ACC lane changing acceleration self-learning method and device

Abstract

The invention discloses an ACC lane changing acceleration self-learning method and device, and relates to the technical field of ACC. The method comprises the steps that when a vehicle meets ACC lane changing acceleration self-learning activation conditions and it is detected that vehicle lane changing is driver lane changing in a human driving mode, lane changing data of the vehicle in the lane changing process is obtained after lane changing of the vehicle is completed; updating the initial waiting acceleration time value based on the lane changing data to obtain an updated waiting acceleration time value; determining a target acceleration deviation according to the lane changing data; updating the acceleration calibration table through the target acceleration deviation to obtain an updated acceleration calibration table; lane-changing acceleration self-learning is completed through the updated acceleration waiting time value and the updated acceleration calibration table, and the acceleration calibration table is updated through the target acceleration deviation, so that the value in the acceleration calibration table is continuously close to the real driver lane-changing parameter and fits the driving habit of a user, and the use experience of the user is improved.

Description

Technical Field

[0001] The present application relates to the field of ACC technology, and in particular to an ACC lane change acceleration self-learning method and device. Background Art

[0002] Current vehicles are generally equipped with ACC (Adaptive Cruise Control) function, but in actual use, ACC's lane change acceleration function may not fit the driver's usage habits well, and may even cause users to panic when they first start using the function because the timing of the action does not meet the driver's expectations.

[0003] The current ACC lane change acceleration control mainly uses fixed parameters that have been calibrated in advance, but different users have different driving habits, and using fixed parameters will affect the user's driving experience. Summary of the Invention

[0004] The main purpose of this application is to provide an ACC lane change acceleration self-learning method and device, aiming to solve the technical problem that ACC lane change acceleration control cannot adapt to user driving habits.

[0005] To achieve the above objectives, the present application proposes an ACC lane change acceleration self-learning method, which includes:

[0006] When the vehicle meets the ACC lane change acceleration self-learning activation conditions and detects that the vehicle is changing lanes in human driving mode, the lane change data of the vehicle during the lane change process is obtained after the vehicle completes the lane change;

[0007] updating the initial waiting acceleration time value based on the lane change data to obtain an updated waiting acceleration time value;

[0008] determining a target acceleration deviation based on the lane change data;

[0009] Updating the acceleration calibration table according to the target acceleration deviation to obtain an updated acceleration calibration table;

[0010] The lane change acceleration self-learning is completed by updating the waiting acceleration time value and the updating acceleration calibration table.

[0011] In one embodiment, the step of updating the initial waiting acceleration time value based on the lane change data to obtain an updated waiting acceleration time value includes:

[0012] obtaining, based on the lane change data, a driver's accelerator pedal pressing time when changing lanes;

[0013] Get the update coefficient and initial waiting acceleration time value;

[0014] The initial waiting acceleration time value is updated according to the initial waiting acceleration time value, the accelerator pedal time, and the update coefficient to obtain an updated waiting acceleration time value.

[0015] In one embodiment, the step of determining the target acceleration deviation based on the lane change data includes:

[0016] Obtain the historical speed average of the previous cycle, and calculate the current speed average based on the historical speed average;

[0017] determining, from the lane change data based on the speed average value, a first vehicle speed and a second vehicle speed of the driver during the lane change process within a range corresponding to the speed average value, wherein the first vehicle speed is less than the second vehicle speed;

[0018] querying the acceleration calibration table according to the first vehicle speed and the second vehicle speed to obtain a first acceleration calibration value and a second acceleration calibration value at a preset time, wherein the acceleration calibration table stores a correspondence between vehicle speed, lane change acceleration time value, and acceleration calibration value;

[0019] Obtaining an actual acceleration value of the velocity average;

[0020] Calculating a first acceleration deviation of the velocity average value by using the first acceleration calibration value, the second acceleration calibration value, and the actual acceleration value;

[0021] A target acceleration deviation is determined based on the first acceleration deviation.

[0022] In one embodiment, the step of determining a target acceleration deviation based on the first acceleration deviation includes:

[0023] calculating a second acceleration deviation according to the first acceleration deviation, the speed average, and the first vehicle speed;

[0024] calculating a third acceleration deviation according to the first acceleration deviation, the speed average, and the second vehicle speed;

[0025] A target acceleration deviation is obtained according to the second acceleration deviation and the third acceleration deviation.

