Curve early-warning method and device for vehicle

Abstract

A curve early-warning method and device for a vehicle, which method and device relate to the technical field of automobile driver assistance, and are used for improving driving safety on curves. The method comprises: a sensor acquiring, in real time, road surface information in a driving direction of a vehicle; when a curve appears in the driving direction, on the basis of the road surface information and operation information of the vehicle, determining a curve risk level of the curve; and on the basis of the curve risk level, sending early-warning information to a driver of the vehicle.

Description

A method and device for warning of vehicle curves

[0001] This application claims priority to Chinese Patent Application No. 202510360214.8, filed on March 25, 2025, entitled "A Method and Device for Warning of Curves of a Vehicle", the entire contents of which are incorporated herein by reference. Technical Field

[0002] This application relates to the field of automotive driver assistance technology, and in particular to a method and device for warning of vehicle curves. Background Technology

[0003] With the rapid development of new energy vehicles, the role and proportion of assisted driving are becoming increasingly important. Vehicle centering control is an important component of L2-level lane assist functions, and cornering ability under vehicle centering control is a key indicator. During cornering, lateral control instability can easily occur due to factors such as perception, road conditions, and vehicle control, causing the vehicle to veer into the opposite lane or overturn, thus further leading to safety accidents.

[0004] Therefore, it is necessary to provide a vehicle curve warning method to improve driving safety on curves. Summary of the Invention

[0005] This application provides a method and device for warning of vehicle curves. The following is an overview of the subject matter described in detail in this application. This overview is not intended to limit the scope of the claims.

[0006] Firstly, this application provides a method for warning of vehicle curves, comprising:

[0007] Real-time road information about the vehicle in the driving direction is obtained through sensors;

[0008] When a curve appears in the driving direction, the curve risk level is determined based on road surface information and vehicle operation information.

[0009] Warning messages are issued to drivers of vehicles based on the risk level of the curve.

[0010] Secondly, this application provides a vehicle curve warning device, comprising:

[0011] The acquisition unit is configured to acquire road surface information of the vehicle in the driving direction in real time through sensors;

[0012] The processing unit is configured to determine the curve risk level based on road surface information and vehicle operation information when a curve appears in the driving direction.

[0013] The warning unit is configured to issue warning information to the driver of the vehicle based on the risk level of the curve.

[0014] Thirdly, this application provides a computer device, including: a memory and a processor, which are communicatively connected to each other. The memory stores computer instructions, and the processor executes the computer instructions to perform the vehicle curve warning method of the first aspect or any corresponding embodiment described above.

[0015] Fourthly, this application provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the vehicle curve warning method as described in any of the preceding claims.

[0016] Fifthly, this application provides a computer program product or computer program, the computer program product including a computer program stored in a computer-readable storage medium; the processor of the computer device reads the computer program from the computer-readable storage medium, and the processor executes the computer program to implement the vehicle curve warning method as described in any of the preceding claims.

[0017] As can be seen from the above technical solution, this application provides a method and device for vehicle curve warning. The method first acquires road surface information in the driving direction in real time using sensors. Then, when a curve appears in the driving direction, the curve risk level is determined based on the road surface information and the vehicle's operating information. Finally, a warning message is issued to the driver based on the curve risk level. The ability to acquire road surface data in real time through sensors makes the judgment of curve risk more timely and accurate. Simultaneously, informing the driver of the curve risk level allows the driver to react in advance, improving driving safety. Attached Figure Description

[0018] To more clearly illustrate the technical solutions in this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only embodiments of this application. For those skilled in the art, other drawings can be obtained based on the provided drawings without creative effort.

[0019] Figure 1 is a flowchart illustrating a vehicle curve warning method provided in an embodiment of this application;

[0020] Figure 2 is a schematic flowchart of a vehicle curve warning method provided in an embodiment of this application;

[0021] Figure 3 is a schematic flowchart of another vehicle curve warning method provided in an embodiment of this application;

[0022] Figure 4 is a schematic flowchart of another vehicle curve warning method provided in the embodiment of this application;

[0023] Figure 5 is a structural schematic diagram of a vehicle curve warning device provided in an embodiment of this application;

[0024] Figure 6 is a schematic diagram of the structure of an electronic device provided in an embodiment of this application. Detailed Implementation

[0025] Unless otherwise defined, the technical or scientific terms used in the embodiments of this application shall have the ordinary meaning understood by those skilled in the art to which this application pertains. The terms "first," "second," and similar terms used in the embodiments of this application do not indicate any order, quantity, or importance, but are merely used to avoid confusion of the constituent elements.

