Bend control method, device and equipment of adaptive cruise control system and medium

Abstract

The application discloses a curve control method, device and equipment of an adaptive cruise control system and a medium, relates to the technical field of automatic driving, and comprises the following steps: acquiring a current curvature value and a current curvature change rate of a host vehicle at a current lane position; determining a current curvature gain based on the current curvature change rate, optimizing the current curvature gain based on the relationship between the current curvature change rate and the current curvature value, and obtaining an optimized curvature gain; determining a current lane curvature based on the current curvature value, the current curvature change rate, the optimized curvature gain and a longitudinal distance between a preset aiming point position and the host vehicle; and determining a target vehicle speed by using the current lane curvature and a current lateral acceleration of the host vehicle, so as to control the host vehicle to travel in a curve by using the target vehicle speed. The application improves the comfort, stability and timeliness of the adaptive cruise control system in curve control.

Description

Technical Field

[0001] This invention relates to the field of autonomous driving technology, and in particular to a curve control method, apparatus, device, and medium for an adaptive cruise control system. Background Technology

[0002] Currently, the market demand for Adaptive Cruise Control (ACC) is increasing, and the functional requirements for ACC are also gradually increasing. Among them, speed reduction in curves is basically a standard feature of ACC.

[0003] Current designs for reducing speed on curves are based on the real-time curvature of the road. The curvature calculation is primarily based on lane line equations to anticipate curves, thereby limiting vehicle speed and achieving early reduction. Figure 1 As shown. However, the sensor's measurement of lane curvature is unstable, and it only recognizes curvature when the vehicle is currently in a curve. This results in slower deceleration when entering a curve, and the vehicle is prone to switching between acceleration and deceleration while in a curve, leading to a poor driving experience.

[0004] In summary, ensuring comfort, stability, and responsiveness during cornering control in adaptive cruise control systems remains a challenge. Summary of the Invention

[0005] In view of this, the purpose of this invention is to provide a curve control method, device, equipment, and medium for an adaptive cruise control system, which can ensure comfort, stability, and timeliness during curve control. The specific solution is as follows:

[0006] In a first aspect, this application discloses a curve control method for an adaptive cruise control system, comprising:

[0007] Obtain the current curvature value and current rate of change of curvature of the vehicle in the current lane;

[0008] The current curvature gain is determined based on the current rate of change of curvature, and the current curvature gain is optimized based on the relationship between the current rate of change of curvature and the current curvature value to obtain the optimized curvature gain.

[0009] The current lane curvature is determined based on the current curvature value, the current rate of change of curvature, the optimized curvature gain, and the longitudinal distance between the preset aiming point and the vehicle.

[0010] A target speed is determined using the current lane curvature and the vehicle's current lateral acceleration, and the vehicle is then controlled to travel in the curve using the target speed.

[0011] Optionally, after determining the current lane curvature based on the current curvature value, the current rate of change of curvature, the optimized curvature gain, and the longitudinal distance between the preset aiming point position and the vehicle, the method further includes:

[0012] The current filter coefficients are determined based on the initial filter coefficients and the preset filter coefficient adjustment rules;

[0013] The current lane curvature is filtered based on the current filter coefficients to obtain the current filtered lane curvature.

[0014] Accordingly, determining the target vehicle speed using the current lane curvature and the vehicle's current lateral acceleration includes:

[0015] The target vehicle speed is determined using the current filtered lane curvature and the current lateral acceleration of the vehicle.

[0016] Optionally, determining the current filter coefficients based on the initial filter coefficients and preset filter coefficient adjustment rules includes:

[0017] Obtain the difference between the current lane curvature and the previous filtered lane curvature, and determine the positive or negative relationship between the current curvature value and the current rate of change of curvature;

[0018] The current filter coefficients are obtained by adjusting the initial filter coefficients based on the magnitude of the difference and the positive / negative relationship.

[0019] Optionally, the current filtering coefficient is proportional to the magnitude of the difference; and when the current curvature value and the current rate of change of curvature have different signs, the current filtering coefficient is proportional to the absolute value of the current rate of change of curvature; when the current curvature value and the current rate of change of curvature have the same sign, the current filtering coefficient is the initial filtering coefficient.

[0020] Optionally, determining the current curvature gain based on the current rate of change of curvature includes:

[0021] Obtain a pre-set mapping relationship between the rate of curvature change and the curvature gain; wherein, in the mapping relationship, the curvature gain is proportional to the absolute value of the rate of curvature change;

[0022] Based on the mapping relationship, the current curvature gain corresponding to the current rate of curvature change is determined.

[0023] Optionally, optimizing the current curvature gain based on the relationship between the current rate of change of curvature and the current curvature value to obtain an optimized curvature gain includes:

[0024] The current curvature gain is optimized based on the target ratio between the current rate of change of curvature and the current curvature value to obtain the optimized curvature gain.

