Automatic Emergency Braking Control Method and System Based on Road Surface Adhesion Coefficient Estimation

Abstract

This invention discloses an automatic emergency braking control method and system based on road surface adhesion coefficient estimation, belonging to the field of autonomous driving technology. It includes: acquiring and fitting multiple road surface adhesion coefficients and tire slip ratios of the autonomous vehicle during acceleration or starting phases to generate an ascending fitting line; acquiring and fitting multiple road surface adhesion coefficients and tire slip ratios of the autonomous vehicle during deceleration phases to obtain a descending fitting line; performing curve fitting on the ascending and descending fitting lines based on their intersection points to obtain the road surface adhesion coefficient-tire slip ratio curve corresponding to the current road surface and extracting the road surface adhesion coefficient estimate; acquiring the relative speed between the autonomous vehicle and the target obstacle, and determining the automatic emergency braking threshold based on the road surface adhesion coefficient estimate. This method can adaptively adjust the braking threshold according to the road surface type, solving the problem of untimely braking in existing AEB systems on low-friction roads.

Description

Technical Field

[0001] This invention relates to the field of autonomous driving technology, and in particular to an automatic emergency braking control method and system based on road surface adhesion coefficient estimation. Background Technology

[0002] The statements in this section merely refer to the background art related to this invention and do not necessarily constitute prior art.

[0003] Traditional Automatic Emergency Braking (AEB) systems require observation of objects ahead to obtain the relative distance and speed between the vehicle and the object, thereby calculating the Time to Collision (TTC). When the TTC falls below a set threshold (TB), automatic emergency braking is triggered, and the AEB system issues a maximum deceleration command, which is then responded to and executed by the braking system.

[0004] This strategy does not take into account the road surface's coefficient of friction. When driving on surfaces with a low coefficient of friction, the braking system cannot respond to the maximum deceleration command issued by the AEB system, and the actual deceleration will be lower than expected, easily leading to a collision risk. Therefore, it is particularly necessary for the AEB system to consider the road surface's coefficient of friction during operation, and to issue braking commands earlier on surfaces with a low coefficient of friction (low-friction road surface).

[0005] Chinese invention patent application number 202011603975.5 describes a vision-based target detection algorithm that identifies dry cement roads, dry asphalt roads, wet cement roads, wet asphalt roads, and icy / snowy roads, and calculates safe distances based on the adhesion coefficient ranges corresponding to different road surfaces. However, this method is based on supervised deep learning, and its estimation of the adhesion coefficient depends on training samples. If an unidentifiable road surface category appears, the adhesion coefficient cannot be estimated.

[0006] In Chinese invention patent application number 202111225837.2, an interactive multi-model approach is used to calculate the road adhesion coefficient at the current moment by probabilistically weighting the adhesion coefficients set by each model. However, the calculated adhesion coefficient only represents the road adhesion coefficient under the current tire slip ratio, and cannot represent the maximum adhesion coefficient that the current road surface can provide. Summary of the Invention

[0007] To address the shortcomings of existing technologies, this invention provides an automatic emergency braking control method, system, electronic device, computer-readable storage medium, and computer program product based on road surface adhesion coefficient estimation. By combining the dynamic changes of the road surface adhesion coefficient, the TTC value is adaptively adjusted to improve the performance of the AEB system on low-adhesion roads.

[0008] In a first aspect, the present invention provides an automatic emergency braking control method based on road surface adhesion coefficient estimation;

[0009] An automatic emergency braking control method based on road surface adhesion coefficient estimation includes:

[0010] Multiple road surface adhesion coefficients and tire slip ratios of autonomous vehicles during acceleration or start-up are obtained and fitted to generate an ascending fitted line; multiple road surface adhesion coefficients and tire slip ratios of autonomous vehicles during deceleration are obtained and fitted to generate a descending fitted line.