[0026] Unless the context otherwise requires, throughout this application, "a plurality of" means "at least two," and "including" is interpreted as open-ended or encompassing, i.e., "including, but not limited to." In the description of the application, terms such as "one embodiment," "some embodiments," "exemplary embodiment," "example," "specific example," or "some examples" are intended to indicate that a particular feature, structure, material, or characteristic associated with that embodiment or example is included in at least one embodiment or example of this application. The illustrative representations of the above terms do not necessarily refer to the same embodiment or example.

[0027] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0028] Overview

[0029] With the rapid development of new energy vehicles, the role and proportion of assisted driving are increasing. Vehicle centering control is an important component of Level 2 lane assist functions, and cornering ability under vehicle centering control is a key feature. Level 2 lane-level assisted driving is an advanced driver assistance system (ADAS) that provides a certain degree of automation assistance to the driver, but still requires the driver to maintain control and attention of the vehicle. At Level 2, the driver's hands must be on the steering wheel and be ready to take over vehicle operation at any time. This is because, despite these assistance functions, the vehicle cannot handle all possible road conditions or emergencies. In cornering, lateral instability can easily occur due to perception, road conditions, and vehicle control issues, causing the vehicle to drift into the oncoming lane or roll over, further leading to accidents.

[0030] However, in related technologies, cornering control mainly relies on high-precision maps, which have poor perception of environmental changes. At the same time, there is no method to inform the driver of risks in cornering control, which makes it impossible to effectively remind the driver to intervene in time when there is a risk of cornering, resulting in reduced driving safety.

[0031] Therefore, it is necessary to provide a method for warning vehicles when they are cornering.

[0032] In this technical solution, road surface information in the driving direction is first acquired in real time using sensors. Then, when a curve appears in the driving direction, the curve risk level is determined based on the road surface information and the vehicle's operating information. Finally, a warning message is issued to the driver based on the curve risk level. The ability to acquire road surface data in real time through sensors makes the assessment of curve risks more timely and accurate, while informing the driver of the curve risk level allows the driver to react in advance, improving driving safety.

[0033] The following is an exemplary description of the vehicle curve warning method provided in the embodiments of this application.

[0034] Exemplary methods

[0035] This application provides a method for warning of vehicle curves, as shown in Figure 1, including:

[0036] S101. Real-time acquisition of road surface information of the vehicle in the driving direction through sensors.

[0037] In practice, the vehicle is equipped with sensors for acquiring road information in real time. These sensors can be cameras, radar, or other sensors, used to capture road data in front of the vehicle and obtain road information in the driving direction, providing reliable data support for subsequent driving decisions and warnings.

[0038] S102. When a curve appears in the driving direction, the curve risk level is determined based on road surface information and vehicle operation information.

[0039] In one embodiment, the presence of lane markings at the curve is first determined based on road surface information, and then the risk level of the curve is further determined based on the presence or absence of lane markings.

[0040] When there are no lane markings at a curve, the risk is relatively higher, and the corresponding curve risk level is higher. The lateral speed risk value of the vehicle can be determined based on road surface and operational information. This lateral speed risk value is used as the criterion for judging the curve risk level. Specifically, if the lateral speed risk value is lower than a preset risk threshold, the curve risk level is medium; if the lateral speed risk value is higher than the preset risk threshold, the curve risk level is high.

