Icy and snowy road section prediction method, device, electronic device and readable storage medium

Abstract

The present invention provides a method, device, electronic device and readable storage medium for predicting icy and snowy road sections, relating to the technical field of road condition prediction, including: determining historical slippery road sections based on historical vehicle data collected by a global positioning system, an anti-lock braking system and a vehicle body electronic stability system; determining weather characteristics and road section characteristics corresponding to historical slippery road sections based on map data and historical weather data; predicting the slip probability of the current vehicle on the road section to be traveled based on the weather characteristics and road section characteristics, and being able to accurately predict and identify icy and snowy road sections to effectively solve the technical problem of improving the user's driving experience.

Description

Technical Field

[0001] The present invention relates to the technical field of road condition prediction, and in particular to a method, device, electronic device and readable storage medium for predicting icy and snowy road sections. Background Art

[0002] With the development of vehicle technology, people's demand for a better driving experience is increasing. However, the road conditions today are more complex. For example, in some areas with extremely cold weather, some sections of the road may be covered with ice and snow, causing vehicles to slip and reducing the user's driving experience.

[0003] Currently, icy and snowy road sections mainly rely on weather forecasts to predict and warn drivers, but this road section identification method has low accuracy. Drivers still encounter unexpected skidding situations, and the driving experience has not been effectively improved. Summary of the Invention

[0004] In view of this, the purpose of the present invention is to provide a method, device, electronic device and readable storage medium for predicting icy and snowy road sections, which can accurately predict and identify icy and snowy road sections to effectively solve the technical problem of improving user driving experience.

[0005] In a first aspect, an embodiment provides a method for predicting icy and snowy road sections, the method comprising:

[0006] Determine historical slippery sections based on historical vehicle data collected by the global positioning system, anti-lock braking system, and electronic stability system;

[0007] Determining weather characteristics and road section characteristics corresponding to the historically slippery road section based on map data and historical weather data;

[0008] The slip probability of the current road section where the vehicle is to travel is predicted based on the weather characteristics and the road section characteristics.

[0009] In an optional embodiment, the step of determining a historical slippery road section based on historical vehicle data collected by a global positioning system, an anti-lock braking system, and an electronic stability system includes:

[0010] Collect real-time location data of each historical vehicle based on the global positioning system;

[0011] collecting real-time braking data of each of the historical vehicles according to an anti-lock braking system and an electronic stability system;

[0012] Based on the real-time braking data, determining whether there is a historical vehicle that has experienced skidding and the skidding time of the historical vehicle that has experienced skidding;

[0013] If so, the historical slipping section of each of the historical vehicles is determined according to the slipping time and the real-time position data corresponding to the slipping time.

[0014] In an optional embodiment, the step of determining the weather characteristics and road section characteristics corresponding to the historical slippery road section based on the map data and historical weather data includes:

[0015] Determining a road section feature corresponding to the historical slippery road section from map data based on the real-time position data corresponding to the historical slippery road section;

[0016] Based on the slipping moment corresponding to the historical slipping road section, the weather characteristics corresponding to the historical slipping road section are determined from the historical weather data corresponding to the slipping moment.

[0017] In an optional embodiment, the step of predicting the slip probability of the current road section to be traveled by the vehicle based on the weather characteristics and the road section characteristics includes:

[0018] Determining, based on the weather characteristics and the road section characteristics, the similarity between the current road section to be traveled by the vehicle and the historical slippery road section;

[0019] Based on the similarity, the slip probability of the current vehicle on the road section to be traveled is predicted.

[0020] In an optional embodiment, the weather characteristics and the road section characteristics each include multiple feature categories; and the step of determining the similarity between the current road section to be traveled by the vehicle and the historical slippery road section based on the weather characteristics and the road section characteristics includes:

[0021] Determining a weight ratio of each feature category according to the frequency of each feature category corresponding to each historical vehicle slip moment;

[0022] Based on the feature category corresponding to the current road section to be traveled by the vehicle and the weight ratio, the similarity between the current road section to be traveled by the vehicle and the historical slippery road section is calculated.

[0023] In an optional embodiment, the step of predicting the slip probability of the current vehicle on the road section to be traveled based on the similarity includes:

[0024] If the similarity between the current road section to be traveled by the vehicle and the historical slippery road section is higher, it is predicted that the slip probability of the current road section to be traveled by the vehicle is higher.

