Vehicle curve early warning method and device

Abstract

The invention provides a curve early warning method and device for a vehicle, relates to the technical field of automobile auxiliary driving, and aims to improve the curve driving safety. The method comprises the steps that road surface information of a vehicle in the driving direction is obtained in real time through a sensor; when a curve appears in the driving direction, determining a curve risk level of the curve based on the road surface information and the running information of the vehicle; and sending early warning information to a driver of the vehicle according to the curve risk level.

Description

Technical Field

[0001] This application relates to the technical field of vehicle assisted driving, and particularly to a curve warning method and device for a vehicle. Background Art

[0002] With the rapid development of new energy vehicles, the role and proportion of assisted driving are becoming increasingly large. Vehicle centering control is an important part of the L2-level lane assist function, and the cornering ability under vehicle centering control is an important manifestation point. In curve control, it is very easy to cause lateral control instability due to reasons such as perception, road, and vehicle control, resulting in situations such as driving onto the oncoming lane or vehicle rollover, which further leads to the occurrence of safety accidents.

[0003] Therefore, it is necessary to provide a curve warning method for a vehicle to improve the safety of curve driving. Summary of the Invention

[0004] To solve the above technical problems, this application provides a curve warning method and device for a vehicle to improve the safety of curve driving.

[0005] To achieve the above technical purpose, this application provides the following technical solutions:

[0006] In a first aspect, an embodiment of this specification provides a curve warning method for a vehicle, including:

[0007] Obtaining road surface information of the vehicle in the driving direction in real time through a sensor;

[0008] When a curve appears in the driving direction, determining the curve risk level of the curve based on the road surface information and the running information of the vehicle;

[0009] Sending a warning message to the driver of the vehicle according to the curve risk level.

[0010] In a second aspect, an embodiment of this specification provides a curve warning device for a vehicle, including:

[0011] An obtaining unit, configured to obtain road surface information of the vehicle in the driving direction in real time through a sensor;

[0012] A processing unit, configured to determine the curve risk level of the curve based on the road surface information and the running information of the vehicle when a curve appears in the driving direction;

[0013] A warning unit, configured to send a warning message to the driver of the vehicle according to the curve risk level.

[0014] In a third aspect, the present invention provides a computer device, comprising: 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 method for warning of a curve of a vehicle according to the first aspect or any corresponding embodiment thereof.

[0015] In a fourth aspect, an embodiment of the present specification provides a computer-readable storage medium, on which a computer program is stored. When the computer program is run by a processor, the method for warning of a curve of a vehicle as described in any one of the above is implemented.

[0016] In a fifth aspect, an embodiment of the present specification provides a computer program product or a computer program. The computer program product includes a computer program, and the computer program is stored in a computer-readable storage medium; a processor of the computer device reads the computer program from the computer-readable storage medium, and when the processor executes the computer program, the method for warning of a curve of a vehicle as described in any one of the above is implemented.

[0017] As can be seen from the above technical solutions, the present application provides a method and a device for warning of a curve of a vehicle. The method first obtains road surface information of the vehicle in the driving direction in real time through a sensor, and then when a curve appears in the driving direction, based on the road surface information and the running information of the vehicle, determines the curve risk level of the curve, and finally sends a warning message to the driver of the vehicle according to the curve risk level. It can obtain road surface data in real time through the sensor, making the judgment of the curve risk more timely and accurate. At the same time, the curve risk level is informed to the driver, enabling the driver to respond in advance and improving the driving safety. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] In order to more clearly illustrate the technical solutions in the present application or the prior art, the following will briefly introduce the drawings required for use in the description of the embodiments or the prior art. Obviously, the drawings in the following description are only the embodiments of the present application. For those of ordinary skill in the art, other drawings can be obtained according to the provided drawings without creative efforts.