[0041] When lane markings exist at the curve, the vehicle speed and comfortable cornering speed are determined based on road surface and operational information. Then, based on these speeds, the curve's risk level is determined. Specifically, when the vehicle speed is less than or equal to the comfortable cornering speed, the curve's risk level is directly determined as low risk. When the vehicle speed exceeds the comfortable cornering speed, the curve's risk level is further determined based on the maximum lateral acceleration value, the vehicle's lateral adhesion coefficient, the comfortable cornering coefficient, and a risk speed threshold. The risk speed threshold includes a first risk speed threshold and a second risk speed threshold. The first risk speed threshold and the second... The value of the risk speed threshold can be set according to actual needs. In one embodiment, the values ​​of the first risk speed threshold and the second risk speed threshold are both determined based on the vehicle's own operating information and the road surface information including the curve. For the first risk speed threshold, the parameters for calculation include the maximum lateral acceleration value, the maximum deceleration value of the curve, the travel distance between the current position and the position of the maximum lateral acceleration value, and the curve radius. For the second risk speed threshold, the parameters for calculation include the lateral adhesion coefficient, the maximum deceleration value of the curve, the travel distance between the position of the maximum lateral acceleration value, and the curve radius. The specific judgment method is as follows: First, determine the relationship between the lateral adhesion coefficient and the ratio of the maximum lateral acceleration value to the gravitational acceleration. When the lateral adhesion coefficient is greater than or equal to the ratio of the maximum lateral acceleration value to the gravitational acceleration, and the vehicle speed is greater than the first risk speed threshold, the cornering risk level is determined to be high risk. When the lateral adhesion coefficient is greater than or equal to the ratio of the maximum lateral acceleration value to the gravitational acceleration, and the vehicle speed is less than or equal to the first risk speed threshold, the cornering risk level is determined to be medium risk. When the lateral adhesion coefficient is less than the ratio of the maximum lateral acceleration value to the gravitational acceleration, and the vehicle speed is greater than the second risk speed threshold, the cornering risk level is determined to be high risk. When the lateral adhesion coefficient is less than the ratio of the maximum lateral acceleration value to the gravitational acceleration, and the vehicle speed is less than or equal to the second risk speed threshold, the cornering risk level is determined to be medium risk.

[0042] S103. Issue warning information to the driver of the vehicle based on the risk level of the curve.

[0043] In practice, the risk level of the upcoming curve is analyzed through the above steps, and then a corresponding warning message is issued to the driver to remind them to drive safely. Specifically, the warning may be one or more of visual, auditory, tactile, or voice warnings; this application embodiment does not limit this.

[0044] This embodiment provides a method for providing curve warning for vehicles. First, sensors acquire real-time road surface information in the driving direction. Then, when a curve appears in the driving direction, the curve risk level is determined based on the road surface information and vehicle operation information. Finally, a warning message is issued to the driver based on the curve risk level. The ability to acquire road surface data in real-time via sensors makes the assessment of curve risk more timely and accurate. Simultaneously, informing the driver of the curve risk level allows for advance warning, improving driving safety.

[0045] This application provides a specific flowchart of a vehicle curve warning method, as shown in Figure 2, including:

[0046] S201. Real-time road information of the vehicle in the driving direction is obtained through sensors.

[0047] In practice, the vehicle is equipped with a sensor for acquiring road information in real time. This sensor can be a camera sensor, used to capture image data of the road surface in front of the vehicle. In one embodiment, the camera sensor can be installed at the front of the vehicle, usually behind the windshield, to ensure that its field of vision is not obstructed and that it can clearly capture the road conditions in the direction of the vehicle's travel.

[0048] Specifically, camera-type sensors can acquire real-time road images through their built-in image processing units and transmit these image data to the vehicle's control unit or central processing system. The control unit processes and analyzes the received image data to extract key road information, such as lane lines, obstacles, potholes, and traffic signs, providing reliable data support for subsequent driving decisions and alerts.

[0049] In one example, camera sensors can be combined with other types of sensors (such as radar, lidar, etc.) to improve the accuracy and robustness of road information acquisition. For instance, radar sensors can be used to detect the distance and speed of obstacles ahead, while lidar can provide more precise three-dimensional road information. Through multi-sensor fusion technology, vehicles can perceive their surroundings more comprehensively, thereby improving driving safety and the performance of autonomous driving systems.

[0050] S202. If it is determined from the road surface information that there are no lane markings at the curve, determine the lateral speed risk value of the vehicle based on the road surface information and the operation information.