[0025] Optionally, optimizing the current curvature gain based on the target ratio between the current rate of change of curvature and the current curvature value to obtain the optimized curvature gain includes:

[0026] Obtain the correspondence between the pre-set ratio and the curvature gain value rule;

[0027] Determine the target ratio between the current rate of change of curvature and the current curvature value, and obtain the target curvature gain value rule corresponding to the target ratio based on the correspondence.

[0028] The current curvature gain is optimized using the curvature gain value rule to obtain the optimized curvature gain.

[0029] Secondly, this application discloses a curve control device for an adaptive cruise control system, comprising:

[0030] The vehicle information acquisition module is used to acquire the current curvature value and current rate of change of curvature of the vehicle in the current lane.

[0031] The curvature gain determination module is used to determine the current curvature gain based on the current rate of change of curvature, and to optimize the current curvature gain based on the relationship between the current rate of change of curvature and the current curvature value, so as to obtain the optimized curvature gain.

[0032] The lane curvature determination module is used to determine the current lane curvature based on the current curvature value, the current curvature change rate, the optimized curvature gain, and the longitudinal distance between the preset aiming point position and the vehicle.

[0033] The curve control module is used to determine a target speed using the current lane curvature and the current lateral acceleration of the vehicle, so as to control the vehicle to drive in the curve using the target speed.

[0034] Thirdly, this application discloses an electronic device, including:

[0035] Memory, used to store computer programs;

[0036] A processor is used to execute the computer program to implement the steps of the aforementioned disclosed adaptive cruise control system's curve control method.

[0037] Fourthly, this application discloses a computer-readable storage medium for storing a computer program; wherein, when the computer program is executed by a processor, it implements the steps of the aforementioned disclosed adaptive cruise control system's curve control method.

[0038] As can be seen, this application obtains the current curvature value and current rate of change of curvature of the vehicle at the current lane position; determines the current curvature gain based on the current rate of change of curvature, and optimizes the current curvature gain based on the relationship between the current rate of change of curvature and the current curvature value to obtain an optimized curvature gain; determines the current lane curvature based on the current curvature value, the current rate of change of curvature, the optimized curvature gain, and the longitudinal distance between the preset aiming point position and the vehicle; and determines the target vehicle speed using the current lane curvature and the current lateral acceleration of the vehicle, so as to control the vehicle to drive in the curve using the target vehicle speed. Therefore, after obtaining the current curvature value and current rate of change of curvature of the vehicle at its current lane position, this application needs to determine the current curvature gain corresponding to the current rate of change of curvature. Furthermore, it needs to optimize the current curvature gain based on the relationship between the current rate of change of curvature and the current curvature value to obtain an optimized curvature gain. Then, using the current curvature value, current rate of change of curvature, optimized curvature gain, and the longitudinal distance between the preset aiming point and the vehicle, the current lane curvature is determined. That is, this application does not directly solve for the current lane curvature using the current curvature value and current rate of change of curvature, but rather adds a corresponding current curvature gain to the current rate of change of curvature to ensure timeliness during cornering control. In addition, to ensure comfort and stability during cornering control, the current curvature gain needs to be further optimized based on the relationship between the current rate of change of curvature and the current curvature value to solve for the current lane curvature. Finally, the target speed can be determined using the currently calculated lane curvature and the vehicle's current lateral acceleration, and then used to control the vehicle's movement in the curve. In this way, this application optimizes the process of calculating the current lane curvature, ensuring comfort, stability, and responsiveness during curve control in the adaptive cruise control system. Attached Figure Description

[0039] To more clearly illustrate the technical solutions in the embodiments of 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 the present invention. For those skilled in the art, other drawings can be obtained based on the provided drawings without creative effort.

[0040] Figure 1 This is a schematic diagram of a pre-aiming point and the current point of the vehicle as disclosed in this application;

[0041] Figure 2 This is a flowchart of a curve control method for an adaptive cruise control system disclosed in this application;

[0042] Figure 3 This is a flowchart of a specific adaptive cruise control system curve control method disclosed in this application;

[0043] Figure 4 This is a diagram showing the correspondence between a ratio and a curvature gain value rule disclosed in this application.

[0044] Figure 5 This is a schematic diagram illustrating the fluctuation of lane curvature as disclosed in this application;

[0045] Figure 6 This is a schematic diagram of the curve control device structure of an adaptive cruise control system disclosed in this application;

[0046] Figure 7 This is a structural diagram of an electronic device disclosed in this application. Detailed Implementation

[0047] 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 the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of the present invention.

[0048] Current designs for cornering deceleration rely on real-time road curvature control. However, sensor measurements of lane curvature are unstable, and curvature is only detected when the vehicle is already in a curve. This results in slow deceleration upon entering a curve, and the vehicle frequently accelerates and decelerates repeatedly within the curve, leading to a poor driving experience. To address this, this application discloses a cornering control method, device, equipment, and medium for an adaptive cruise control system, ensuring comfort, stability, and timeliness during cornering control.