[0026] In one embodiment, the step of updating the acceleration calibration table according to the target acceleration deviation to obtain an updated acceleration calibration table includes:

[0027] Obtaining a second acceleration deviation and a third acceleration deviation according to the target acceleration deviation;

[0028] Obtaining an update coefficient, a first acceleration calibration value, and a second acceleration calibration value;

[0029] updating the first acceleration calibration value in the acceleration calibration table according to the second acceleration deviation, the update coefficient, and the first acceleration calibration value to obtain an updated first acceleration calibration value;

[0030] updating the second acceleration calibration value in the acceleration calibration table according to the third acceleration deviation, the update coefficient, and the second acceleration calibration value to obtain an updated second acceleration calibration value;

[0031] An updated acceleration calibration table is obtained according to the updated first acceleration calibration value and the updated second acceleration calibration value.

[0032] In one embodiment, when the vehicle satisfies the ACC lane change acceleration self-learning activation conditions and detects that the vehicle is changing lanes by the driver in the human driving mode, before the step of obtaining the lane change data of the vehicle during the lane change process after the vehicle completes the lane change, the step further includes:

[0033] Get the current vehicle status, current speed, vehicle acceleration, vehicle turn signal status, and lane change status;

[0034] When the current vehicle state is that the vehicle is in a stable following state, the current vehicle speed is greater than a preset vehicle speed threshold, the vehicle turn signal state is that the left turn signal is on, and the lane change lane state is that there is no preceding vehicle within a preset range, start timing and record the current throttle opening;

[0035] When the change rate of the current throttle opening is greater than a preset change rate value, the current throttle opening is greater than a preset throttle opening threshold, and the vehicle acceleration is greater than a preset value, it is determined that the vehicle meets the ACC lane change acceleration self-learning activation condition.

[0036] In one embodiment, after completing the lane change acceleration self-learning by updating the waiting acceleration time value and updating the acceleration calibration table, the method further includes:

[0037] Using the updated waiting acceleration time value as the initial waiting acceleration time value and replacing the acceleration calibration table with the updated acceleration calibration table;

[0038] When the ACC function of the vehicle is activated and the vehicle meets the lane change acceleration activation conditions, querying the target acceleration calibration value from the acceleration calibration table according to the current lane change acceleration time value and the current vehicle speed;

[0039] The vehicle is subjected to lane change acceleration control by using the target acceleration calibration value.

[0040] In one embodiment, before the step of obtaining the current waiting acceleration time value and the current vehicle speed when the ACC function of the vehicle is activated and the vehicle meets the lane change acceleration activation condition, the step further includes:

[0041] After the vehicle's ACC function is activated, obtain the current vehicle status, current speed, vehicle turn signal status, and lane change lane status;

[0042] When the current vehicle state is that the vehicle is in a following vehicle driving state, the vehicle turn signal state is that the left turn signal of the vehicle is on, the current vehicle speed is greater than a preset vehicle speed threshold, the deviation between the current vehicle speed and the set vehicle speed is greater than a preset deviation value, and the lane change lane state is that there is no preceding vehicle in the lane change lane within a preset range, the timing is started;

[0043] When the timing number is greater than the initial waiting acceleration time value, it is determined that the vehicle meets the lane change acceleration activation condition.

[0044] In one embodiment, the step of performing lane change acceleration control on the vehicle using the target acceleration calibration value includes:

[0045] performing lane change acceleration on the vehicle using the target acceleration calibration value;

[0046] During vehicle acceleration, obtain vehicle steering wheel angle and lane line data;

[0047] When it is detected within a preset steering wheel operation time that the distance between the vehicle and the lane change lane in the lane line data is shortened and the vehicle steering wheel angle is greater than or equal to a preset angle value, obtaining the current vehicle position;

[0048] When the current vehicle position is in the lane change lane and within the target range of the lane change lane, the vehicle is controlled to complete lane change acceleration.