[0025] In an optional embodiment, the method further comprises:

[0026] If the slip probability exceeds a slip threshold, a slip reminder is issued for the road section to be traveled.

[0027] In a second aspect, an embodiment provides a device for predicting icy and snowy road sections, the device comprising:

[0028] A first determination module determines a historical slippery road section based on historical vehicle data collected by a global positioning system, an anti-lock braking system, and an electronic stability system;

[0029] A second determining module determines weather characteristics and road section characteristics corresponding to the historically slippery road section based on map data and historical weather data;

[0030] The prediction module predicts the slip probability of the current road section where the vehicle is to travel based on the weather characteristics and the road section characteristics.

[0031] In a third aspect, an embodiment provides an electronic device comprising a memory and a processor, wherein the memory stores a computer program that can be run on the processor, and when the processor executes the computer program, the steps of the method described in any of the aforementioned embodiments are implemented.

[0032] In a fourth aspect, an embodiment provides a machine-readable storage medium, wherein the machine-readable storage medium stores machine-executable instructions. When the machine-executable instructions are called and executed by a processor, the machine-executable instructions prompt the processor to implement the steps of the method described in any one of the aforementioned implementation methods.

[0033] The embodiments of the present invention provide a method, device, electronic device and readable storage medium for predicting icy and snowy road sections. By using historical vehicle data collected by sensors of various historical vehicle collection systems, historical slippery sections can be determined. The section characteristics and weather characteristics corresponding to the historical slippery sections can then be determined based on map data and weather data. Based on the section characteristics and weather characteristics corresponding to the current section to be traveled by the vehicle, the similarity of the remaining historical slippery sections can be determined, and the slip probability of the section to be traveled can be predicted. This can achieve relatively accurate prediction of icy and snowy slippery sections, thereby effectively improving the user's driving experience.

[0034] Other features and advantages of the present disclosure will be set forth in the following description, or some features and advantages may be inferred or unambiguously determined from the description, or may be learned by practicing the above-mentioned technology of the present disclosure.

[0035] In order to make the above-mentioned objectives, features and advantages of the present disclosure more obvious and easy to understand, preferred embodiments are given below and described in detail with reference to the accompanying drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] In order to more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the specific embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0037] Figure 1 A flow chart of a method for predicting icy and snowy road sections provided by an embodiment of the present invention;

[0038] Figure 2 A schematic diagram of an application scenario of a method for predicting icy and snowy road sections provided by an embodiment of the present invention;

[0039] Figure 3 A schematic diagram of an application scenario of another method for predicting icy and snowy road sections provided by an embodiment of the present invention;

[0040] Figure 4 A schematic diagram of an application scenario of another method for predicting icy and snowy road sections provided by an embodiment of the present invention;

[0041] Figure 5 A functional module diagram of a device for predicting icy and snowy road sections provided by an embodiment of the present invention;

[0042] Figure 6 A schematic diagram of the hardware architecture of an electronic device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0043] To make the objectives, technical solutions, and advantages of the embodiments of the present invention more clear, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the embodiments described are only part of the embodiments of the present invention, not all of them. All other embodiments obtained by ordinary technicians in this field based on the embodiments of the present invention without making any creative efforts shall fall within the scope of protection of the present invention.

[0044] Currently, icy and snowy road sections generally rely on weather forecasts or image recognition sections for predictions and reminders. However, due to the accuracy of the weather forecast itself, the accuracy of the icy and snowy road section predictions is not high. In addition, due to the complex road conditions, image recognition methods cannot achieve accurate predictions for icy and snowy road sections.

[0045] Based on this, the embodiments of the present invention provide a method, device, electronic device and readable storage medium for predicting icy and snowy road sections, which accurately predict similar slippery icy and snowy road sections through weather and map category features, thereby effectively improving the user's driving experience.

[0046] To facilitate understanding of this embodiment, a method for predicting icy and snowy road sections disclosed in an embodiment of the present invention is first introduced in detail. This method can be applied to intelligent control devices such as the cloud and vehicle computers.

[0047] Figure 1 A flow chart of a method for predicting icy and snowy road sections provided by an embodiment of the present invention.