[0019] Figure 1 It is a schematic flow chart of a method for warning of a curve of a vehicle provided for an embodiment of the present specification;

[0020] Figure 2 It is a specific schematic flow chart of a method for warning of a curve of a vehicle provided for an embodiment of the present specification;

[0021] Figure 3 It is a specific schematic flow chart of another method for warning of a curve of a vehicle provided for an embodiment of the present specification;

[0022] Figure 4 A schematic flow chart of another method for warning of a vehicle's curve for the embodiments of this specification;

[0023] Figure 5 A schematic structural diagram of a vehicle curve warning device provided for the embodiments of this specification;

[0024] Figure 6 A schematic structural diagram of an electronic device provided for the embodiments of this specification. Specific embodiments

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

[0026] Unless otherwise required by the context, throughout this specification, "a plurality" means "at least two", and "comprising" is interpreted as open and inclusive, that is, "including, but not limited to". In the description of the specification, the terms "one embodiment", "some embodiments", "exemplary embodiments", "examples", "specific examples" or "some examples" etc. are intended to indicate that the specific features, structures, materials or characteristics related to the embodiment or example are included in at least one embodiment or example of this specification. The schematic representations of the above terms do not necessarily refer to the same embodiment or example.

[0027] Next, the technical solutions in the embodiments of this specification will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of this specification. Obviously, the described embodiments are only a part of the embodiments of this specification, rather than all the embodiments. Based on the embodiments in this specification, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the scope of protection of this specification.

[0028] Overview

[0029] As described in the background art, with the rapid development of new energy vehicles, the role and proportion of assisted driving are increasing. Vehicle centering control is an important part of the L2-level lane assistance function, and the cornering ability under vehicle centering control is an important manifestation point. L2-level lane-level assisted driving is an advanced driver assistance system (ADAS) that provides a certain degree of automated assistance to the driver, but the driver still needs to maintain control and attention of the vehicle. At the L2 level, the driver's hands must be placed on the steering wheel and be ready to take over the operation of the vehicle at any time. This is because despite these assistance functions, the vehicle cannot handle all possible road conditions or emergencies, and it is easy to cause lateral control instability due to reasons such as perception, road, and vehicle control during cornering, resulting in rushing into the oncoming lane or vehicle rollover, which further leads to the occurrence of safety accidents.

[0030] However, in the related art, cornering control mainly relies on high-precision maps, with poor perception ability of environmental changes. At the same time, there is no method to inform the driver of the risk situations during cornering control. As a result, when there is a risk of cornering, the driver cannot be effectively reminded to intervene manually in a timely manner, reducing the driving safety.

[0031] Therefore, it is necessary to provide a corner warning method for vehicles to solve the above problems.

[0032] To solve the problems in the prior art that cornering control mainly relies on high-precision maps, with poor perception ability of environmental changes, and there is no method to inform the driver of the risk situations during cornering control. As a result, when there is a risk of cornering, the driver cannot be effectively reminded to intervene manually in a timely manner, reducing the driving safety. In the technical solution of this application, first, the road surface information of the vehicle in the driving direction is obtained in real time through sensors. Then, when a curve appears in the driving direction, based on the road surface information and the running information of the vehicle, the corner risk level of the curve is determined. Finally, a warning message is sent to the driver of the vehicle according to the corner risk level. It can obtain road surface data in real time through sensors, making the judgment of corner risks more timely and accurate. At the same time, informing the driver of the corner risk level enables the driver to respond in advance and improves driving safety.

[0033] Based on the above inventive concept, the corner warning method for vehicles provided by the embodiments of this specification will be described exemplarily below.

[0034] Exemplary Method

[0035] The embodiments of this specification provide a corner warning method for vehicles, as Figure 1 shown, including:

[0036] S101. Obtain the road surface information of the vehicle in the driving direction in real time through sensors.

[0037] In specific implementation, sensors for obtaining road surface information in real time are equipped on the vehicle. The sensors can be cameras, radars, etc., which are used to capture the road surface data in front of the vehicle, and then obtain the road surface information in the driving direction, providing reliable data support for subsequent driving decisions and alarm steps.

[0038] S102. When a curve appears in the driving direction, determine the curve risk level of the curve based on the road surface information and the running information of the vehicle.

[0039] In one embodiment, first determine whether there are lane lines at the curve according to the road surface information, and then further determine the curve risk level based on whether there are lane lines.

[0040] When there are no lane lines at the curve, the risk is relatively higher at this time, and the corresponding curve risk level is higher. Specifically, determine the lateral speed risk value of the vehicle according to the road surface information and the running information, and use the lateral speed risk value as the evaluation criterion for the curve risk level. Specifically, if the lateral speed risk value is lower than the preset risk threshold, the curve risk level of this curve is the medium risk level; if the lateral speed risk value is higher than the preset risk threshold, the curve risk level of this curve is the high risk level.