[0049] In addition, to achieve the above-mentioned purpose, the present application also proposes an ACC lane change acceleration self-learning device, which includes:

[0050] An acquisition module is used to acquire lane change data of the vehicle during the lane change process after the vehicle completes the lane change when the vehicle meets the ACC lane change acceleration self-learning activation conditions and detects that the vehicle changes lanes by the driver in the human driving mode;

[0051] an updating module, configured to update the initial waiting acceleration time value based on the lane change data to obtain an updated waiting acceleration time value;

[0052] a determination module, configured to determine a target acceleration deviation based on the lane change data;

[0053] The updating module is further configured to update the acceleration calibration table according to the target acceleration deviation to obtain an updated acceleration calibration table;

[0054] The lane change acceleration self-learning module is used to complete the lane change acceleration self-learning by updating the waiting acceleration time value and the updating acceleration calibration table.

[0055] In addition, to achieve the above-mentioned purpose, the present application also proposes an ACC lane change acceleration self-learning device, which includes: a memory, a processor, and a computer program stored on the memory and runnable on the processor, and the computer program is configured to implement the steps of the ACC lane change acceleration self-learning method described above.

[0056] In addition, to achieve the above-mentioned purpose, the present application also proposes a storage medium, which is a computer-readable storage medium. A computer program is stored on the storage medium. When the computer program is executed by the processor, the steps of the ACC lane change acceleration self-learning method as described above are implemented.

[0057] In addition, to achieve the above-mentioned purpose, the present application also provides a computer program product, which includes a computer program, and when the computer program is executed by a processor, it implements the steps of the ACC lane change acceleration self-learning method as described above.

[0058] One or more technical solutions proposed in the present application are: when the vehicle meets the ACC lane change acceleration self-learning activation conditions and detects that the vehicle lane change is caused by the driver in the human driving mode, the lane change data of the vehicle in the lane change process is obtained after the vehicle completes the lane change; based on the lane change data, the initial waiting acceleration time value is updated to obtain an updated waiting acceleration time value; according to the lane change data, a target acceleration deviation is determined; the acceleration calibration table is updated by the target acceleration deviation to obtain an updated acceleration calibration table; the lane change acceleration self-learning is completed by the updated waiting acceleration time value and the updated acceleration calibration table, and the acceleration calibration table is updated by the target acceleration deviation, so that the values ​​in the acceleration calibration table are continuously close to the actual driver's lane change parameters, and the parameters in the lane change process can be self-learned to flexibly adjust the lane change parameters in the lane change process, thereby correcting the lane change acceleration process, fitting the user's driving habits, and improving the user experience. BRIEF DESCRIPTION OF THE DRAWINGS

[0059] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present application and, together with the description, serve to explain the principles of the present application.

[0060] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, for ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0061] Figure 1 This is a flowchart of the first embodiment of the ACC lane change acceleration self-learning method provided in this application;

[0062] Figure 2 This is a flowchart of the second embodiment of the ACC lane change acceleration self-learning method provided in this application;

[0063] Figure 3 This is a flowchart of the third embodiment of the ACC lane change acceleration self-learning method provided in this application;

[0064] Figure 4 This is a flowchart of the fourth embodiment of the ACC lane change acceleration self-learning method provided in this application;

[0065] Figure 5 This is a schematic diagram of the module structure of the ACC lane change acceleration self-learning device according to an embodiment of the present application;

[0066] Figure 6 This is a schematic diagram of the device structure of the hardware operating environment involved in the ACC lane change acceleration self-learning method in the embodiment of the present application.

[0067] The purpose, features and advantages of this application will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0068] It should be understood that the specific embodiments described herein are merely used to explain the technical solutions of the present application and are not intended to limit the present application.

[0069] In order to better understand the technical solution of the present application, a detailed description will be given below in conjunction with the accompanying drawings and specific implementation methods.

[0070] The main solution of the embodiment of the present application is: when the vehicle meets the ACC lane change acceleration self-learning activation conditions and it is detected that the vehicle changes lanes by the driver in the human driving mode, the lane change data of the vehicle during the lane change process is obtained after the vehicle completes the lane change; the initial waiting acceleration time value is updated based on the lane change data to obtain an updated waiting acceleration time value; the target acceleration deviation is determined according to the lane change data; the acceleration calibration table is updated by the target acceleration deviation to obtain an updated acceleration calibration table; and the lane change acceleration self-learning is completed by the updated waiting acceleration time value and the updated acceleration calibration table.