[0049] See Figure 2 As shown in the figure, this application discloses a curve control method for an adaptive cruise control system, the method comprising:

[0050] Step S11: Obtain the current curvature value and current rate of change of curvature of the vehicle in the current lane.

[0051] In this embodiment, the current curvature value and the rate of change of curvature of the vehicle in the current lane are obtained from the sensor output. The rate of change of curvature indicates how quickly the curvature changes. The rate of change of curvature and the current curvature differ in sign. In this embodiment, the current curvature is tentatively set to positive for a left curve. Therefore, a positive rate of change of curvature indicates that the current curvature is gradually increasing, meaning the curve radius is gradually decreasing; conversely, a negative rate of change of curvature indicates that the current curvature is gradually decreasing, meaning the curve radius is gradually increasing.

[0052] Currently, the design for reducing speed on curves is based on controlling the speed according to the real-time curvature of the road. The basic principle is based on the following formula:

[0053]

[0054]

[0055]

[0056] Where a is the lateral acceleration, v is the vehicle speed, r is the lane radius, and c is the lane curvature; from the above formula, it can be seen that the speed limit v in a curve is directly related to c, and the two are inversely proportional. The greater the lane curvature, the lower the vehicle speed. In other words, the smaller the lane radius, the lower the vehicle speed.

[0057] The calculation of curvature is basically based on the lane line equation to anticipate the curve, thereby limiting the vehicle's speed and reducing speed in advance. Therefore, the calculation of lane curvature c is mainly based on the following formula:

[0058] c = c0 + 1 / 2 × c1 × x;

[0059] Where c0 is the current curvature value of the vehicle in the current lane, c1 is the current rate of change of curvature, and x is the longitudinal distance between the preset aiming point and the vehicle.

[0060] Step S12: Determine the current curvature gain based on the current rate of change of curvature, and optimize the current curvature gain based on the relationship between the current rate of change of curvature and the current curvature value to obtain the optimized curvature gain.

[0061] In this embodiment, the current curvature gain is first determined based on the current rate of change of curvature. It is understood that, considering the instability of sensor measurements of lane curvature and the fact that curvature is only detected when the vehicle is currently in a curve, this results in slower deceleration when entering a curve. Therefore, to ensure timely deceleration in curves, this embodiment amplifies the lane curvature c. Since the calculation of c is directly related to the current rate of change of curvature, this embodiment adds a corresponding current curvature gain k to the current rate of change of curvature.

[0062] After increasing the current curvature gain k, the formula for calculating the lane curvature c becomes:

[0063] c = c0 + 1 / 2 × k × c1 × x.

[0064] In a specific implementation, determining the current curvature gain based on the current rate of curvature change includes: obtaining a pre-set mapping relationship between the rate of curvature change and the curvature gain; wherein, in the mapping relationship, the curvature gain is proportional to the absolute value of the rate of curvature change; and determining the current curvature gain corresponding to the current rate of curvature change based on the mapping relationship. It is understood that this application pre-sets a mapping relationship between the rate of curvature change and the curvature gain, which can specifically be in the form of a mapping table. In this mapping relationship, the curvature gain is proportional to the absolute value of the rate of curvature change. Therefore, after obtaining the current rate of curvature change, the corresponding current curvature gain can be obtained by looking up the table. For example, if the current curvature value c0 is positive and the current rate of change of curvature c1 is positive, and assuming that the corresponding curvature gain k = 2 is obtained based on c1, then the calculated lane curvature c is larger than c when k is not increased. In this way, the curve radius of the preset aiming point position will be artificially set smaller, as shown in Table 1. As a result, the vehicle speed will decrease more when it reaches the curve, so as to ensure timely deceleration in the curve.

[0065] Table 1

[0066] <![CDATA[c1]]> 0.0001 0.0001 x / m 50 50 k 1 2 c 0.0035 0.006 <![CDATA[r0 / m]]> 1000 1000 r / m 286 167

[0067] In Table 1, r0 is the curve radius at the current lane position, r is the curve radius at the preset aiming point position, and m represents the unit meter.

[0068] As described above, this embodiment increases the gain k of the curvature change rate c1, amplifying the lane curvature c when the absolute value of c1 is large, thus achieving timely deceleration. However, it also amplifies the instability and discomfort of deceleration in curves. Therefore, to ensure the comfort and stability of deceleration in curves, this embodiment further optimizes the current curvature gain based on the relationship between the current curvature change rate c1 and the current curvature value c0, obtaining the optimized curvature gain k'. In other words, this embodiment requires secondary processing of k.

[0069] As can be seen, this application does not directly use the current curvature value and the current rate of change of curvature to solve for the current lane curvature. Instead, it needs to add a corresponding current curvature gain on the basis of the current rate of change of curvature to ensure the timeliness of the curve control process. In addition, in order to ensure the comfort and stability of the curve control process, it is also necessary to further optimize the current curvature gain according to the relationship between the current rate of change of curvature and the current curvature value, so as to obtain the optimized curvature gain.