[0011] Based on the intersection of the rising and falling fitted lines, curve fitting is performed on the rising and falling fitted lines to obtain the road adhesion coefficient-tire slip ratio curve corresponding to the current road surface and extract the estimated value of the road adhesion coefficient.

[0012] The relative speeds of the autonomous vehicle and the target obstacle are obtained, and the road surface adhesion coefficient is estimated to determine the automatic emergency braking threshold. Automatic emergency braking is then performed based on the automatic emergency braking threshold and the collision time.

[0013] In some implementations, obtaining the road adhesion coefficient and tire slip ratio of an autonomous vehicle during acceleration or start-up phases includes:

[0014] Obtain the acceleration of the autonomous vehicle during the acceleration or start-up phase, and determine the road adhesion coefficient based on the acceleration and gravitational acceleration.

[0015] The speed, angular velocity of the drive wheels, and tire rolling radius of the autonomous vehicle during acceleration or start-up are obtained to determine the tire slip ratio.

[0016] In some implementations, obtaining the road adhesion coefficient and tire slip ratio of an autonomous vehicle during the deceleration phase includes:

[0017] Obtain the deceleration of the autonomous vehicle during the deceleration phase, and determine the road adhesion coefficient based on the deceleration and gravitational acceleration;

[0018] The speed, angular velocity of the braking wheels, and tire rolling radius of the autonomous vehicle during the deceleration phase are obtained to determine the tire slip ratio.

[0019] In some implementations, the curve fitting of the rising and falling fitted lines based on the intersection of the rising and falling fitted lines specifically involves: determining multiple fitting points on the rising and falling fitted lines using confidence levels, and fitting the multiple fitting points and the intersection of the rising and falling fitted lines using a second-order Bézier curve.

[0020] In some implementations, the automatic emergency braking threshold is expressed as:

[0021]

[0022] In the formula, V L μ represents the relative speed between the autonomous vehicle and the target obstacle. max T0 represents the estimated value of the road surface adhesion coefficient, and T0 represents the braking system response time.

[0023] In some implementations, automatic emergency braking is performed based on an automatic emergency braking threshold and a collision time. Specifically, when the collision time is less than the automatic emergency braking threshold, emergency braking is performed.

[0024] Secondly, the present invention provides an automatic emergency braking control system based on road surface adhesion coefficient estimation;

[0025] An automatic emergency braking control system based on road surface adhesion coefficient estimation includes:

[0026] The fitting module is configured to: acquire and fit multiple road surface adhesion coefficients and tire slip ratios of the autonomous vehicle during the acceleration or start-up phase to generate an ascending fitting line; acquire and fit multiple road surface adhesion coefficients and tire slip ratios of the autonomous vehicle during the deceleration phase to obtain a descending fitting line.

[0027] The road surface adhesion coefficient estimation module is configured to: perform curve fitting on the rising and falling fitting lines based on the intersection of the rising and falling fitting lines, obtain the road surface adhesion coefficient-tire slip ratio curve corresponding to the current road surface, and extract the estimated value of the road surface adhesion coefficient.

[0028] The automatic emergency braking module is configured to: acquire the relative speed between the autonomous vehicle and the target obstacle, combine the road surface adhesion coefficient estimate to determine the automatic emergency braking threshold, and execute automatic emergency braking based on the automatic emergency braking threshold and the collision time.

[0029] Thirdly, the present invention provides an electronic device;

[0030] An electronic device includes a memory, a processor, and a computer program stored in the memory, wherein the processor executes the computer program to implement the steps of the above-described automatic emergency braking control method based on road surface adhesion coefficient estimation.

[0031] Fourthly, the present invention provides a computer-readable storage medium;

[0032] A computer-readable storage medium having a computer program / instructions stored thereon, which, when executed by a processor, implements the steps of the above-described automatic emergency braking control method based on road surface adhesion coefficient estimation.

[0033] Fifthly, the present invention provides a computer program product;

[0034] A computer program product includes a computer program / instructions that, when executed by a processor, implement the steps of the above-described automatic emergency braking control method based on road surface adhesion coefficient estimation.