[0048] like Figure 1 As shown, the method includes the following steps:

[0049] Step S102 : determining historical slippery sections based on historical vehicle data collected by the global positioning system, the anti-lock braking system, and the electronic stability system.

[0050] Among them, the Global Positioning System (GPS) can obtain the vehicle's real-time position information, and the Anti-lock Braking System (ESP) and the Electronic Stability Program (ABS) can obtain the vehicle's real-time braking information; the historical vehicle data includes the historical vehicle's real-time position information and real-time braking information; the historical slippery section can be understood as the actual slippery section determined based on the historical vehicle data.

[0051] Step S104: determining weather characteristics and road section characteristics corresponding to historical slippery road sections based on map data and historical weather data.

[0052] Among them, map data can be obtained from the navigation APP, or from a database pre-stored in the vehicle computer or the cloud; historical weather data corresponding to historical moments are obtained through the Internet; it can be understood that the actual historical slippage section will correspond to the weather characteristics at that time and the section characteristics of the section.

[0053] Step S106 , predicting the slip probability of the current road section where the vehicle is to travel based on the weather characteristics and the road section characteristics.

[0054] Among them, by comparing the weather characteristics and road section characteristics corresponding to the historical slippery sections with the weather characteristics and road section characteristics corresponding to the current vehicle's road section to be traveled, the similarity between the historical slippery sections and the road section to be traveled can be determined, and then the slip probability of the current vehicle's road section to be traveled can be predicted.

[0055] In a preferred embodiment of actual application, the historical vehicle data collected by the sensors of each historical vehicle collection system can be used to determine the historical slippery sections, and then the section characteristics and weather characteristics corresponding to the historical slippery sections can be determined based on the map data and weather data. Based on the section characteristics and weather characteristics corresponding to the current section to be traveled by the vehicle, the similarity of the remaining historical slippery sections can be determined, and the slip probability of the section to be traveled can be predicted, which can achieve a more accurate prediction of ice and snow slippery sections, thereby effectively improving the user's driving experience.

[0056] In some embodiments, a more accurate prediction result can be obtained by determining the actual slippery road sections in history and using them as a criterion for the current vehicle to determine whether the road section to be traveled will slip. For example, the actual slippery road sections in history in step S102 can be determined by the following steps, including:

[0057] Step 1.1), collect the real-time location data of each historical vehicle based on the global positioning system.

[0058] Step 1.2) Collecting real-time braking data of each of the historical vehicles based on the anti-lock braking system and the vehicle electronic stability system.

[0059] It is understandable that the global positioning system, anti-lock braking system and electronic stability system are all installed in the corresponding historical vehicles, that is, each collection system can only collect the corresponding historical vehicle data by being installed in the historical vehicle.

[0060] Step 1.3) Based on the real-time braking data, determine whether there is a historical vehicle that has experienced skidding and the skidding time of the historical vehicle that has experienced skidding.

[0061] Among them, according to the real-time braking data, it is possible to know whether the historical vehicle has abnormal braking conditions. If the real-time braking data is abnormal, it can be determined that the historical vehicle has slipped. By comparing the abnormal time of the real-time braking data, the time when the historical vehicle slipped can be known.

[0062] Step 1.4): if so, determine the historical slipping section of each of the historical vehicles based on the slipping moment and the real-time position data corresponding to the slipping moment.

[0063] Here, the embodiment of the present invention achieves the prediction effect of the road section to be traveled based on the existence of historical slip sections; based on the combination of real-time position data and real-time braking data, the historical slip moments and sections of historical vehicles can be determined.

[0064] In some embodiments, in order to further achieve accurate prediction of icy and snowy road sections, the similarity between the historical slippery road section and the current road section to be traveled by the vehicle can be determined based on the road section characteristics and weather characteristics. For example, the method for determining the road section characteristics and weather characteristics in step S104 can be achieved by the following steps, including:

[0065] Step 2.1) Based on the real-time position data corresponding to the historical slippery road section, determine the road section features corresponding to the historical slippery road section from the map data.

[0066] Among them, the section features of the historical slippery section are obtained after the corresponding position of the slippery section is obtained from the map data, and then the section features corresponding to the historical slippery section are determined based on the surrounding (preset distance range) environmental features of the position in the map.