[0071] Because the existing technology uses fixed lane change parameters for ACC lane change acceleration control, due to differences between drivers, there will be differences in the timing and magnitude of acceleration when changing lanes. Therefore, it is difficult to meet the needs of all users when calibrating the lane change acceleration with fixed ACC parameters. More aggressive users will feel a slow response, while more gentle users will experience discomfort due to excessive acceleration. It cannot adapt to user habits and is likely to cause user complaints.

[0072] This application provides a solution to self-learn user habits for the lane change acceleration function of ACC, and based on the self-learning results, correct the entire ACC lane change acceleration process to achieve the purpose of adapting to user habits.

[0073] It should be noted that the execution entity of this embodiment can be a computing service device with data processing, network communication, and program execution capabilities, such as a tablet computer, personal computer, or mobile phone, or an electronic device capable of implementing the aforementioned functions, such as an ACC lane change acceleration self-learning device. This embodiment and the following embodiments will be described using the ACC lane change acceleration self-learning device as an example.

[0074] Based on this, the embodiment of the present application provides an ACC lane change acceleration self-learning method, referring to Figure 1 , Figure 1 This is a flowchart of the first embodiment of the ACC lane change acceleration self-learning method of this application.

[0075] In this embodiment, the ACC lane change acceleration self-learning method includes steps S10 to S50:

[0076] Step S10: When the vehicle meets the ACC lane change acceleration self-learning activation conditions and detects that the vehicle changes lanes by the driver in the human driving mode, the lane change data of the vehicle during the lane change process is obtained after the vehicle completes the lane change.

[0077] Before the vehicle activates lane change acceleration self-learning, it is possible to first determine whether the vehicle's ACC function is activated. If the vehicle's ACC function is activated and the vehicle meets the lane change acceleration conditions, the vehicle can be controlled to change lanes automatically through ACC. Specifically, the corresponding acceleration calibration value is first queried through a pre-set acceleration calibration table, and then the vehicle is controlled for lane change acceleration through the acceleration calibration value. Since this lane change acceleration control is a fixed acceleration calibration value queried, it cannot fit the user's habits. Therefore, during the subsequent user driving process, it is possible to determine whether the vehicle meets the ACC lane change acceleration self-learning activation conditions and detect whether the user is driving the vehicle to change lanes. If so, the lane change acceleration self-learning can be triggered, thereby continuously learning driving parameters that are more in line with user habits.

[0078] It should be noted that when a vehicle meets the activation conditions for ACC lane change acceleration self-learning, meaning the vehicle is currently capable of self-learning lane change acceleration parameters, the vehicle is operating in human-driven lane change mode. Human-driven mode here refers to driver-controlled longitudinal vehicle control, including both ACC-inactive and override modes when ACC is activated. Once the user completes the lane change, lane change data for the vehicle during the process can be obtained. ACC lane change acceleration self-learning continuously learns the driver's habits based on collected data while the driver is controlling the vehicle to accelerate through lane changes, thereby obtaining lane change parameters that best suit the driver's driving habits and improving lane change control effectiveness.

[0079] After the vehicle completes the lane change, the lane change data of the vehicle during the lane change process can be obtained, including the user's acceleration speed, acceleration time, etc., so as to perform lane change acceleration self-learning.

[0080] In a feasible implementation manner, before step S10, steps S01 to S03 may be further included:

[0081] Step S01: Obtain the current vehicle state, current vehicle speed, vehicle acceleration, vehicle turn signal state, and lane change lane state.

[0082] It should be noted that the current vehicle status refers to whether the vehicle is in a stable following driving state, the current vehicle speed can be detected by a speed sensor, the vehicle acceleration can be detected or calculated, the vehicle turn signal status can be whether the vehicle turn signal is turned on and the direction of the turned-on turn signal, etc. The lane change lane status is the vehicle information on the lane to be changed, that is, whether there is a leading or trailing vehicle in the lane to be changed.

[0083] Step S02: When the current vehicle state is that the vehicle is in a stable following state, the current vehicle speed is greater than a preset vehicle speed threshold, the vehicle turn signal state is that the vehicle's left turn signal is on, and the lane change lane state is that there is no preceding vehicle in the lane change lane within a preset range, start timing and record the current throttle opening.