[0051] In practice, if lane markings are absent, the vehicle will inevitably lose stability and veer off the lane without driver intervention. Furthermore, when lateral speed is excessive, the vehicle will quickly veer off the lane the moment it loses control. Therefore, curves generally have a higher risk level. The specific judgment logic is as follows:

[0052] First, the lateral speed risk value of the vehicle is determined based on road surface information and operational information. The expression for the lateral speed risk value is as follows:

[0053] Wherein, the vehicle's driving speed is V1, the comfortable cornering speed is V′, a is the maximum deceleration value for cornering (this speed value can be a calibrated value or set according to safety and comfort; this embodiment does not limit this), x is the driving distance between the current position and the position where the lane line is lost, lateral speed is the main manifestation of the strength of cornering control and an important indicator for judging the risk of instability after lane line loss; based on the actual performance of the vehicle, the threshold speeds for medium and high risk are calibrated as V0. Let be the angle between the vehicle speed and the vehicle's y-axis. If the vehicle does not need to slow down, then If the vehicle speed is too high, it needs to slow down. Slowing down can be divided into two categories. The first is slowing down to a comfortable cornering speed before reaching the curve. In this case, the vehicle will not continue to slow down after reaching the comfortable cornering speed. If the maximum deceleration is insufficient to reach a comfortable cornering speed, this falls under the second scenario. In this case, the vehicle will continue to decelerate until the lane markings are lost.

[0054] S203. If the lateral speed risk value is lower than the preset risk threshold, the curve risk level is determined to be medium risk level; if the lateral speed risk value is higher than the preset risk threshold, the curve risk level is determined to be high risk level.

[0055] In practice, if the lateral speed risk value is lower than the preset risk threshold, that is: At this point, the curve's risk level is determined to be medium risk. If the lateral speed risk value is not lower than the preset risk threshold, that is: If this is not the case, then the curve's risk level is determined to be high risk.

[0056] S204. Issue warning information to the driver of the vehicle based on the risk level of the curve.

[0057] In practice, the risk level of the upcoming curve is analyzed through the above steps, and then a corresponding warning message is issued to the driver to remind them to drive safely. Specifically, the warning may be one or more of visual, auditory, tactile, or voice warnings; this application embodiment does not limit this.

[0058] In one example, an alert can be issued as follows:

[0059] When visual warnings are triggered, a yellow warning icon is displayed on the vehicle's dashboard or head-up display (HUD) for low-risk situations, such as "Curve ahead, please slow down." For medium-risk situations, an orange warning icon is displayed, flashing with the message "Sharp curve ahead, please slow down." For high-risk situations, a red warning icon is displayed, accompanied by the text "Danger curve, please slow down immediately!"

[0060] When an auditory warning is issued, a short, sharp beep is emitted for low-risk situations. For medium-risk situations, a series of moderately oriented beeps are emitted. For high-risk situations, a high-frequency, rapid alarm sound is emitted to draw the driver's full attention.

[0061] When a tactile warning is triggered, the steering wheel vibrates slightly once for low-risk situations. For medium-risk situations, the steering wheel vibrates continuously to alert the driver. For high-risk situations, the seat or seatbelt vibrates, while the steering wheel vibrates strongly to enhance the warning effect.

[0062] When a voice warning is issued, the voice prompts are as follows: Low risk: "Curve ahead, please pay attention to your speed." Medium risk: "Sharp curve ahead, please slow down." High risk: "Danger curve, please slow down immediately!"

[0063] In this step, response measures can also be taken simultaneously with the warning information, as shown in one example:

[0064] When a low-risk warning is issued, the system will display a yellow warning icon on the instrument panel or HUD and emit a short warning sound as the vehicle approaches a low-risk curve. At this time, the driver only needs to adjust the vehicle speed accordingly.

[0065] When a medium-risk warning is issued, the system will display an orange warning icon and emit a continuous warning sound as the vehicle approaches a medium-risk curve. Simultaneously, the steering wheel may vibrate slightly to remind the driver to slow down.

[0066] When a high-risk warning is triggered, the system will display a red warning icon and emit a high-frequency, rapid alarm sound as the vehicle approaches a high-risk curve. The steering wheel and seat may vibrate strongly, and a voice prompt will say, "Dangerous curve, please slow down immediately!" At this time, the driver must immediately take measures to slow down to ensure safe passage through the curve.