[0070] Step S13: Determine the current lane curvature based on the current curvature value, the current rate of change of curvature, the optimized curvature gain, and the longitudinal distance between the preset aiming point position and the vehicle.

[0071] In this embodiment, after obtaining the optimized curvature gain, the current lane curvature can be determined based on the current curvature value, the current rate of change of curvature, the optimized curvature gain, and the longitudinal distance between the preset aiming point and the vehicle. The formula for calculating the current lane curvature is as follows:

[0072] c = c0 + 1 / 2 × k' × c1 × x.

[0073] Step S14: Determine the target vehicle speed using the current lane curvature and the current lateral acceleration of the vehicle, and use the target vehicle speed to control the vehicle to drive in the curve.

[0074] In this embodiment, the target vehicle speed is determined using the currently calculated lane curvature and the vehicle's current lateral acceleration, and then the vehicle is controlled to navigate the curve using the target speed. In this way, this application optimizes the process of calculating the current lane curvature, ensuring comfort, stability, and timeliness during curve control in the adaptive cruise control system.

[0075] As can be seen, this application obtains the current curvature value and current rate of change of curvature of the vehicle at the current lane position, and determines the current curvature gain corresponding to the current rate of change of curvature; optimizes the current curvature gain based on the relationship between the current rate of change of curvature and the current curvature value to obtain an optimized curvature gain; determines the current lane curvature based on the current curvature value, the current rate of change of curvature, the optimized curvature gain, and the longitudinal distance between the preset aiming point position and the vehicle; and determines the target vehicle speed using the current lane curvature and the current lateral acceleration of the vehicle, so as to control the vehicle to drive in the curve using the target vehicle speed. Therefore, after obtaining the current curvature value and current rate of change of curvature of the vehicle at its current lane position, this application needs to determine the current curvature gain corresponding to the current rate of change of curvature. Furthermore, it needs to optimize the current curvature gain based on the relationship between the current rate of change of curvature and the current curvature value to obtain an optimized curvature gain. Then, using the current curvature value, current rate of change of curvature, optimized curvature gain, and the longitudinal distance between the preset aiming point and the vehicle, the current lane curvature is determined. In other words, this application does not directly solve for the current lane curvature using the current curvature value and current rate of change of curvature. Instead, it adds a corresponding current curvature gain to the current rate of change of curvature to ensure timeliness during cornering control. Additionally, to ensure comfort and stability during cornering control, the current curvature gain needs further optimization based on the relationship between the current rate of change of curvature and the current curvature value to solve for the current lane curvature. Finally, the target speed can be determined using the currently calculated lane curvature and the vehicle's current lateral acceleration, and then used to control the vehicle's movement in the curve. In this way, this application optimizes the process of calculating the current lane curvature, ensuring comfort, stability, and responsiveness during curve control in the adaptive cruise control system.

[0076] See Figure 3 As shown, this application discloses a specific curve control method for an adaptive cruise control system. Compared to the previous embodiment, this embodiment further explains and optimizes the technical solution. Specifically, it includes:

[0077] Step S21: Obtain the current curvature value and current rate of change of curvature of the vehicle at the current lane position, and determine the current curvature gain based on the current rate of change of curvature.

[0078] Step S22: Optimize the current curvature gain based on the target ratio between the current rate of change of curvature and the current curvature value to obtain the optimized curvature gain.

[0079] In this embodiment, experimental calculations show that, with the current rate of change of curvature c1 remaining constant, its impact on the current curvature value c0 varies as c0 increases. As shown in Table 2, it can be seen that the smaller c0 is, the more sensitive it is to c1.

[0080] Table 2

[0081]

[0082] Simultaneously, using the ratio e of c1 / c0, it can be seen that the sensitivity of c0 to c1 is directly proportional to e. This leads to the conclusion that even if c1 changes significantly while the vehicle is in a curve, it will not have an excessive impact on the curve radius. Therefore, without secondary processing of k, k will increase the fluctuation of c when the vehicle is in a curve. The secondary processing method involves optimizing the current curvature gain based on the target ratio e between the current rate of change of curvature and the current curvature value, resulting in the optimized curvature gain.

[0083] In a specific implementation, the above-mentioned optimization of the current curvature gain based on the target ratio between the current rate of change of curvature and the current curvature value to obtain the optimized curvature gain includes: obtaining a pre-set correspondence between the ratio and the curvature gain value rule; determining the target ratio between the current rate of change of curvature and the current curvature value, and obtaining the target curvature gain value rule corresponding to the target ratio based on the correspondence; and optimizing the current curvature gain using the curvature gain value rule to obtain the optimized curvature gain. That is, in this embodiment, a pre-set correspondence between the ratio and the curvature gain value rule is established, for example, by creating a correspondence table or a correspondence graph. Then, after determining the target ratio between the current rate of change of curvature and the current curvature value, the target curvature gain value rule corresponding to the target ratio is obtained based on the correspondence; and the current curvature gain is optimized using the curvature gain value rule to obtain the optimized curvature gain.