[0084] It can be understood that when the vehicle is in a stable following state, that is, the distance between the vehicle and the leading vehicle is stable and the absolute value of the vehicle's acceleration is less than a certain value, if the vehicle's left turn signal is on and the current vehicle speed is greater than a preset speed threshold, it indicates that the driver has a lane change demand. When the vehicle has a lane change demand, it is necessary to accelerate to change lanes to the left. The preset speed threshold can be set to 70 kph, or other values. This embodiment does not impose any restrictions on this. When the current vehicle speed is greater than the preset speed threshold and there is no leading vehicle in the lane changing lane within a preset range (when the vehicle has a rear radar, it is also required that there is no following vehicle within a certain distance behind the lane changing lane), it indicates that the lane change can be made to the lane changing lane. For example, if the current vehicle speed is greater than 70 kph and there is no leading vehicle in the left lane within the preset range, the timer count = 0 starts at this time. The preset range can be calibrated according to the specific vehicle speed. For example, the preset range is set to 100 m. The greater the vehicle speed, the larger the preset range.

[0085] In a specific implementation, the current throttle opening pedalpercent_ini can be detected by a sensor installed on the accelerator pedal.

[0086] Step S03: When the change rate of the current throttle opening is greater than a preset change rate value, the current throttle opening is greater than a preset throttle opening threshold, and the vehicle acceleration is greater than a preset value, it is determined that the vehicle meets the ACC lane change acceleration self-learning activation condition.

[0087] In a specific implementation, the rate of change of the throttle opening can be continuously calculated based on the real-time detected throttle opening. The preset rate of change value can be set according to needs, such as 10%, 20%, etc. The preset throttle opening threshold can be set to pedalpercent_ini+δ, and the value of δ can be 5, 10, etc. When the rate of change of the current throttle opening is greater than the preset rate of change threshold and the current throttle opening is greater than the preset opening threshold, it is detected whether the vehicle acceleration is greater than the preset value. The preset value is 0. If the vehicle acceleration is greater than 0 at this time, it is determined that the vehicle meets the lane change acceleration self-learning activation condition. At this time, the timing can be stopped and the initial waiting acceleration time value count_accel is defined. The value is 0 when it is offline. For the working condition with initial count_accel=0, after collecting n (for example, n=3) waiting acceleration start times, the average value is taken, and count_accel=the average value of count3. After that, when self-learning is triggered again, the initial waiting acceleration time value count_accel can be updated.

[0088] The initial waiting acceleration time value count_accel is a critical value. When the vehicle needs to change lanes, acceleration can only begin when the waiting time is greater than count_accel.

[0089] Step S20: updating the initial waiting acceleration time value based on the lane change data to obtain an updated waiting acceleration time value.

[0090] In a specific implementation, the updated waiting acceleration time value can be calculated through the lane change data, so as to update the initial waiting acceleration time value. The updated waiting acceleration time value may be equal to or unequal to the initial waiting acceleration time value. If they are equal, it means that the initial waiting acceleration time value fits the user's driving habits. If they are unequal, it means that the initial waiting acceleration time value does not fit the user's driving habits and needs to be updated.

[0091] Step S30: determining a target acceleration deviation according to the lane change data.

[0092] In specific implementations, the target acceleration deviation is the acceleration deviation at different user acceleration speeds, i.e., the deviation from the corresponding data in the acceleration calibration table. This target acceleration deviation can be calculated based on lane change data to obtain the target acceleration deviation. The target acceleration deviation can also be 0, indicating that it is the same as the acceleration calibration value found in the acceleration calibration table, indicating no deviation.

[0093] Step S40: updating the acceleration calibration table according to the target acceleration deviation to obtain an updated acceleration calibration table.

[0094] It should be noted that the acceleration in the acceleration calibration table can be updated by the target acceleration deviation. If the target acceleration deviation is 0, the acceleration calibration table does not need to be updated in the current cycle. If the target acceleration deviation is not 0, the corresponding acceleration calibration value in the acceleration calibration table needs to be updated in the current cycle to obtain an updated acceleration calibration table.