[0035] Compared with the prior art, the beneficial effects of the present invention are:

[0036] 1. The technical solution provided by this invention samples the road surface adhesion coefficient-slip ratio during acceleration and braking in the driving process of a vehicle. The straight line segment of the adhesion coefficient-slip ratio graph is fitted by a linear regression method, and the curve segment of the adhesion coefficient-slip ratio is fitted by a second-order Bezier curve, thereby estimating the maximum road surface adhesion coefficient and adjusting the time threshold for triggering automatic emergency braking. The calculation of road surface adhesion coefficient under different tire slip ratios is considered, so that the automatic emergency braking function can play its maximum role on roads with different adhesion coefficients.

[0037] 2. The technical solution provided by this invention combines the real-time driving status of the vehicle to determine the relationship between the road surface adhesion coefficient and the tire slip ratio, thereby obtaining the estimated real-time road surface adhesion coefficient for the corresponding road segment. Based on the real-time driving status of the vehicle and the road surface condition, the automatic emergency braking threshold is adjusted to improve driving safety. Attached Figure Description

[0038] The accompanying drawings, which form part of this invention, are used to provide a further understanding of the invention. The illustrative embodiments of the invention and their descriptions are used to explain the invention and do not constitute an improper limitation of the invention.

[0039] Figure 1 A flowchart illustrating the automatic emergency braking control method based on road surface adhesion coefficient estimation provided in an embodiment of the present invention;

[0040] Figure 2 An example diagram of the road adhesion coefficient-tire slip ratio (μ-s) curve provided in an embodiment of the present invention;

[0041] Figure 3 This is an example diagram of μ-s line segment fitting provided in an embodiment of the present invention;

[0042] Figure 4 This is an example diagram of μ-s curve segment fitting provided in an embodiment of the present invention;

[0043] Figure 5 μ provided for embodiments of the present invention max Example diagram for determining the value. Detailed Implementation

[0044] It should be noted that the following detailed descriptions are exemplary and intended to provide further explanation of the invention. Unless otherwise specified, all technical and scientific terms used in this invention have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains.

[0045] It should be noted that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to limit the exemplary embodiments of the present invention. As used herein, unless the context clearly indicates otherwise, the singular form is also intended to include the plural form. Furthermore, it should be understood that the terms “comprising” and “having”, and any variations thereof, are intended to cover non-exclusive inclusion, for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0046] Where there is no conflict, the embodiments and features in the embodiments of the present invention can be combined with each other.

[0047] Example 1

[0048] Existing vehicle automatic emergency braking functions do not consider the impact of different road surface types on vehicle braking; therefore, this invention provides an automatic emergency braking control method based on road surface adhesion coefficient estimation, which reflects the road surface condition through the vehicle's real-time driving state and dynamically adjusts the automatic emergency braking threshold accordingly, thereby improving driving safety.

[0049] Next, combined Figures 1-5 This embodiment discloses a detailed method for automatic emergency braking control based on road surface adhesion coefficient estimation. The method includes the following steps:

[0050] S1. Obtain and fit multiple road surface adhesion coefficients and tire slip ratios of the autonomous vehicle during the acceleration or start-up phase to generate an ascending fitted curve; obtain and fit multiple road surface adhesion coefficients and tire slip ratios of the autonomous vehicle during the deceleration phase to obtain a descending fitted curve. Specifically, this includes:

[0051] S101. Obtain multiple accelerations of the autonomous vehicle during the acceleration or start-up phase, and determine multiple road adhesion coefficients μ1 based on the acceleration and gravitational acceleration; obtain multiple speeds, angular velocities of multiple drive wheels, and tire rolling radii of the autonomous vehicle during the acceleration or start-up phase, and determine the tire slip ratio s1; fit the collected multiple (μ1, s1) sample values ​​using a linear regression method to obtain the rising fitting line, i.e., the first half (rising segment) of the μ-s curve.