[0067] Through the aforementioned early warning mechanism, vehicles can provide drivers with timely and effective warning information on curves of different risk levels, thereby reducing the probability of accidents and improving driving safety.

[0068] In this step, the warning information can also be sent to surrounding vehicles or pedestrians via external vehicle devices (such as flashing headlights). Furthermore, the system can integrate with the vehicle's autonomous driving function to automatically reduce speed or adjust the driving trajectory if the driver fails to respond to the warning in a timely manner, further ensuring safety.

[0069] This application provides a specific flowchart of a vehicle curve warning method, as shown in Figure 3, including:

[0070] S301: Real-time acquisition of road surface information in the driving direction of the vehicle through sensors.

[0071] Steps S301 and S304 are the same as steps S201 and S204, and will not be repeated here.

[0072] S302. If lane markings are determined at the curve based on road surface information, determine the vehicle speed and comfortable cornering speed based on road surface information and driving information.

[0073] In practice, assuming the lane markings are not lost, the risk assessment of instability is primarily based on the vehicle's speed and comfortable cornering speed. Specifically, if the comfortable cornering coefficient is set to K, then the comfortable cornering lateral acceleration is Kug, where u is the vehicle's actual lateral adhesion coefficient (this value can be calculated using an adhesion coefficient measuring instrument or other methods; this application does not limit this), g is the acceleration due to gravity, the vehicle's speed is V1, a is the maximum deceleration value during the curve, x is the distance traveled from the current position to the position with the maximum lateral acceleration, R is the curve radius, and the comfortable cornering speed is...

[0074] The actual lateral adhesion coefficient (ALC) is a parameter describing the magnitude of friction between a vehicle's tires and the road surface, and it is crucial for evaluating vehicle safety and performance. In wet, snowy, or other special road conditions, understanding the ALC can help drivers adjust their driving behavior or optimize vehicle control strategies in autonomous driving systems. Measuring the ALC is typically not done directly but estimated indirectly. Examples include measurements using portable tribometers, sensor-based real-time monitoring systems, and laser texture analyzers; this application does not limit the specific methods used. It is worth noting that in many cases, multiple methods and technologies are combined to ensure accuracy. For autonomous vehicles, accurately perceiving the environment and understanding road conditions is essential; therefore, they often integrate more complex sensing systems and algorithms to achieve this. Furthermore, since the ALC is affected by various factors such as weather, temperature, and humidity, dynamically updated and adaptive algorithms are also critical.

[0075] S303. Determine the corner risk level based on vehicle speed and comfortable cornering speed.

[0076] In practice, first compare the vehicle speed with the comfortable cornering speed. When the vehicle speed is less than or equal to the comfortable cornering speed, that is... , the curve risk level of the curve is determined as a low risk level. If the above formula is not satisfied, further judgment is performed using risk speed thresholds, wherein the risk speed thresholds include a first risk speed threshold and a second risk speed threshold. In one embodiment, the first risk speed threshold depends on the maximum lateral acceleration value, the maximum deceleration value for curve deceleration, the driving distance from the current position to the position where the maximum lateral acceleration occurs, and the curve radius, and the specific expression thereof is The second risk speed threshold depends on the lateral adhesion coefficient, the maximum deceleration value, the driving distance and the curve radius, and the specific expression is wherein the maximum lateral acceleration is a ymax , and the meanings of other parameters are the same as those in step S302. a ymax is generally locked after evaluation on a high-adhesion road surface, so on a high-adhesion road surface, a ymax <ug, but in rainy and snowy weather, the road adhesion coefficient will be much lower than the high-adhesion coefficient, which will cause a ymax >ug.

[0077] When the lateral adhesion coefficient is greater than or equal to the ratio of the maximum lateral acceleration value to the gravitational acceleration, that is, a ymax ≤ug, it indicates that the maximum lateral acceleration value carried by the current road adhesion coefficient is greater than the maximum lateral acceleration value set by the algorithm. At this time, if the vehicle speed is greater than the first risk speed threshold, that is then the curve risk level of the curve is determined as a high risk level; if a ymax ≤ug, but at this time exceeds the comfortable cornering speed, the curve risk level of the curve is determined as a medium risk level.