[0067] Step 2.2) Based on the slipping moment corresponding to the historical slipping section, determine the weather characteristics corresponding to the historical slipping section from the historical weather data corresponding to the slipping moment.

[0068] Here, the historical weather data corresponding to the moment of slipping is obtained, so that the weather conditions when slipping occurs can be known.

[0069] As an optional preferred embodiment, it is possible to determine whether the historical slippery section is an icy or snowy section based on the weather characteristics and section characteristics of the historical slippery section. In actual application, the slippage on some non-icy or snowy sections may be due to the braking defects of the historical vehicle itself, which causes the slippage abnormality, that is, the current vehicle may not have the slippage problem; in order to further ensure the user's driving experience and avoid the problem of excessive slippage reminders, the embodiment of the present invention can pre-screen and determine the icy and snowy sections caused by ice and snow, and remind of the slippage probability of such icy and snowy sections.

[0070] It should be noted that weather characteristics and road section characteristics include multiple feature categories respectively; for weather characteristics, they may include temperature category, humidity category, rain and snow category, wind direction category, wind speed category and lighting category, etc.; for road section characteristics, they may include road surface facility category (under bridge, on bridge, top block expressway), environmental landform category (river, rice field, pond), etc.

[0071] Generally speaking, icy and snowy sections of roads will occur in the early morning or late at night. At this time, vehicles may slip when passing through such icy and snowy sections. When light appears in the early morning and the temperature rises, the ice and snow covering the road section are more likely to melt, and the road section will no longer cause vehicles to slip. Such sections of roads where ice and snow will form in the early morning or late at night often have certain landform features, such as rivers, bridges, or relatively heavy humidity.

[0072] Therefore, the environmental characteristics and weather characteristics corresponding to each historical slippery road section can be compared with the preset environmental characteristics and preset weather characteristics of the above-mentioned icy and snowy road section; if the feature categories that match the historical slippery road section and the icy and snowy road section meet the requirements, the historical slippery road section can be determined as an icy and snowy road section; vice versa.

[0073] Based on the above embodiment, the similarity between the current road section to be traveled and the historical slippery road section can be evaluated by weather characteristics and road section characteristics, thereby realizing the slip probability prediction of the road section to be traveled, wherein the historical slippery road section includes icy and snowy road sections and non-icy and snowy road sections. For example, step S106 may include:

[0074] Step 3.1) Determine the similarity between the current road section to be traveled by the vehicle and the historical slippery road section based on the weather characteristics and the road section characteristics.

[0075] It should be noted that to further ensure the reliability of the slip probability prediction, the similarity between the current road section to be traveled and historical slippery sections can be determined based on each feature category of weather characteristics and road section characteristics, as well as the influence ratio of each feature category. Specifically, the following factors are included:

[0076] Step 3.1.1) Determine the weight ratio of each feature category based on the frequency of each feature category corresponding to each historical vehicle slip moment.

[0077] The influence ratio of each feature category can be determined based on the actual historical vehicle slippage situation, that is, the weight ratio is determined based on the frequency of occurrence of each feature category when slippage occurs; the greater the frequency of occurrence, the greater the corresponding weight ratio.

[0078] Step 3.1.2) Based on the feature category corresponding to the current road section to be traveled by the vehicle and the weight ratio, the similarity between the current road section to be traveled by the vehicle and the historical slippery road section is calculated.

[0079] For example, the weight ratio corresponding to feature category A illumination is 10%, the weight ratio corresponding to feature category B temperature is 20%, the weight ratio corresponding to feature category C humidity is 20%, the weight ratio corresponding to feature category D road facilities is 20%, and the weight ratio corresponding to feature category E environment and landform is 30%.

[0080] In actual application, if the road section to be traveled is consistent with any feature category of any historical slippery road section, the feature category is set to 1, otherwise it is set to 0; for example, the road section to be traveled is consistent with the feature categories B and C of the first historical slippery road section, and is consistent with the feature categories A, B, C and D of the second historical slippery road section, then the similarity between the road section to be traveled and the first historical slippery road section is: 0×10%+1×20%+1×20%+0×20%+0×30%=40%, and the similarity between the road section to be traveled and the second historical slippery road section is: 1×10%+1×20%+1×20%+1×20%+0×30%=70%.

[0081] Step 3.2) Based on the similarity, the slip probability of the current vehicle on the road section to be traveled is predicted.