[0095] Step S50: completing lane change acceleration self-learning by updating the waiting acceleration time value and the updating acceleration calibration table.

[0096] It can be understood that lane change acceleration self-learning can be completed by updating the waiting acceleration time value and updating the acceleration calibration table. Then, the next time the vehicle changes lanes and accelerates, the updated waiting acceleration time value and the acceleration in the updated acceleration calibration table can be directly used for lane change acceleration control. At the same time, after the lane change acceleration is completed, the lane change parameter self-learning continues according to the lane change data.

[0097] The present embodiment provides an ACC lane change acceleration self-learning method, which obtains lane change data of the vehicle during the lane change process after the vehicle completes the lane change when the vehicle meets the lane change acceleration self-learning activation conditions and detects that the vehicle lane change is caused by the driver in the human driving mode; updates the initial waiting acceleration time value based on the lane change data to obtain an updated waiting acceleration time value; determines a target acceleration deviation according to the lane change data; updates an acceleration calibration table according to the target acceleration deviation to obtain an updated acceleration calibration table; completes lane change acceleration self-learning through the updated waiting acceleration time value and the updated acceleration calibration table, updates the acceleration calibration table through the target acceleration deviation, so that the values ​​in the acceleration calibration table are continuously close to the actual driver's lane change parameters, and can flexibly adjust the lane change parameters during the lane change process by self-learning the parameters during the lane change process, thereby correcting the lane change acceleration process, fitting the user's driving habits, and improving the user experience.

[0098] Based on the first embodiment of the present application, in the second embodiment of the present application, the same or similar contents as those in the above embodiment 1 can be referred to the above introduction and will not be described in detail later. Figure 2 , step S20 includes steps S201 to S203:

[0099] Step S201: Obtaining the accelerator pedal pressing time of the driver when changing lanes based on the lane change data.

[0100] It should be noted that when the vehicle changes lanes, the current throttle opening pedalpercent_ini can be recorded, and the driver's accelerator pedal time when changing lanes can be determined based on the lane change data. Specifically, it can be determined based on the previously counted current throttle opening whether the driver further steps on the accelerator, that is, the time point when the vehicle begins to accelerate significantly, thereby obtaining the driver's accelerator pedal time when changing lanes.

[0101] Step S202: Obtain the update coefficient and the initial waiting acceleration time value.

[0102] It should be noted that the update coefficient is a relatively small value. The smaller the value, the slower and more stable the self-learning update, while the larger the value, the faster and more unstable the update. Calibration is required. For example, the update coefficient is set to 0.01. The initial waiting acceleration time value is the initial defined value. It can be calculated based on the count value. For example, after collecting n (for example, n = 3) waiting acceleration start times, take the average value and set count_accel = the average value of count3.

[0103] Step S203: updating the initial waiting acceleration time value according to the initial waiting acceleration time value, the accelerator pedal time, and the update coefficient to obtain an updated waiting acceleration time value.

[0104] In a specific implementation, the initial waiting acceleration time value can be updated by the initial waiting acceleration time value, the accelerator pedal time, and the update coefficient, as shown in the following formula:

[0105] count_accel'=count_accel+(accelerator pedal time-count_accel)*f

[0106] In the above formula, count_accel' is the updated waiting acceleration time value, count_accel on the right side of the equation is the initial waiting acceleration time value, and f is the update coefficient. The updated waiting acceleration time value can be calculated using the above formula.

[0107] For example, the initial waiting acceleration time value count_accel is calibrated to 3 seconds, and the update weight f = 0.01. However, the driver begins to further step on the accelerator at 2 seconds (which manifests as a significant increase in the vehicle's acceleration). Then, after the lane change is completed, the ACC internal parameter updates the lane change acceleration time value count_accel' = 3 + (2-3) * 0.01 = 2.99 seconds, that is, the initial waiting time acceleration value is updated from 3 seconds to 2.99 seconds.

[0108] This embodiment obtains the accelerator pedal time of the driver when changing lanes based on the lane change data; obtains an update coefficient and an initial waiting acceleration time value; updates the initial waiting acceleration time value according to the initial waiting acceleration time value, the accelerator pedal time, and the update coefficient to obtain an updated waiting acceleration time value; and self-learns the initial waiting time acceleration value according to the lane change data of the driver when changing lanes, thereby obtaining an acceleration waiting time that is more in line with the user's driving habits and improving the user's driving experience.