[0052] Here, μ1 is represented as:

[0053]

[0054] In the formula, a1 represents the acceleration of the autonomous vehicle, g represents the gravitational acceleration, v represents the velocity of the autonomous vehicle, w1 represents the angular velocity of the drive wheel, and r represents the tire rolling radius.

[0055] S102. Obtain multiple decelerations of the autonomous vehicle during the deceleration phase, and determine the road adhesion coefficient based on the multiple decelerations and gravitational acceleration; obtain multiple speeds, angular velocities of multiple brake wheels, and tire rolling radii of the autonomous vehicle during the deceleration phase, and determine multiple tire slip ratios; fit the collected multiple (μ2, s2) sample values ​​using a linear regression method to obtain a descending fitting line, i.e., the latter half (descending segment) of the μ-s curve.

[0056] Here, μ2 is represented as:

[0057]

[0058] s2 is represented as:

[0059]

[0060] In the formula, a2 represents the deceleration of the autonomous vehicle, g represents the gravitational acceleration, v represents the speed of the autonomous vehicle, w2 represents the angular velocity of the brake wheel, and r represents the tire rolling radius.

[0061] Furthermore, during acceleration or constant-speed driving, the speed of the autonomous vehicle is determined based on the angular velocity of the non-drive wheels and the tire rolling radius, expressed as:

[0062] v = w0r;

[0063] In the formula, w0 represents the angular velocity of the non-driving wheel.

[0064] During the deceleration phase, the angular velocity of the autonomous vehicle is determined based on the vehicle speed before deceleration and the deceleration during deceleration, and is expressed as:

[0065]

[0066] In the formula, v0 represents the vehicle speed before deceleration begins, and a represents the deceleration during the deceleration process, which is measured by the deceleration sensor.

[0067] S2. Determine whether the rising and falling fitted lines are successfully fitted. If yes, execute S3; otherwise, execute S1.

[0068] S3. Based on the intersection of the rising and falling fitted lines, perform curve fitting on the rising and falling fitted lines to obtain the road adhesion coefficient-tire slip ratio curve corresponding to the current road surface and extract the estimated value of the road adhesion coefficient.

[0069] After the ascending and descending fitted lines are successfully fitted, for example, as shown below. Figure 3 As shown, the circles represent several (μ1, s1) sample values ​​that are fitted to a straight line L1, and the triangles represent several (μ2, s2) sample values ​​that are fitted to a straight line L2.

[0070] Combination Figure 4 First, select points P and Q with high confidence levels, which satisfy preset conditions, expressed as follows:

[0071]

[0072] In the formula, s P s Q This represents the coordinates of points P and Q on the s-axis. This represents the maximum value of μ1 in the sample (μ1, s1). It is the minimum value of s2 in the (μ2, s2) sample, and ε and δ are preset thresholds.

[0073] Secondly, based on points P, Q, and the intersection point O of L1 and L2, a second-order Bezier curve is used for fitting to obtain the road surface adhesion coefficient-tire slip ratio curve corresponding to the current road surface.

[0074] This embodiment does not limit the curve fitting method; other curve fitting methods or deep learning-based fitting methods can also be used.

[0075] Ultimately, combining Figure 5 The extreme points of the fitted μ-s curve are taken as μ max The estimated value, i.e. the estimated value of the road surface adhesion coefficient.

[0076] S3. Obtain the relative speed between the autonomous vehicle and the target obstacle, and determine the automatic emergency braking threshold based on the road surface adhesion coefficient estimate; the automatic emergency braking threshold is expressed as:

[0077]

[0078] In the formula, V L μ represents the relative speed between the autonomous vehicle and the target obstacle. max This represents the estimated road surface adhesion coefficient, and T0 represents the braking system response time. To follow μ max Deceleration will reduce velocity V L The time it takes to decrease to 0.