[0041] When there are lane lines at a bend, the vehicle speed and the comfortable cornering speed are determined based on road surface information and operation information. Then, according to the vehicle speed and the comfortable cornering speed, the bend risk level of the bend is determined. Specifically, when the vehicle speed is less than or equal to the comfortable cornering speed, the bend risk level of the bend is directly determined as a low risk level. When the vehicle speed is greater than the comfortable cornering speed, the bend risk level of the bend is further determined based on the maximum lateral acceleration value, the lateral adhesion coefficient of the vehicle, the comfortable cornering coefficient, and the risk speed threshold. The risk speed threshold includes a first risk speed threshold and a second risk speed threshold. The values of the first risk speed threshold and the second 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 operation information of the vehicle itself and the road surface information including the bend. For the first risk speed threshold, the parameters for reference calculation include the maximum lateral acceleration value, the maximum deceleration value of bend deceleration, the driving distance from the current position to the position of the maximum lateral acceleration value, and the bend radius. For the second risk speed threshold, the parameters for reference calculation include the lateral adhesion coefficient, the maximum deceleration value of bend deceleration, the driving distance of the position of the maximum lateral acceleration value, and the bend radius. The specific judgment method is to first judge: 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 bend risk level is set as a 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 bend risk level is set as a medium 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 greater than the second risk speed threshold, the bend risk level is set as a 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 bend risk level is set as a medium risk level.

[0042] S103. Send a warning message to the driver of the vehicle according to the bend risk level.

[0043] In specific implementation, the risk level of the front bend is analyzed through the above steps, and then a corresponding warning message is sent to the driver to remind the driver to pay attention to safe driving. Specifically, it can be one or more of visual warning, auditory warning, tactile warning, and voice warning. The embodiments of the present invention do not limit this.

[0044] In the curve warning method for a vehicle provided in this embodiment, first, road surface information of the vehicle in the driving direction is obtained in real time through a sensor. Then, when a curve appears in the driving direction, based on the road surface information and the running information of the vehicle, the curve risk level of the curve is determined. Finally, a warning message is sent to the driver of the vehicle according to the curve risk level. It can obtain road surface data in real time through the sensor, making the judgment of curve risk more timely and accurate. At the same time, the curve risk level is informed to the driver, enabling the driver to respond in advance and improving driving safety.

[0045] The specific process of a curve warning method for a vehicle provided in the embodiments of this specification is as Figure 2 shown and includes:

[0046] S201. Obtain the road surface information of the vehicle in the driving direction in real time through a sensor.

[0047] Specifically, when implementing, a sensor for obtaining road surface information in real time is equipped on the vehicle. This sensor can be a camera sensor for capturing 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 view is not blocked and it can clearly capture the road surface conditions in the driving direction of the vehicle.

[0048] Specifically, a sensor of the camera type can, through its built-in image processing unit, collect road surface images in real time and transmit these image data to the control unit or central processing system of the vehicle. The control unit processes and analyzes the received image data to extract key information on the road surface, such as lane lines, obstacles, road surface potholes, traffic signs, etc., which provides reliable data support for subsequent driving decisions and alarm steps.

[0049] In one example, the camera sensor can be used in combination with other types of sensors (such as radar, lidar, etc.) to improve the accuracy and robustness of road surface information acquisition. For example, the radar sensor can be used to detect the distance and speed of obstacles in front, while the lidar can provide more accurate three-dimensional road surface information. Through multi-sensor fusion technology, the vehicle can more comprehensively perceive the surrounding environment, thereby improving driving safety and the performance of the autonomous driving system.

[0050] S202. When it is determined that there are no lane lines at the curve according to the road surface information, determine the lateral speed risk value of the vehicle based on the road surface information and the running information.