[0084] In this embodiment, the correspondence between the ratio and the curvature gain value rule is shown in Table 3:

[0085] Table 3

[0086] k' k 1

[0087] The correspondence between the ratio and the curvature gain value rules is shown in the figure below. Figure 4 As shown in Table 3 and Figure 4It can be concluded that when the target ratio between the current rate of curvature change and the current curvature value is less than or equal to 0.1, the optimized curvature gain is always 1. When the target ratio is greater than or equal to 0.2, there is no need to optimize the current curvature gain, that is, the current curvature gain obtained above remains unchanged. When the target ratio is between 0.1 and 0.2, linear interpolation is performed in the range of 1 to k to obtain the optimized curvature gain.

[0088] In this way, the influence of k on c can be avoided when in a curve, thus ensuring comfort and stability.

[0089] Step S23: Determine the current lane curvature based on the current curvature value, the current rate of change of curvature, the optimized curvature gain, and the longitudinal distance between the preset aiming point position and the vehicle.

[0090] Step S24: Determine the current filter coefficient based on the initial filter coefficient and the preset filter coefficient adjustment rules, and perform filtering processing on the current lane curvature based on the current filter coefficient to obtain the current filtered lane curvature.

[0091] In this embodiment, although the aforementioned content has already added restrictions on the curvature gain k, since c0 and c1 are acquired by sensors, they inevitably contain some noise. Therefore, the calculated current lane curvature c still needs final optimization. This embodiment adopts a method of limiting the filtering coefficient. Specifically, the current filtering coefficient is first determined based on the initial filtering coefficient and the preset filtering coefficient adjustment rules. Then, the current filtering coefficient is used to filter the current lane curvature c to obtain the current filtered lane curvature. It should be noted that since ACC is a longitudinal control, this embodiment does not focus on the sign of the current lane curvature c, but only on its magnitude. Therefore, the absolute value of c is used in the control. The purpose of filtering is to smooth the input, but there is inevitably a certain delay. To ensure timely deceleration on curves, this embodiment only performs filtering when c decreases.

[0092] In a specific implementation, the determination of the current filter coefficient based on the initial filter coefficient and a preset filter coefficient adjustment rule specifically includes: obtaining the difference between the current lane curvature and the lane curvature after the previous filter, and determining the positive or negative relationship between the current curvature value and the current rate of change of curvature; adjusting the initial filter coefficient based on the difference and the positive or negative relationship to obtain the current filter coefficient. That is, for the current filter coefficient, this embodiment adjusts it according to the difference between the current lane curvature and the lane curvature after the previous filter, and the positive or negative relationship between the current curvature value and the current rate of change of curvature. The filtering formula is as follows:

[0093] c' = c old +g×c diff;

[0094] Where c' is the current lane curvature after filtering, g is the current filtering coefficient, and c diff The current lane curvature c and the lane curvature c after the previous filter are given. old The difference.

[0095] Specifically, the current filter coefficient is directly proportional to the magnitude of the difference; and when the current curvature value and the current rate of change of curvature have different signs, the current filter coefficient is directly proportional to the absolute value of the current rate of change of curvature; when the current curvature value and the current rate of change of curvature have the same sign, the current filter coefficient is the initial filter coefficient.

[0096] It is understandable that, such as Figure 5 As indicated by the solid black line, 'c' can exhibit both large-period fluctuations and small-period fluctuations. The handling of small fluctuations depends on 'c'. diff The magnitude of c is used to adjust the current filter coefficient g. diff The larger the value of g, the larger the value of g, and vice versa. That is, the current filter coefficient is directly proportional to the difference between the current lane curvature and the lane curvature after the previous filter. This ensures both the stability and timeliness of the lane curvature. Figure 5 The dotted lines indicate that small fluctuations are followed slowly, while large fluctuations are followed more quickly.

[0097] At the same time, for the sake of stability, according to c diff The obtained value of g is relatively small. Therefore, when exiting a curve, due to the filtering delay, c1 will have a significant delay compared to the actual c0, resulting in a slower acceleration out of the curve. Thus, this embodiment adjusts g by simultaneously considering the positive and negative relationship between the current curvature value and the current rate of change of curvature.

[0098] As mentioned earlier, the sign of the current curvature c0 and the current rate of change of curvature c1 affects the filter coefficient. Therefore, this design amplifies g based on the absolute value of the current rate of change of curvature when the signs of the current curvature value and the current rate of change of curvature are opposite. In other words, the current filter coefficient is directly proportional to the absolute value of the current rate of change of curvature. For example, if c0 is positive and c1 is negative, meaning the vehicle is entering a left-hand curve and is in the latter half of the curve (i.e., the exit point), increasing g allows c1 to approach the lane curvature more quickly, ensuring the vehicle can reach the set speed before fully exiting the curve, thus guaranteeing timely and comfortable deceleration during curves. Furthermore, when the signs of the current curvature value and the current rate of change of curvature are the same, the current filter coefficient is the initial filter coefficient, meaning g does not need to be adjusted.