[0078] When the lateral adhesion coefficient is less than the ratio of the maximum lateral acceleration value to the gravitational acceleration, that is, a ymax >ug, it indicates that the maximum lateral acceleration value carried by the road adhesion coefficient is less than the maximum lateral acceleration value set by the algorithm. At this time, if the vehicle speed is greater than the second risk speed threshold, that is there must be a vehicle instability risk, and the curve risk level of the curve is determined as a high risk level. If a ymax >ug, but at this time exceeds the comfortable cornering speed, the curve risk level of the curve is determined as a medium risk level.

[0079] S304: Send early warning information to the driver of the vehicle according to the curve risk level.

[0080] This application embodiment also provides a specific flow of a vehicle curve warning method, as shown in Figure 4. First, road surface information is acquired through a camera. Then, based on the road surface information, it is determined whether lane lines exist. If no lane lines exist, logic 1 is performed, which involves comparing the lateral speed risk value with a preset risk threshold, i.e., min... If lane markings exist, proceed to logic 2, which involves determining the vehicle speed and the optimal cornering speed (V1 and V0). exist When the lateral adhesion coefficient is greater than the ratio of the maximum lateral acceleration to the gravitational acceleration, that is, when comparing a... ymax and ug, a ymax When the speed exceeds the threshold, logic 3 is executed; if it fails, logic 4 is executed. Logic 3 compares the vehicle speed to the second risk speed threshold, and logic 4 compares the vehicle speed to the first risk speed threshold. This method determines the risk level of the curve and alerts the driver.

[0081] Exemplary device

[0082] In one exemplary embodiment of this application, a vehicle curve warning device 500 is also provided, as shown in FIG5, comprising:

[0083] The acquisition unit 501 is configured to acquire road surface information of the vehicle in the driving direction in real time through sensors;

[0084] The processing unit 502 is configured to determine the curve risk level of a curve based on road surface information and vehicle operation information when a curve appears in the driving direction.

[0085] The warning unit 503 is configured to issue warning information to the driver of the vehicle based on the risk level of the curve.

[0086] In one embodiment, the processing unit 502 is specifically configured as follows:

[0087] If it is determined from the road surface information that there are no lane markings at the curve, determine the lateral speed risk value of the vehicle based on the road surface information and the operation information.

[0088] If the lateral speed risk value is lower than the preset risk threshold, the curve risk level is determined to be medium risk level; if the lateral speed risk value is not lower than the preset risk threshold, the curve risk level is determined to be high risk level.

[0089] In one embodiment, the processing unit 502 is further configured to:

[0090] If lane markings are confirmed at the curve based on road information, determine the vehicle speed and comfortable cornering speed based on road information and driving information.

[0091] The cornering risk level is determined based on vehicle speed and comfortable cornering speed.

[0092] In one embodiment, the processing unit 502 is specifically configured as follows:

[0093] When the vehicle speed is less than or equal to the comfortable cornering speed, the corner risk level is determined to be low risk level.

[0094] When the vehicle speed is greater than the comfortable cornering speed, the cornering risk level is determined based on the maximum lateral acceleration value, the vehicle's lateral adhesion coefficient, the comfortable cornering coefficient, and the risk speed threshold.

[0095] In one implementation, the risk speed threshold includes a first risk speed threshold and a second risk speed threshold, and the processing unit 502 is specifically configured as follows:

[0096] When the lateral adhesion coefficient is greater than or equal to the ratio of the maximum lateral acceleration value to the gravitational acceleration, the curve risk level is determined based on the first risk speed threshold.

[0097] When the lateral adhesion coefficient is less than the ratio of the maximum lateral acceleration value to the gravitational acceleration, the curve risk level is determined based on the second risk speed threshold.

[0098] In one embodiment, the processing unit 502 is specifically configured as follows:

[0099] When the lateral adhesion coefficient is greater than or equal to the ratio of the maximum lateral acceleration value to the gravitational acceleration, and the vehicle speed is greater than the first risk speed threshold, the curve risk level is determined to be high risk level.

[0100] When the lateral adhesion coefficient is greater than or equal to the ratio of the maximum lateral acceleration value to the gravitational acceleration, and the vehicle speed is less than or equal to the first risk speed threshold, the curve risk level is determined to be medium risk level.