[0051] In specific implementation, if there are no lane lines and the driver does not take over, the vehicle will inevitably have the risk of instability and running out of the lane. Moreover, when the lateral speed is too high, when the vehicle loses the lane lines, the vehicle will quickly run out of the lane. Therefore, relatively speaking, the risk level of the curve is relatively high, and the specific judgment logic is as follows:

[0052] First, determine the lateral speed risk value of the vehicle according to the road surface information and the running information. The expression of the lateral speed risk value is as follows:

[0053]

[0054] Among them, the driving speed of the vehicle is V1, the comfortable cornering speed is V ′ , a is the maximum deceleration value for cornering deceleration. This speed value can be a calibrated value or set according to safety and comfort. The embodiments of the present invention do not limit this. x is the driving distance between the current position and the position where the lane lines are lost. The lateral speed is the main manifestation of the strength of curve control and is also an important indicator for judging the instability risk after the lane lines are lost. The threshold speeds for medium risk and high risk are calibrated according to the actual performance of the vehicle, which is V0, is the angle between the vehicle speed and the y-axis of the vehicle. In , if the vehicle does not need to decelerate, then If the driving speed is too high, deceleration is required. Deceleration is divided into two categories. The first is that the vehicle can decelerate to the comfortable cornering speed before reaching the curve. Then, after the vehicle decelerates to the comfortable cornering speed, it will not continue to decelerate. At this time If it is impossible to decelerate to the comfortable cornering speed by decelerating at the maximum deceleration, this is the second case. Then, when the vehicle travels to the moment when the lane lines are lost, it has been decelerating all the time, that is

[0055] S203. If the lateral speed risk value is lower than the preset risk threshold, determine that the curve risk level of the curve is the medium risk level. If the lateral speed risk value is higher than the preset risk threshold, determine that the curve risk level of the curve is the high risk level.

[0056] In specific implementation, if the lateral speed risk value is lower than the preset risk threshold, that is: At this time, determine that the curve risk level of the curve is the medium risk level. If the lateral speed risk value is not lower than the preset risk threshold, that is: does not hold, then determine that the curve risk level of the curve is the high risk level.

[0057] S204. Send a warning message to the driver of the vehicle according to the curve risk level.

[0058] In specific implementation, the risk level of the upcoming curve is analyzed through the above steps, and then corresponding warning information is sent to the driver to remind the driver to pay attention to safe driving. Specifically, it can be one or more of visual warning, auditory warning, tactile warning, and voice warning. The embodiments of the present invention do not limit this.

[0059] In one example, the warning can be carried out in the following manner:

[0060] During visual warning, when the risk is low, a yellow prompt icon, such as "Upcoming Curve, Please Slow Down", is displayed on the vehicle dashboard or head-up display (HUD). When the risk is medium, an orange warning icon is displayed and flashes with the prompt "Sharp Curve Ahead, Please Slow Down and Drive Carefully". When the risk is high, a red warning icon is displayed and accompanied by the text prompt "Dangerous Curve, Please Decelerate Immediately!".

[0061] During auditory warning, when the risk is low, a short beep is emitted. When the risk is medium, continuous beeps are emitted at a moderate frequency. When the risk is high, a high-frequency and rapid alarm sound is emitted to attract the driver's high attention.

[0062] During tactile warning, when the risk is low, the steering wheel vibrates slightly once. When the risk is medium, the steering wheel vibrates continuously to remind the driver to pay attention. When the risk is high, the seat or seat belt vibrates, and at the same time the steering wheel vibrates strongly to enhance the warning effect.

[0063] During voice warning, when the risk is low, the voice prompt is "Upcoming Curve, Please Pay Attention to Vehicle Speed". When the risk is medium, the voice prompt is "Sharp Curve Ahead, Please Decelerate". When the risk is high, the voice prompt is "Dangerous Curve, Please Decelerate Immediately!".

[0064] In this step, corresponding measures can also be taken while giving the warning information. In one example:

[0065] During low-risk warning, when the vehicle approaches a low-risk curve, the system will display a yellow prompt icon on the dashboard or HUD and emit a short beep. At this time, the driver only needs to appropriately adjust the vehicle speed.

[0066] During medium-risk warning, when the vehicle approaches a medium-risk curve, the system will display an orange warning icon and emit continuous beeps. At the same time, the steering wheel may vibrate slightly to remind the driver to slow down and drive carefully.

[0067] During high-risk warning, when the vehicle approaches a high-risk curve, the system will display a red warning icon and emit a high-frequency and rapid alarm sound. The steering wheel and seat may vibrate strongly, and at the same time the voice prompt is "Dangerous Curve, Please Decelerate Immediately!". At this time, the driver needs to immediately take deceleration measures to ensure safe passage through the curve.