[0099] Step S25: Determine the target vehicle speed using the current filtered lane curvature and the current lateral acceleration of the vehicle, and use the target vehicle speed to control the vehicle to drive in the curve.

[0100] In this embodiment, the target vehicle speed is determined by using the current filtered lane curvature and the current lateral acceleration of the vehicle. The target vehicle speed is then used to control the vehicle's driving in the curve, enabling the ACC to control the vehicle to drive comfortably and smoothly in the curve based on the lane curvature, and to perform acceleration and deceleration control in a timely manner.

[0101] The following example illustrates the technical solution of this application, using the vehicle's current curvature value c0 = 0.0005, current rate of change of curvature c1 = 0.0001, current lateral acceleration a = , the longitudinal distance between the preset aiming point and the vehicle x = 50, and the current curvature gain k = 2 as an example. It is assumed that the current rate of change of curvature and the current curvature have the same sign.

[0102] First, the ratio e of c1 / c0 is calculated to be 0.2, according to Table 3 and Figure 4 It can be seen that when the ratio is 0.2, there is no need to optimize the current curvature gain, that is, keep the current curvature gain k=2 unchanged, that is, the optimized curvature gain k'=k=2.

[0103] Then, using the formula for calculating the current lane curvature c = c0 + 1 / 2 × k' × c1 × x, we obtain c = 0.0055. Furthermore, this embodiment also needs to apply the filtering formula c' = c old +g×c diff The current lane curvature c is filtered to obtain the current filtered lane curvature. Assuming the previous filtered lane curvature c... old If the current filter coefficient g is 0.004 and the current filter coefficient g is 0.9, then the current lane curvature obtained by the above filtering formula is 0.00535.

[0104] Calculate the final vehicle speed.

[0105] For more detailed processing of steps S21 and S23, please refer to the relevant content disclosed in the foregoing embodiments, which will not be repeated here.

[0106] As can be seen, in this embodiment, the current curvature gain is optimized based on a target ratio between the current rate of change of curvature and the current curvature value to obtain an optimized curvature gain. By pre-setting the correspondence between the ratio and the curvature gain value rule, and then determining the target ratio between the current rate of change of curvature and the current curvature value, the target curvature gain value rule corresponding to the target ratio is obtained based on the correspondence. This curvature gain rule is then used to optimize the current curvature gain, resulting in an optimized curvature gain. This ensures that the curvature gain does not affect the lane curvature when in a curve, guaranteeing comfort and stability. Furthermore, since the current curvature value and the current rate of change of curvature are obtained by sensors, this embodiment uses a limited filtering coefficient to filter the current lane curvature to obtain the current filtered lane curvature. The current filtering coefficient is adjusted based on the difference between the current lane curvature and the previous filtered lane curvature, as well as the positive or negative relationship between the current curvature value and the current rate of change of curvature. Finally, the target speed is determined by using the current filtered lane curvature and the vehicle's current lateral acceleration. The target speed is then used to control the vehicle's movement in the curve, enabling the ACC to control the vehicle's comfortable and stable driving in the curve based on the lane curvature, and to perform timely acceleration and deceleration control.

[0107] See Figure 6 As shown in the figure, this application discloses a curve control device for an adaptive cruise control system, the device comprising:

[0108] Vehicle information acquisition 11 is used to obtain the current curvature value and current rate of change of curvature of the vehicle in the current lane position;

[0109] The curvature gain determination module 12 is used to determine the current curvature gain based on the current rate of change of curvature, and to optimize the current curvature gain based on the relationship between the current rate of change of curvature and the current curvature value, so as to obtain the optimized curvature gain.

[0110] The lane curvature determination module 13 is used to determine the current lane curvature based on the current curvature value, the current curvature change rate, the optimized curvature gain, and the longitudinal distance between the preset aiming point position and the vehicle.

[0111] The curve control module 14 is used to determine a target vehicle speed using the current lane curvature and the current lateral acceleration of the vehicle, so as to control the vehicle to drive in the curve using the target vehicle speed.