[0079] In actual driving, the maximum deceleration provided by the braking system does not represent the maximum deceleration that the vehicle can actually achieve during deceleration. If the road's coefficient of friction μ... max <|a max The actual deceleration will not reach a. max Therefore, if only the maximum deceleration that the vehicle can actually achieve during the deceleration process is considered in the threshold determination process, there is a significant safety risk.

[0080] S4. Execute automatic emergency braking based on the automatic emergency braking threshold and collision time.

[0081] Specifically, emergency braking is executed when the time to collision (TTC) is below the automatic emergency braking threshold.

[0082] Calculating the time of collision (TTC) based on the relative distance and relative speed between the autonomous vehicle and the target object is a standard technique in this field and will not be elaborated upon here.

[0083] Example 2

[0084] This embodiment discloses an automatic emergency braking control system based on road surface adhesion coefficient estimation, including:

[0085] The fitting module is configured to: acquire and fit multiple road surface adhesion coefficients and tire slip ratios of the autonomous vehicle during the acceleration or start-up phase to generate an ascending fitting line; acquire and fit multiple road surface adhesion coefficients and tire slip ratios of the autonomous vehicle during the deceleration phase to obtain a descending fitting line.

[0086] The road surface adhesion coefficient estimation module is configured to: perform curve fitting on the rising and falling fitting lines based on the intersection of the rising and falling fitting lines, obtain the road surface adhesion coefficient-tire slip ratio curve corresponding to the current road surface, and extract the estimated value of the road surface adhesion coefficient.

[0087] The automatic emergency braking module is configured to: acquire the relative speed between the autonomous vehicle and the target obstacle, combine the road surface adhesion coefficient estimate to determine the automatic emergency braking threshold, and execute automatic emergency braking based on the automatic emergency braking threshold and the collision time.

[0088] It should be noted that the fitting module, road surface adhesion coefficient estimation module, and automatic emergency braking module described above correspond to the steps in Embodiment 1. The examples and application scenarios implemented by these modules and their corresponding steps are the same, but they are not limited to the content disclosed in Embodiment 1. It should also be noted that these modules, as part of the system, can be executed in a computer system, such as a set of computer-executable instructions.

[0089] Example 3

[0090] Embodiment 3 of the present invention provides an electronic device, including a memory and a processor, as well as computer instructions stored in the memory and running on the processor. When the computer instructions are executed by the processor, they complete the steps of the above-mentioned automatic emergency braking control method based on road surface adhesion coefficient estimation.

[0091] Example 4

[0092] Embodiment 4 of the present invention provides a computer-readable storage medium for storing computer instructions, which, when executed by a processor, complete the steps of the above-described automatic emergency braking control method based on road surface adhesion coefficient estimation.

[0093] Example 5

[0094] Embodiment 5 of the present invention provides a computer program product, including a computer program / instruction, which, when executed by a processor, implements the steps of the above-described automatic emergency braking control method based on road surface adhesion coefficient estimation.

[0095] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0096] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0097] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment, whereby a series of operational steps are performed to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1The steps of the function specified in one or more boxes.

[0098] The descriptions of each embodiment in the above embodiments have different focuses. For parts not described in detail in a certain embodiment, please refer to the relevant descriptions in other embodiments.

[0099] The above description is merely a preferred embodiment of the present invention and is not intended to limit the invention. Various modifications and variations can be made to the present invention by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. An automatic emergency braking control method based on road surface adhesion coefficient estimation, characterized in that, include: The system acquires and fits multiple road adhesion coefficients and tire slip ratios of autonomous vehicles during acceleration or start-up phases, generating an ascending fitting curve. Multiple road adhesion coefficients and tire slip ratios of autonomous vehicles during the deceleration phase are obtained and fitted to obtain a descent fitting line; Based on the intersection of the rising and falling fitted lines, curve fitting is performed on the rising and falling fitted lines. Specifically, multiple fitting points are determined on the rising and falling fitted lines by confidence level. The multiple fitting points and the intersection of the rising and falling fitted lines are fitted by a second-order Bézier curve to obtain the road adhesion coefficient-tire slip ratio curve corresponding to the current road surface and extract the estimated value of the road adhesion coefficient. The relative speeds of the autonomous vehicle and the target obstacle are obtained, and the road surface adhesion coefficient is estimated to determine the automatic emergency braking threshold. Automatic emergency braking is then performed based on the automatic emergency braking threshold and the collision time.