[0068] Through the above warning mechanism, the vehicle can provide timely and effective warning information for the driver in curves with different risk levels, thereby reducing the probability of accidents and improving driving safety.

[0069] In this step, the warning information can also be sent to surrounding vehicles or pedestrians through external devices of the vehicle (such as flashing lights). In addition, the system can be linked with the vehicle's automatic driving function to automatically reduce the vehicle speed or adjust the driving trajectory when the driver fails to respond to the warning in a timely manner to further ensure safety.

[0070] The embodiment of this specification provides a specific process of a curve warning method for a vehicle, as Figure 3 shown, including:

[0071] S301. Obtain the road surface information of the vehicle in the driving direction in real time through sensors.

[0072] Steps S301 and S304 are the same as steps S201 and S204, and will not be elaborated here.

[0073] S302. When it is determined that there are lane lines at the curve according to the road surface information, determine the vehicle speed and the comfortable cornering speed according to the road surface information and the running information.

[0074] In specific implementation, when the lane lines in front do not disappear, the instability risk judgment is mainly based on the vehicle speed and the comfortable cornering speed. Specifically, set the comfortable cornering coefficient as K, then the comfortable cornering lateral acceleration is Kug, where u is the actual lateral adhesion coefficient of the vehicle, and this value can be based on an adhesion coefficient measuring instrument or other calculation methods, which are not limited in the embodiments of the present invention again. g is the acceleration due to gravity, the driving speed of the vehicle is V1, a is the maximum deceleration value for cornering deceleration, x is the driving distance from the current position to the position of the maximum lateral acceleration, R is the curve radius, and the comfortable cornering speed is

[0075] The actual lateral adhesion coefficient of a vehicle is a parameter that describes the magnitude of the frictional force between the vehicle's tires and the road surface. It is very important for evaluating the driving safety and performance of the vehicle. Under wet, ice and snow covered or other special road conditions, understanding the adhesion coefficient of the road surface can help drivers adjust their driving behavior or be used in autonomous driving systems to optimize vehicle control strategies. Measuring the actual lateral adhesion coefficient of a vehicle is usually not carried out directly, but estimated by indirect methods. For example: measurement by a portable tribometer, measurement by a real-time monitoring system based on sensors, measurement by a laser texture analyzer, etc. The embodiments of the present invention do not limit this. It should be noted that in many cases, in order to ensure accuracy, the above-mentioned multiple methods and technologies are used in combination. For autonomous vehicles, accurately perceiving the environment and understanding the road surface conditions are crucial, so they often integrate more complex sensing systems and algorithms to achieve this. In addition, since the road surface adhesion coefficient is affected by various factors such as weather, temperature, and humidity, dynamic update and adaptive algorithms are also key.

[0076] S303. Determine the curve risk level of the curve according to the vehicle speed and the comfortable cornering speed.

[0077] During specific implementation, first compare the vehicle speed and the comfortable cornering speed. When the vehicle speed is less than or equal to the comfortable cornering speed, that is at this time, determine that the curve risk level of the curve is a low risk level. If the above formula does not hold, then further judge using the risk speed threshold. The risk speed threshold includes a first risk speed threshold and a second speed risk threshold. In one embodiment, the first risk speed threshold depends on the maximum lateral acceleration value, the maximum deceleration value of curve deceleration, the driving distance from the current position to the position of the maximum lateral acceleration, and the curve radius. Its specific expression is The second speed risk threshold depends on the lateral adhesion coefficient, the maximum deceleration value, the driving distance, and the curve radius. 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. Therefore, on a high-adhesion road surface, usually a ymax < ug, but in rainy or snowy weather, the road surface adhesion coefficient will be much lower than the high-adhesion coefficient, so that it will cause a ymax > ug.

[0078] 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 borne by the road surface adhesion coefficient at this time 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 determine that the curve risk level of the curve is a high risk level; if a ymax ≤ug, but at this time has exceeded the comfortable cornering speed, then determine that the curve risk level of the curve is a medium risk level.