[0082] As an optional embodiment, the road section to be traveled may be directly compared with various historical slippery road sections, and the highest similarity obtained is used as the slip probability of the road section to be traveled by the current vehicle.

[0083] Among them, if the similarity between the current road section to be traveled by the vehicle and the historical slipping road section is higher, the slip probability of the current road section to be traveled by the vehicle is predicted to be higher.

[0084] In some embodiments, to further enhance the user's driving experience, a reminder may be issued based on the aforementioned predicted slip probability so that the user can take a detour or take appropriate action. Exemplarily, the above method further includes:

[0085] Step 4.1): If the slip probability exceeds the slip threshold, a slip reminder is issued for the road section to be traveled.

[0086] In actual application, a road section to be traveled can be determined at a first preset distance from the current driving position. Weather and road characteristics are then obtained for the vehicle approaching the road section. Before the vehicle reaches a second preset distance from the road section, the slip probability for the road section is predicted. If the slip probability exceeds a threshold, a slip warning is promptly issued to the user, and the vehicle is controlled to take detours or reduce speed, thereby improving the user's driving experience. The second preset distance is smaller than the first preset distance, and both preset distances can be set based on actual conditions or user customization.

[0087] As some optional reminder methods, embodiments of the present invention Figure 2-Figure 4 A page display scene of a vehicle display screen is shown respectively;

[0088] Among them, during the actual driving process, if the road section to be traveled has the risk of slipping, the user will be reminded through the car display screen, such as Figure 2As shown, a message pops up saying that the road ahead is slippery 50 meters and that you should drive carefully. At the same time, a light-up sign is displayed on the road ahead.

[0089] In the actual driving process, the user may be in the entertainment and music function interface. When it is predicted that the road section 50 meters ahead has the risk of slipping, the slip reminder can be directly popped up on the interface to remind the user in time, such as Figure 3 shown.

[0090] As another application scenario, users can plan a driving route based on the starting point and destination, and estimate the weather characteristics and road section characteristics corresponding to the vehicle when driving on the planned route based on the current average driving speed of the vehicle, and then predict the slippery sections with slip risks in the planned route and highlight them in the planned route, such as Figure 4 As shown, at this time, the user can choose to re-plan the route based on the number, length and other information of the slippery section, or based on the reminder of the slippery section, the user can take a detour or slow down when driving on the corresponding section to ensure an improved driving experience for the user.

[0091] The embodiments of the present invention can predict slippery sections in advance in rainy and snowy weather, and control the vehicle to slow down or avoid them, thereby reducing driving risks. At the same time, the slip risk of the current section can be predicted based on weather information, achieving more accurate slip reminders than weather forecasts, thereby improving the user's driving experience.

[0092] like Figure 5 As shown, an embodiment of the present invention further provides an icy and snowy road section prediction device 500, which includes:

[0093] A first determining module 501 determines a historical slippery road section based on historical vehicle data collected by a global positioning system, an anti-lock braking system, and an electronic stability system;

[0094] A second determining module 502 determines weather characteristics and road section characteristics corresponding to the historically slippery road section based on map data and historical weather data;

[0095] The prediction module 503 predicts the slip probability of the current road section where the vehicle is to travel based on the weather characteristics and the road section characteristics.

[0096] In some embodiments, the first determination module 501 is further specifically used to collect real-time position data of each historical vehicle based on the global positioning system; collect real-time braking data of each historical vehicle based on the anti-lock braking system and the vehicle electronic stability system; based on the real-time braking data, determine whether there is a historical vehicle that has skidded and the slipping moment of the historical vehicle that has skidded; if so, determine the historical slipping section of each historical vehicle based on the slipping moment and the real-time position data corresponding to the slipping moment.

[0097] In some embodiments, the second determination module 502 is further specifically used to determine the section characteristics corresponding to the historical slippery section from the map data based on the real-time location data corresponding to the historical slippery section; and to determine the weather characteristics corresponding to the historical slippery section from the historical weather data corresponding to the slippery moment based on the slippery moment corresponding to the historical slippery section.

[0098] In some embodiments, the prediction module 503 is further specifically used to determine the similarity between the current road section to be traveled by the vehicle and the historical slippery road section based on the weather characteristics and the road section characteristics; and based on the similarity, predict the slip probability of the current road section to be traveled by the vehicle.