[0112] As can be seen, this application obtains the current curvature value and current rate of change of curvature of the vehicle at the current lane position; determines the current curvature gain based on the current rate of change of curvature, and optimizes the current curvature gain based on the relationship between the current rate of change of curvature and the current curvature value to obtain an optimized curvature gain; determines the current lane curvature based on the current curvature value, the current rate of change of curvature, the optimized curvature gain, and the longitudinal distance between the preset aiming point position and the vehicle; and determines the target vehicle speed using the current lane curvature and the current lateral acceleration of the vehicle, so as to control the vehicle to drive in the curve using the target vehicle speed. Therefore, after obtaining the current curvature value and current rate of change of curvature of the vehicle at its current lane position, this application needs to determine the current curvature gain corresponding to the current rate of change of curvature. Furthermore, it needs to optimize the current curvature gain based on the relationship between the current rate of change of curvature and the current curvature value to obtain an optimized curvature gain. Then, using the current curvature value, current rate of change of curvature, optimized curvature gain, and the longitudinal distance between the preset aiming point and the vehicle, the current lane curvature is determined. That is, this application does not directly solve for the current lane curvature using the current curvature value and current rate of change of curvature, but rather adds a corresponding current curvature gain to the current rate of change of curvature to ensure timeliness during cornering control. In addition, to ensure comfort and stability during cornering control, the current curvature gain needs to be further optimized based on the relationship between the current rate of change of curvature and the current curvature value to solve for the current lane curvature. Finally, the target speed can be determined using the currently calculated lane curvature and the vehicle's current lateral acceleration, and then used to control the vehicle's movement in the curve. In this way, this application optimizes the process of calculating the current lane curvature, ensuring comfort, stability, and responsiveness during curve control in the adaptive cruise control system.

[0113] Figure 7 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application. Specifically, it may include: at least one processor 21, at least one memory 22, a power supply 23, a communication interface 24, an input / output interface 25, and a communication bus 26. The memory 22 stores a computer program, which is loaded and executed by the processor 21 to implement the relevant steps in the curve control method of the adaptive cruise control system executed by the electronic device disclosed in any of the foregoing embodiments.

[0114] In this embodiment, the power supply 23 is used to provide operating voltage for each hardware device on the electronic device 20; the communication interface 24 can create a data transmission channel between the electronic device 20 and external devices, and the communication protocol it follows can be any communication protocol applicable to the technical solution of this application, and is not specifically limited here; the input / output interface 25 is used to acquire external input data or output data to the outside world, and its specific interface type can be selected according to specific application needs, and is not specifically limited here.

[0115] The processor 21 may include one or more processing cores, such as a quad-core processor or an octa-core processor. The processor 21 may be implemented using at least one hardware form selected from DSP (Digital Signal Processing), FPGA (Field-Programmable Gate Array), and PLA (Programmable Logic Array). The processor 21 may also include a main processor and a coprocessor. The main processor, also known as a CPU (Central Processing Unit), is used to process data in the wake-up state; the coprocessor is a low-power processor used to process data in the standby state. In some embodiments, the processor 21 may integrate a GPU (Graphics Processing Unit), which is responsible for rendering and drawing the content to be displayed on the screen. In some embodiments, the processor 21 may also include an AI (Artificial Intelligence) processor, which is used to handle computational operations related to machine learning.

[0116] In addition, the memory 22, as a carrier for resource storage, can be a read-only memory, random access memory, disk or optical disk, etc. The resources stored on it include operating system 221, computer program 222 and data 223, etc., and the storage method can be temporary storage or permanent storage.

[0117] The operating system 221 manages and controls the various hardware devices and computer programs 222 on the electronic device 20 to enable the processor 21 to perform calculations and processing on the massive amounts of data 223 in the memory 22. The operating system 221 can be Windows, Unix, Linux, etc. The computer program 222, in addition to including a computer program capable of performing the cornering control method of the adaptive cruise control system executed by the electronic device 20 as disclosed in any of the foregoing embodiments, may further include computer programs capable of performing other specific tasks. The data 223 may include data received by the electronic device from external devices, as well as data collected by its own input / output interface 25.

[0118] Furthermore, this application also discloses a computer-readable storage medium storing a computer program. When the computer program is loaded and executed by a processor, it implements the cornering control method steps of the adaptive cruise control system disclosed in any of the foregoing embodiments.

[0119] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on its differences from other embodiments. Similar or identical parts between embodiments can be referred to interchangeably. For the apparatus disclosed in the embodiments, since it corresponds to the method disclosed in the embodiments, the description is relatively simple; relevant parts can be referred to in the method section.

[0120] Those skilled in the art will further recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of both. To clearly illustrate the interchangeability of hardware and software, the components and steps of the various examples have been generally described in terms of functionality in the foregoing description. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0121] The steps of the methods or algorithms described in conjunction with the embodiments disclosed herein can be implemented directly by hardware, a software module executed by a processor, or a combination of both. The software module can be located in random access memory (RAM), main memory, read-only memory (ROM), electrically programmable ROM, electrically erasable programmable ROM, registers, hard disk, removable disk, compact disc read-only memory (CD-ROM), or any other form of storage medium known in the art.

[0122] Finally, it should be noted that in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0123] The foregoing has provided a detailed description of the curve control method, apparatus, device, and storage medium of an adaptive cruise control system provided by the present invention. Specific examples have been used to illustrate the principles and implementation methods of the present invention. The descriptions of the above embodiments are only for the purpose of helping to understand the method and core ideas of the present invention. At the same time, for those skilled in the art, there will be changes in the specific implementation methods and application scope based on the ideas of the present invention. Therefore, the content of this specification should not be construed as a limitation of the present invention.