[0079] 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 means that the maximum lateral acceleration value borne by the road surface 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 then there must be a risk of vehicle instability. At this time, determine that the curve risk level of the curve is a high risk level. If a ymax >ug, but at this time has exceeded the comfortable cornering speed, then determine that the curve risk level of the curve is a medium risk level.

[0080] S304. Send a warning message to the driver of the vehicle according to the curve risk level.

[0081] The embodiment of the present specification also provides a specific process of a curve warning method for a vehicle, as Figure 4 shown. First, obtain road surface information through a camera, and then judge whether there is a lane line according to the road surface information. If there is no lane line, perform the judgment of Logic 1. Logic 1 is to judge the lateral speed risk value and the preset risk threshold, that is and V0. If there is a lane line, perform the judgment of Logic 2. Logic 2 is to judge the vehicle speed and the comfortable cornering speed, that is, V1 and At time, compare that the lateral adhesion coefficient is greater than the ratio of the maximum lateral acceleration value to the gravitational acceleration, that is, compare a ymax and ug. When a ymax >ug, perform the judgment of Logic 3. When it does not hold, perform the judgment of Logic 4. Logic 3 is to judge the size of the vehicle speed and the second risk speed threshold, and Logic 4 is to judge the size of the vehicle speed and the first risk speed threshold. Thus, determine the curve risk level of the curve through the above method, and then remind the driver.

[0082] Exemplary Apparatus

[0083] In an exemplary embodiment of the present specification, a curve warning device 500 for a vehicle is also provided, as Figure 5 shown, including:

[0084] An acquisition unit 501, configured to acquire road surface information of a vehicle in a driving direction in real time through a sensor;

[0085] A processing unit 502, configured to determine a curve risk level of a curve based on road surface information and running information of the vehicle when a curve appears in the driving direction;

[0086] An early warning unit 503, configured to send an early warning message to a driver of the vehicle according to the curve risk level.

[0087] In an implementation manner, the processing unit 502 is specifically configured to:

[0088] When it is determined according to the road surface information that there is no lane line at the curve, determine a lateral speed risk value of the vehicle according to the road surface information and the running information;

[0089] If the lateral speed risk value is lower than a preset risk threshold, determine that the curve risk level of the curve is a medium risk level; if the lateral speed risk value is not lower than the preset risk threshold, determine that the curve risk level of the curve is a high risk level.

[0090] In an implementation manner, the processing unit 502 is further configured to:

[0091] When it is determined according to the road surface information that there is a lane line at the curve, determine the vehicle speed and a comfortable cornering speed according to the road surface information and the running information;

[0092] Determine the curve risk level of the curve according to the vehicle speed and the comfortable cornering speed.

[0093] In an implementation manner, the processing unit 502 is specifically configured to:

[0094] When the vehicle speed is less than or equal to the comfortable cornering speed, determine that the curve risk level of the curve is a low risk level;

[0095] When the vehicle speed is greater than the comfortable cornering speed, determine the curve risk level of the curve according to the maximum lateral acceleration value, the lateral adhesion coefficient of the vehicle, the comfortable cornering coefficient, and the risk speed threshold.

[0096] In an implementation manner, the risk speed threshold includes a first risk speed threshold and a second risk speed threshold, and the processing unit 502 is specifically configured to:

[0097] When the lateral adhesion coefficient is greater than or equal to the ratio of the maximum lateral acceleration value to the gravitational acceleration, determine the curve risk level of the curve based on the first risk speed threshold;

[0098] When the lateral adhesion coefficient is less than the ratio of the maximum lateral acceleration value to the gravitational acceleration, determine the curve risk level of the curve based on the second risk speed threshold.

[0099] In one embodiment, the processing unit 502 is specifically configured to:

[0100] When the lateral adhesion coefficient is greater than or equal to the ratio of the maximum lateral acceleration value to the gravitational acceleration value and the vehicle speed is greater than the first risk speed threshold, determine that the curve risk level of the curve is a high risk level;

[0101] When the lateral adhesion coefficient is greater than or equal to the ratio of the maximum lateral acceleration value to the gravitational acceleration value and the vehicle speed is less than or equal to the first risk speed threshold, determine that the curve risk level of the curve is a medium risk level.