[0101] In one embodiment, the processing unit 502 determines a first risk speed threshold based on the maximum lateral acceleration value, the maximum deceleration value of the curve, the travel distance between the current position and the position of the maximum lateral acceleration value, and the curve radius.

[0102] In one embodiment, the processing unit 502 is further configured to:

[0103] When the lateral adhesion coefficient is less than the ratio of the maximum lateral acceleration value to the gravitational acceleration, and the vehicle speed is greater than the second risk speed threshold, the curve risk level is determined to be high risk level.

[0104] When the lateral adhesion coefficient is less than the ratio of the maximum lateral acceleration value to the gravitational acceleration, and the vehicle speed is less than or equal to the second risk speed threshold, the curve risk level is determined to be medium risk level.

[0105] In one embodiment, the processing unit 502 determines a second risk speed threshold based on the lateral adhesion coefficient, the maximum deceleration value, the travel distance, and the curve radius.

[0106] The vehicle curve warning device provided in this embodiment belongs to the same concept as the vehicle curve warning method provided in the above embodiments of this application. It can execute the vehicle curve warning method provided in any of the above embodiments of this application and has the corresponding functional modules and beneficial effects for executing the vehicle curve warning method. Technical details not described in detail in this embodiment can be found in the specific processing content of the vehicle curve warning method provided in the above embodiments of this application, and will not be repeated here.

[0107] Exemplary device

[0108] In one exemplary embodiment of this application, an electronic device is also provided, as shown in FIG6. This electronic device may include: a processor 610, a communications interface 620, a memory 630, and a communication bus 640, wherein the processor 610, the communications interface 620, and the memory 630 communicate with each other via the communication bus 640. The processor 610 may invoke logical instructions in the memory 630 to execute a vehicle curve warning method, the method including:

[0109] Real-time road information about the vehicle in the driving direction is obtained through sensors;

[0110] When a curve appears in the driving direction, the curve risk level is determined based on road surface information and vehicle operation information.

[0111] Warning messages are issued to drivers of vehicles based on the risk level of the curve.

[0112] Furthermore, the logical instructions in the aforementioned memory 630 can be implemented as software functional units and, when sold or used as independent products, can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or a portion of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0113] Exemplary computer program products and storage media

[0114] In addition to the methods and devices described above, the vehicle curve warning method provided in the embodiments of this application can also be a computer program product, which includes computer program instructions that, when executed by a processor, cause the processor to perform the steps in the vehicle curve warning method according to various embodiments of this application as described in the "Exemplary Methods" section above.

[0115] The computer program product can be written in any combination of one or more programming languages ​​to perform the operations of the embodiments of this application. The programming languages ​​include object-oriented programming languages ​​such as Java and C++, as well as conventional procedural programming languages ​​such as the "C" language or similar programming languages.

[0116] Furthermore, embodiments of this application also provide a computer-readable storage medium having a computer program stored thereon, the computer program being executed by a processor of the steps in the vehicle curve warning method according to various embodiments of this application as described in the "Exemplary Methods" section above.

[0117] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium, and when executed, it can include the processes of the embodiments of the above methods. Any references to memory, storage, databases, or other media used in the embodiments provided in this application can include non-volatile and / or volatile memory. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memory can include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in various forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), dual data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), Rambus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), etc.

[0118] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this application.

[0119] The embodiments described above are merely illustrative of several implementation methods of this application, and while the descriptions are specific and detailed, they should not be construed as limiting the scope of the solutions provided in the embodiments of this application. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this application, and these all fall within the protection scope of this application. Therefore, the protection scope of this patent application should be determined by the appended claims.

Claims

1. A method for warning of vehicle curves, comprising: Real-time road information about the vehicle in the driving direction is obtained through sensors; When a curve appears in the driving direction, the curve risk level is determined based on the road surface information and the vehicle's operating information. A warning message is issued to the driver of the vehicle based on the curve risk level.

2. The method according to claim 1, wherein, Determining the curve risk level based on the road surface information and the vehicle's operating information includes: If, based on the road surface information, it is determined that there are no lane markings at the curve, the lateral speed risk value of the vehicle is determined based on the road surface information and the operation information. If the lateral speed risk value is lower than a preset risk threshold, the curve risk level is determined to be medium risk; if the lateral speed risk value is not lower than the preset risk threshold, the curve risk level is determined to be high risk.