[0099] In some embodiments, the weather characteristics and the road section characteristics respectively include multiple feature categories; the prediction module 503 is further specifically used to determine the weight ratio of each feature category according to the frequency of each feature category corresponding to each historical vehicle slip moment; based on the feature category and the weight ratio corresponding to the current vehicle to travel section, calculate the similarity between the current vehicle to travel section and the historical slip section.

[0100] In some embodiments, the prediction module 503 is further specifically configured to predict that the probability of slipping on the current road section to be traveled by the vehicle is higher if the similarity between the current road section to be traveled by the vehicle and the historical slipping road section is higher.

[0101] In some embodiments, the device further includes a reminder module, which is further specifically configured to provide a slip reminder for the road section to be traveled if the slip probability exceeds a slip threshold.

[0102] Figure 6 Schematic diagram of the hardware architecture of the electronic device 300 provided in an embodiment of the present invention. Figure 6As shown, the electronic device 300 includes a machine-readable storage medium 301 and a processor 302. It may also include a non-volatile storage medium 303, a communication interface 304, and a bus 305. The machine-readable storage medium 301, the processor 302, the non-volatile storage medium 303, and the communication interface 304 communicate with each other via the bus 305. The processor 302 reads and executes the machine-executable instructions for predicting icy and snowy road sections in the machine-readable storage medium 301 to perform the icy and snowy road section prediction method described in the above embodiment.

[0103] The machine-readable storage medium referred to herein may be any electronic, magnetic, optical, or other physical storage device that can contain or store information, such as executable instructions, data, and the like. For example, the machine-readable storage medium may be: RAM (Random Access Memory), volatile memory, non-volatile memory, flash memory, a storage drive (such as a hard disk drive), any type of storage disk (such as a CD, DVD, etc.), or similar storage media, or a combination thereof.

[0104] The non-volatile medium may be a non-volatile memory, a flash memory, a storage drive (such as a hard drive), any type of storage disk (such as an optical disk, a DVD, etc.), or similar non-volatile storage medium, or a combination thereof.

[0105] It can be understood that the specific operation methods of each functional module in this embodiment can refer to the detailed description of the corresponding steps in the above method embodiment, and will not be repeated here.

[0106] The computer-readable storage medium provided in the embodiment of the present invention stores a computer program. When the computer program code is executed, the icy and snowy road section prediction method described in any of the above embodiments can be implemented. For specific implementation, please refer to the method embodiment, which will not be repeated here.

[0107] Those skilled in the art will clearly understand that, for the convenience and brevity of description, the specific working processes of the above-described systems and devices can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0108] In addition, in the description of the embodiments of the present invention, unless otherwise expressly specified or limited, the terms "mounted," "connected," and "connected" should be understood in a broad sense. For example, they may refer to fixed connections, detachable connections, or integral connections; they may refer to mechanical connections or electrical connections; they may refer to direct connections or indirect connections through an intermediate medium; and they may refer to internal communication between two components. Those skilled in the art will understand the specific meanings of the above terms in the present invention based on the specific circumstances.

[0109] In the description of the present invention, it should be noted that the terms "center," "upper," "lower," "left," "right," "vertical," "horizontal," "inner," and "outer," etc., indicating orientations or positional relationships, are based on the orientations or positional relationships shown in the accompanying drawings and are intended solely to facilitate and simplify the description of the present invention. They are not intended to indicate or imply that the devices or components referred to must have, be constructed, or operate in a specific orientation, and therefore should not be construed as limitations on the present invention. Furthermore, the terms "first," "second," and "third" are used for descriptive purposes only and should not be construed as indicating or implying relative importance.

[0110] Finally, it should be noted that the above-described embodiments are only specific implementation methods of the present invention, which are used to illustrate the technical solutions of the present invention rather than to limit them. The protection scope of the present invention is not limited thereto. Although the present invention has been described in detail with reference to the above-mentioned embodiments, ordinary technicians in this field should understand that any technician familiar with this technical field can still modify the technical solutions recorded in the above-mentioned embodiments within the technical scope disclosed by the present invention, or make equivalent replacements for some of the technical features therein; and these modifications, changes or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention, and should all be covered by the protection scope of the present invention.