2. The automatic emergency braking control method based on road surface adhesion coefficient estimation as described in claim 1, characterized in that, Obtaining the road adhesion coefficient and tire slip ratio of an autonomous vehicle during acceleration or start-up includes: Obtain the acceleration of the autonomous vehicle during the acceleration or start-up phase, and determine the road adhesion coefficient based on the acceleration and gravitational acceleration. The speed, angular velocity of the drive wheels, and tire rolling radius of the autonomous vehicle during acceleration or start-up are obtained to determine the tire slip ratio.

3. The automatic emergency braking control method based on road surface adhesion coefficient estimation as described in claim 1, characterized in that, Obtaining the road adhesion coefficient and tire slip ratio of an autonomous vehicle during the deceleration phase includes: Obtain the deceleration of the autonomous vehicle during the deceleration phase, and determine the road adhesion coefficient based on the deceleration and gravitational acceleration; The speed, angular velocity of the braking wheels, and tire rolling radius of the autonomous vehicle during the deceleration phase are obtained to determine the tire slip ratio.

4. The automatic emergency braking control method based on road surface adhesion coefficient estimation as described in claim 1, characterized in that, The automatic emergency braking threshold is expressed as: ； In the formula, This indicates the relative speed between the autonomous vehicle and the target obstacle. This represents the estimated value of the road surface adhesion coefficient. This indicates the response time of the braking system.

5. The automatic emergency braking control method based on road surface adhesion coefficient estimation as described in claim 1, characterized in that, Based on the automatic emergency braking threshold and the collision time, the automatic emergency braking is specifically performed as follows: when the collision time is lower than the automatic emergency braking threshold, emergency braking is performed.

6. An automatic emergency braking control system based on road surface adhesion coefficient estimation, characterized in that, include: The fitting module is configured to: acquire and fit multiple road surface adhesion coefficients and tire slip ratios of the autonomous vehicle during the acceleration or start-up phase to generate an ascending fitting line; acquire and fit multiple road surface adhesion coefficients and tire slip ratios of the autonomous vehicle during the deceleration phase to obtain a descending fitting line. The road surface adhesion coefficient estimation module is configured to: perform curve fitting on the rising and falling fitting lines based on the intersection of the rising and falling fitting lines. Specifically, multiple fitting points are determined on the rising and falling fitting lines by confidence level, and the multiple fitting points and the intersection of the rising and falling fitting lines are fitted by a second-order Bézier curve to obtain the road surface adhesion coefficient-tire slip ratio curve corresponding to the current road surface and extract the estimated value of the road surface adhesion coefficient. The automatic emergency braking module is configured to: acquire the relative speed between the autonomous vehicle and the target obstacle, combine the road surface adhesion coefficient estimate to determine the automatic emergency braking threshold, and execute automatic emergency braking based on the automatic emergency braking threshold and the collision time.

7. An electronic device comprising a memory, a processor, and a computer program stored in the memory, characterized in that, The processor executes the computer program to implement the steps of the automatic emergency braking control method based on road surface adhesion coefficient estimation as described in any one of claims 1-5.

8. A computer-readable storage medium having a computer program / instructions stored thereon, characterized in that, When the computer program / instructions are executed by the processor, they implement the steps of the method according to any one of claims 1-5.

9. A computer program product, comprising a computer program / instructions, characterized in that, When the computer program / instruction is executed by the processor, it implements the steps of the automatic emergency braking control method based on road surface adhesion coefficient estimation as described in any one of claims 1-5.

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