[0102] In one embodiment, the processing unit 502 determines the first risk speed threshold according to the maximum lateral acceleration value, the maximum deceleration value of curve deceleration, the driving distance from the current position to the position of the maximum lateral acceleration value, and the curve radius.

[0103] In one embodiment, the processing unit 502 is further configured to:

[0104] When the lateral adhesion coefficient is less than the ratio of the maximum lateral acceleration value to the gravitational acceleration value and the vehicle speed is greater than the second risk speed threshold, determine that the curve risk level of the curve is a high risk level;

[0105] When the lateral adhesion coefficient is less than the ratio of the maximum lateral acceleration value to the gravitational acceleration value and the vehicle speed is less than or equal to the second risk speed threshold, determine that the curve risk level of the curve is a medium risk level.

[0106] In one embodiment, the processing unit 502 determines the second risk speed threshold according to the lateral adhesion coefficient, the maximum deceleration value, the driving distance, and the curve radius.

[0107] The curve warning device of the vehicle provided in this embodiment belongs to the same inventive concept as the vehicle curve warning method provided in the above embodiments of the present application, can execute the vehicle curve warning method provided in any of the above embodiments of the present application, and has the corresponding functional modules and beneficial effects for executing the vehicle curve warning method. For the technical details not described in detail in this embodiment, reference may be made to the specific processing content of the vehicle curve warning method provided in the above embodiments of the present application, which will not be elaborated here.

[0108] Exemplary Device

[0109] In an exemplary embodiment of this specification, an electronic device is further provided, such as Figure 6As shown in the figure, the electronic device may include: a processor 610, a communications interface 620, a memory 630, and a communication bus 640. Among them, the processor 610, the communications interface 620, and the memory 630 complete mutual communication through the communication bus 640. The processor 610 may call the logical instructions in the memory 630 to execute the curve warning method for a vehicle. The method includes:

[0110] Obtain the road surface information of the vehicle in the driving direction in real time through a sensor;

[0111] When a curve appears in the driving direction, determine the curve risk level of the curve based on the road surface information and the running information of the vehicle;

[0112] Send a warning message to the driver of the vehicle according to the curve risk level.

[0113] In addition, when the logical instructions in the above-mentioned memory 630 are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on such an understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or a part of this technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in various embodiments of the present invention. The foregoing storage medium includes: various media such as a USB flash drive, a mobile hard disk, a read-only memory (ROM, Read-Only Memory), a random access memory (RAM, Random Access Memory), a magnetic disk, or an optical disc that can store program codes.

[0114] Exemplary Computer Program Product and Storage Medium

[0115] In addition to the above methods and devices, the curve warning method for a vehicle provided in the embodiments of this specification may also be a computer program product, which includes computer program instructions. When the computer program instructions are run by a processor, the processor is caused to execute the steps in the curve warning method for a vehicle according to various embodiments of this specification described in the "Exemplary Method" part of this specification.

[0116] The computer program product may be written in any combination of one or more programming languages for programming code to perform the operations of the embodiments of this specification. The programming languages include object-oriented programming languages such as Java, C++, etc., and also include conventional procedural programming languages such as the "C" language or similar programming languages.

[0117] In addition, the embodiments of this specification also provide a computer-readable storage medium, on which a computer program is stored. The computer program is executed by a processor to perform the steps in the curve warning method for vehicles according to various embodiments of this specification described in the above "Exemplary Method" section.

[0118] Those of ordinary skill in the art can understand that all or part of the processes in the methods of the above embodiments can be completed by instructing relevant hardware through a computer program. The computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above methods. Among them, any reference to a memory, storage, database, or other medium used in the various embodiments provided in this specification can include non-volatile and / or volatile memories. Non-volatile memories can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memories 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), double data rate SDRAM (DDR SDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), etc.

[0119] The technical features of the above embodiments can be combined arbitrarily. For the sake of concise description, 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, it should be considered to be within the scope described in this specification.

[0120] The above-described embodiments merely represent several implementation manners of this specification. The description is relatively specific and detailed, but it should not be construed as a limitation on the scope of the solutions provided by the embodiments of this specification. It should be noted that for those of ordinary skill in the art, without departing from the concept of this specification, several modifications and improvements can still be made, and these all fall within the protection scope of this specification. Therefore, the protection scope of the patent of this specification shall be subject to the appended claims.

