complex road sections as described in any one of claims 1 to 6, characterized in that, The alarm device for vehicles driving on complex road sections includes: The historical habit database acquisition module is used to acquire a historical habit database, which includes at least one preset complex road condition information and driver braking reaction time. One driver braking reaction time corresponds to one preset complex road condition information. Current traffic information acquisition module, the current traffic information acquisition module is used to acquire current traffic information; The driver's braking reaction time acquisition module is used to acquire the driver's braking reaction time corresponding to preset complex road condition information that is the same as the current road condition information. A vehicle basic information acquisition module, which is used to acquire basic vehicle information; An alarm distance generation module is used to generate an alarm distance based on basic vehicle information, current road condition information, and the driver's braking reaction time. A forward obstacle distance acquisition module, wherein the forward obstacle distance acquisition module is used to acquire the distance of the forward obstacle; The judgment module is used to determine whether the alarm distance is less than or equal to the distance to the obstacle ahead; An alarm signal generation module is used to generate an alarm signal when the judgment module determines that it is true.

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