Claims

1. A method for predicting icy and snowy road sections, characterized in that: The method comprises: Determine historical slippery sections based on historical vehicle data collected by the global positioning system, anti-lock braking system, and electronic stability system; Determining weather characteristics and road section characteristics corresponding to the historically slippery road section based on map data and historical weather data; Predicting the slip probability of the current road section to be traveled by the vehicle based on the weather characteristics and the road section characteristics; The step of determining weather characteristics and road section characteristics corresponding to the historical slippery road section based on map data and historical weather data includes: Determining a road section feature corresponding to the historical slippery road section from map data based on the real-time position data corresponding to the historical slippery road section; Based on the slipping moment corresponding to the historical slipping road section, determining the weather characteristics corresponding to the historical slipping road section from the historical weather data corresponding to the slipping moment; The road section feature is obtained by obtaining the corresponding position of the slippery road section from the map data, and then determining the road section feature corresponding to the slippery road section based on the environmental features of the preset distance range of the corresponding position of the slippery road section in the map.

2. The method according to claim 1, characterized in that The steps for determining historical skidding sections based on historical vehicle data collected by the global positioning system, the anti-lock braking system, and the electronic stability system include: Collect real-time location data of each historical vehicle based on the global positioning system; collecting real-time braking data of each of the historical vehicles according to an anti-lock braking system and an electronic stability system; Based on the real-time braking data, determining whether there is a historical vehicle that has experienced skidding and the skidding time of the historical vehicle that has experienced skidding; If so, the historical slipping section of each of the historical vehicles is determined according to the slipping time and the real-time position data corresponding to the slipping time.

3. The method according to claim 1, characterized in that The step of predicting the slip probability of the current road section to be traveled by the vehicle according to the weather characteristics and the road section characteristics includes: Determining, based on the weather characteristics and the road section characteristics, the similarity between the current road section to be traveled by the vehicle and the historical slippery road section; Based on the similarity, the slip probability of the current vehicle on the road section to be traveled is predicted.

4. The method according to claim 3, characterized in that The weather characteristics and the road section characteristics respectively include multiple feature categories; The step of determining the similarity between the current road section to be traveled by the vehicle and the historical slippery road section based on the weather characteristics and the road section characteristics includes: Determining a weight ratio of each feature category according to the frequency of each feature category corresponding to each historical vehicle slip moment; Based on the feature category corresponding to the current road section to be traveled by the vehicle and the weight ratio, the similarity between the current road section to be traveled by the vehicle and the historical slippery road section is calculated.

5. The method according to claim 3, characterized in that The step of predicting the slip probability of the current vehicle on the road section to be traveled based on the similarity includes: If the similarity between the current road section to be traveled by the vehicle and the historical slippery road section is higher, it is predicted that the slip probability of the current road section to be traveled by the vehicle is higher.

6. The method according to claim 1, characterized in that The method further comprises: If the slip probability exceeds a slip threshold, a slip reminder is issued for the road section to be traveled.

7. A device for predicting icy and snowy road sections, characterized in that: The device comprises: A first determination module determines a historical slippery road section based on historical vehicle data collected by a global positioning system, an anti-lock braking system, and an electronic stability system; A second determining module determines weather characteristics and road section characteristics corresponding to the historically slippery road section based on map data and historical weather data; A prediction module, which predicts the slip probability of the current road section to be traveled by the vehicle based on the weather characteristics and the road section characteristics; The second determination module determines the section characteristics corresponding to the historical slipping section from the map data based on the real-time position data corresponding to the historical slipping section; and determines the weather characteristics corresponding to the historical slipping section from the historical weather data corresponding to the slipping moment based on the slipping moment corresponding to the historical slipping section; wherein, the section characteristics are obtained by obtaining the corresponding position of the slipping section from the map data, and then determining the section characteristics corresponding to the slipping section based on the environmental characteristics of the preset distance range of the corresponding position of the slipping section in the map.

8. An electronic device comprising a memory and a processor, wherein the memory stores a computer program that can be run on the processor, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 6 are implemented.

9. A machine-readable storage medium, characterized in that The machine-readable storage medium stores machine-executable instructions. When the machine-executable instructions are called and executed by a processor, the machine-executable instructions prompt the processor to implement the steps of the method according to any one of claims 1 to 6.

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