Claims

1. A vehicle curve warning method, characterized in that: include: The road surface information of the vehicle in the driving direction is obtained in real time through sensors; When a curve appears in the driving direction, determining a curve risk level of the curve based on the road surface information and the operation information of the vehicle; A warning message is sent to the driver of the vehicle according to the curve risk level.

2. The method according to claim 1, characterized in that The determining the curve risk level of the curve based on the road surface information and the operation information of the vehicle includes: When it is determined according to the road surface information that there is no lane line at the curve, determining a lateral speed risk value of the vehicle according to the road surface information and the operation information; If the lateral speed risk value is lower than the preset risk threshold, the curve risk level of the curve is determined to be a medium risk level; if the lateral speed risk value is not lower than the preset risk threshold, the curve risk level of the curve is determined to be a high risk level.

3. The method according to claim 1, characterized in that The determining the curve risk level of the curve based on the road surface information and the operation information of the vehicle includes: When it is determined according to the road surface information that there is a lane line at the curve, determining the vehicle speed and comfortable cornering speed of the vehicle according to the road surface information and the operation information; A curve risk level of the curve is determined according to the vehicle speed and the comfortable curve speed.

4. The method according to claim 3, characterized in that The determining the curve risk level of the curve according to the vehicle speed and the comfortable curve speed includes: When the vehicle speed is less than or equal to the comfortable cornering speed, determining that the curve risk level of the curve is a low risk level; When the vehicle speed is greater than the comfortable cornering speed, the curve risk level of the curve is determined according to the maximum lateral acceleration value, the lateral adhesion coefficient of the vehicle, the comfortable cornering coefficient and the risk speed threshold.

5. The method according to claim 4, characterized in that The risk speed threshold comprises a first risk speed threshold and a second risk speed threshold, and determining the curve risk level of the curve according to the maximum lateral acceleration value, the lateral adhesion coefficient of the vehicle, the comfortable cornering coefficient and the risk speed threshold comprises: When the lateral adhesion coefficient is greater than or equal to the ratio of the maximum lateral acceleration value to the gravity acceleration, determining the curve risk level of the curve 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 gravity acceleration, a curve risk level of the curve is determined based on the second risk speed threshold.

6. The method according to claim 5, characterized in that When the lateral adhesion coefficient is greater than or equal to the ratio of the maximum lateral acceleration value to the gravity acceleration, determining the curve risk level of the curve based on the first risk speed threshold comprises: When the lateral adhesion coefficient is greater than or equal to the ratio of the maximum lateral acceleration value to the gravity acceleration, and the vehicle speed is greater than the first risk speed threshold, determining that the curve risk level of the curve is a high risk level; When the lateral adhesion coefficient is greater than or equal to the ratio of the maximum lateral acceleration value to the gravity acceleration, and the vehicle speed is less than or equal to the first risk speed threshold, it is determined that the curve risk level of the curve is a medium risk level.

7. The method according to claim 6, characterized in that The first risk speed threshold is determined according to the maximum lateral acceleration value, the maximum deceleration value of curve deceleration, the driving distance between the current position and the position of the maximum lateral acceleration, and the curve radius.

8. The method according to claim 5, characterized in that When the lateral adhesion coefficient is less than the ratio of the maximum lateral acceleration value to the gravity acceleration, determining the curve risk level of the curve based on the second risk speed threshold comprises: When the lateral adhesion coefficient is less than the ratio of the maximum lateral acceleration value to the gravity acceleration, and the vehicle speed is greater than the second risk speed threshold, determining that the curve risk level of the curve is a high risk level; When the lateral adhesion coefficient is less than the ratio of the maximum lateral acceleration value to the gravity acceleration, and the vehicle speed is less than or equal to the second risk speed threshold, it is determined that the curve risk level of the curve is a medium risk level.

9. The method according to claim 8, characterized in that The second risk speed threshold is determined according to the lateral adhesion coefficient, the maximum deceleration value, the driving distance and the curve radius.

10. A computer device, characterized in that: include: A memory and a processor, wherein the memory and the processor are communicatively connected to each other, the memory stores computer instructions, and the processor executes the vehicle curve warning method according to any one of claims 1 to 9 by executing the computer instructions.

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