3. The method according to claim 1, wherein, Determining the curve risk level based on the road surface information and the vehicle's operating information includes: If lane markings are determined to exist at the curve based on the road surface information, the vehicle speed and comfortable cornering speed are determined based on the road surface information and the operating information. The cornering risk level of the curve is determined based on the vehicle speed and the comfortable cornering speed.

4. The method according to claim 3, wherein, Determining the corner risk level based on the vehicle speed and the comfortable cornering speed includes: When the vehicle speed is less than or equal to the comfortable cornering speed, the corner risk level is determined to be low risk level. When the vehicle speed is greater than the comfortable cornering speed, the cornering risk level of the curve is determined based on the maximum lateral acceleration value, the vehicle's lateral adhesion coefficient, the comfortable cornering coefficient, and the risk speed threshold.

5. The method according to claim 4, wherein, The risk speed threshold includes a first risk speed threshold and a second risk speed threshold. Determining the cornering risk level based on the maximum lateral acceleration value, the vehicle's lateral adhesion coefficient, the comfort cornering coefficient, and the risk speed threshold includes: When the lateral adhesion coefficient is greater than or equal to the ratio of the maximum lateral acceleration value to the gravitational acceleration, the curve risk level of the curve is determined based on the first risk speed threshold. When the lateral adhesion coefficient is less than the ratio of the maximum lateral acceleration value to the gravitational acceleration, the curve risk level of the curve is determined based on the second risk speed threshold.

6. The method according to claim 5, wherein, When the lateral adhesion coefficient is greater than or equal to the ratio of the maximum lateral acceleration value to the gravitational acceleration, determining the curve risk level of the curve based on the first risk speed threshold includes: When the lateral adhesion coefficient is greater than or equal to the ratio of the maximum lateral acceleration value to the gravitational acceleration, and the vehicle speed is greater than the first risk speed threshold, the curve risk level is determined to be high risk level. When the lateral adhesion coefficient is greater than or equal to the ratio of the maximum lateral acceleration value to the gravitational acceleration, and the vehicle speed is less than or equal to the first risk speed threshold, the curve risk level is determined to be medium risk level.

7. The method according to claim 6, wherein, The first risk speed threshold is determined based on the maximum lateral acceleration value, the maximum deceleration value of the curve, the travel distance between the current position and the position of the maximum lateral acceleration value, and the curve radius.

8. The method according to claim 5, wherein, When the lateral adhesion coefficient is less than the ratio of the maximum lateral acceleration value to the gravitational acceleration, determining the curve risk level of the curve based on the second risk speed threshold includes: When the lateral adhesion coefficient is less than the ratio of the maximum lateral acceleration value to the gravitational acceleration, and the vehicle speed is greater than the second risk speed threshold, the curve risk level is determined to be high risk level. When the lateral adhesion coefficient is less than the ratio of the maximum lateral acceleration value to the gravitational acceleration, and the vehicle speed is less than or equal to the second risk speed threshold, the curve risk level is determined to be medium risk level.

9. The method according to claim 8, wherein, The second risk speed threshold is determined based on the lateral adhesion coefficient, the maximum deceleration value, the travel distance, and the curve radius.

10. A vehicle curve warning device, comprising: The acquisition unit is configured to acquire road surface information of the vehicle in the driving direction in real time through sensors; The processing unit is configured to determine the curve risk level of a curve based on the road surface information and the vehicle's operating information when a curve appears in the driving direction. The warning unit is configured to issue a warning message to the driver of the vehicle based on the curve risk level.

11. A computer device, comprising: A memory and a processor are communicatively connected, the memory storing computer instructions, and the processor executing the computer instructions to perform the vehicle curve warning method according to any one of claims 1 to 9.

12. A computer-readable storage medium having a computer program stored thereon, the computer program being executed by a processor as a method for warning of a vehicle's curves according to any one of claims 1 to 9.

13. A computer program product comprising computer program instructions, which, when executed by a processor, cause the processor to perform the curve warning method for a vehicle according to any one of claims 1 to 9.

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