Claims

1. A curve control method for an adaptive cruise control system, characterized in that, include: Obtain the current curvature value and current rate of change of curvature of the vehicle in the current lane; The current curvature gain is determined based on the current rate of change of curvature, and the current curvature gain is optimized based on the relationship between the current rate of change of curvature and the current curvature value to obtain the optimized curvature gain. The current lane curvature is determined based on the current curvature value, the current rate of change of curvature, the optimized curvature gain, and the longitudinal distance between the preset aiming point and the vehicle. The target vehicle speed is determined using the current lane curvature and the current lateral acceleration of the vehicle, and the vehicle is controlled to drive in the curve using the target vehicle speed; The step of determining the current curvature gain based on the current rate of change of curvature includes: Obtain a pre-set mapping relationship between the rate of curvature change and the curvature gain; wherein, in the mapping relationship, the curvature gain is proportional to the absolute value of the rate of curvature change; Based on the mapping relationship, the current curvature gain corresponding to the current rate of curvature change is determined; The optimization of the current curvature gain based on the relationship between the current rate of change of curvature and the current curvature value, to obtain the optimized curvature gain, includes: Obtain the correspondence between the pre-set ratio and the curvature gain value rule; Determine the target ratio between the current rate of change of curvature and the current curvature value, and obtain the target curvature gain value rule corresponding to the target ratio based on the correspondence. The current curvature gain is optimized using the curvature gain value rule to obtain the optimized curvature gain.

2. The curve control method of the adaptive cruise control system according to claim 1, characterized in that, After determining the current lane curvature based on the current curvature value, the current rate of change of curvature, the optimized curvature gain, and the longitudinal distance between the preset aiming point position and the vehicle, the process further includes: The current filter coefficients are determined based on the initial filter coefficients and the preset filter coefficient adjustment rules; The current lane curvature is filtered based on the current filter coefficients to obtain the current filtered lane curvature. Accordingly, determining the target vehicle speed using the current lane curvature and the vehicle's current lateral acceleration includes: The target vehicle speed is determined using the current filtered lane curvature and the current lateral acceleration of the vehicle.

3. The curve control method of the adaptive cruise control system according to claim 2, characterized in that, The determination of the current filter coefficients based on the initial filter coefficients and preset filter coefficient adjustment rules includes: Obtain the difference between the current lane curvature and the previous filtered lane curvature, and determine the positive or negative relationship between the current curvature value and the current rate of change of curvature; The current filter coefficients are obtained by adjusting the initial filter coefficients based on the magnitude of the difference and the positive / negative relationship.

4. The curve control method of the adaptive cruise control system according to claim 3, characterized in that, The current filtering coefficient is proportional to the magnitude of the difference; and when the current curvature value and the current rate of change of curvature have different signs, the current filtering coefficient is proportional to the absolute value of the current rate of change of curvature; when the current curvature value and the current rate of change of curvature have the same sign, the current filtering coefficient is the initial filtering coefficient.

5. A curve control device for an adaptive cruise control system, characterized in that, include: The vehicle information acquisition module is used to acquire the current curvature value and current rate of change of curvature of the vehicle in the current lane. The curvature gain determination module is used to determine the current curvature gain based on the current rate of change of curvature, and to optimize the current curvature gain based on the relationship between the current rate of change of curvature and the current curvature value, so as to obtain the optimized curvature gain. The lane curvature determination module is used to determine the current lane curvature based on the current curvature value, the current curvature change rate, the optimized curvature gain, and the longitudinal distance between the preset aiming point position and the vehicle. A curve control module is used to determine a target speed using the current lane curvature and the current lateral acceleration of the vehicle, so as to control the vehicle to drive in a curve using the target speed. Specifically, the curvature gain determination module is used to obtain a pre-set mapping relationship between the rate of curvature change and the curvature gain; wherein, in the mapping relationship, the curvature gain is proportional to the absolute value of the rate of curvature change; determine the current curvature gain corresponding to the current rate of curvature change based on the mapping relationship; obtain a pre-set correspondence between a ratio and a curvature gain value rule; determine a target ratio between the current rate of curvature change and the current curvature value, and obtain a target curvature gain value rule corresponding to the target ratio based on the correspondence; and optimize the current curvature gain using the curvature gain value rule to obtain an optimized curvature gain.

6. An electronic device, characterized in that, include: Memory, used to store computer programs; A processor for executing the computer program to implement the steps of the curve control method of the adaptive cruise control system as described in any one of claims 1 to 4.

7. A computer-readable storage medium, characterized in that, Used to store a computer program; wherein, when the computer program is executed by a processor, it implements the steps of the curve control method of the adaptive cruise control system as described in any one of claims 1 to 4.

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