[0109] Based on the first embodiment of the present application, in the third embodiment of the present application, the same or similar contents as those in the above embodiment 1 can be referred to the above introduction and will not be described in detail later. Figure 3 , step S30 includes steps S301 to S306:

[0110] Step S301: Obtain the historical speed average value of the previous cycle, and calculate the current speed average value based on the historical speed average value.

[0111] It should be noted that after the vehicle begins accelerating, the average acceleration per second for the 10 seconds following the lane change is calculated. The average speed is then calculated by selecting data for the intervals Δ before and Δ after that time point. After obtaining the average, it is necessary to wait for the lane change to be completed. Only after the lane change is completed can the acceleration data for this lane change be updated. Therefore, the historical speed average of the previous cycle is obtained to calculate the current speed average using the following formula:

[0112] V_avg'=[V_avg*(count-1)+v] / count

[0113] In the above formula, V_avg' on the left side of the equation is the current average speed, V_avg on the right side of the equation is the historical average speed of the previous cycle, v is the current vehicle speed, and count is the number of cycles.

[0114] Step S302: determining a first vehicle speed and a second vehicle speed of the driver during the lane change process within a range corresponding to the speed average value from the lane change data based on the speed average value, wherein the first vehicle speed is less than the second vehicle speed.

[0115] It should be noted that the interval in which the average speed falls can be determined based on the average speed. For example, if the average speed is 106 kph, the interval is [100, 110]. In a specific implementation, the first speed and the second speed are critical values ​​of the interval. For example, if the average speed is 106 kph, the first speed is 100 and the second speed is 110.

[0116] Step S303: querying an acceleration calibration table according to the first vehicle speed and the second vehicle speed to obtain a first acceleration calibration value and a second acceleration calibration value at a preset time, wherein the acceleration calibration table stores a correspondence between vehicle speed, lane change acceleration time value and acceleration calibration value.

[0117] In a specific implementation, the acceleration calibration table can be queried according to the first vehicle speed and the second vehicle speed to obtain the first acceleration calibration value and the second acceleration calibration value at the preset time, as shown in Table 1. Table 1 is an acceleration calibration table, and the time unit is s. For example, if the lane change acceleration time count2 is 1s, the first acceleration calibration value at 100kph in 1s is a31, and the first acceleration calibration value at 110kph in 1s is a41.

[0118] Table 1

[0119] Speed / time 1 2 3 4 5 6 7 8 9 10 70 a1 a2 a3 a4 a5 a6 a7 a8 a9 a10 80 a11 a12 a13 a14 a15 a16 a17 a18 a19 a20 90 a21 a22 a23 a24 a25 a26 a27 a28 a29 a30 100 a31 a32 a33 a34 a35 a36 a37 a38 a39 a40 110 a41 a42 a43 a44 a45 a46 a47 a48 a49 a50

[0120] For example, if the current vehicle speed is 80 and the lane change acceleration time count2 is 3 seconds, the acceleration value is a13, and a13 is used to control the vehicle's acceleration. After the vehicle leaves the production line, the initial lane change acceleration is 0. After user use, the acceleration and acceleration timing are gradually adjusted to the driver's desired value through self-learning.

[0121] Step S304: obtaining the actual acceleration value of the average velocity.

[0122] It should be noted that the actual acceleration value of the average speed can also be obtained based on the lane change data. For example, if the average speed is 106, the actual acceleration value corresponding to when the vehicle accelerates to 106 in the lane change data can be obtained.

[0123] Step S305: Calculating a first acceleration deviation of the velocity average value by using the first acceleration calibration value, the second acceleration calibration value, and the actual acceleration value.

[0124] In a specific implementation, the first acceleration deviation a_error of the current average velocity can be calculated using the first acceleration calibration value, the second acceleration calibration value, and the actual acceleration value. Specifically, the theoretical acceleration calibration value corresponding to the average velocity value can be obtained by inversely interpolating the first acceleration calibration value and the second acceleration calibration value, as calculated by the following formula:

[0125] First acceleration deviation a_error=actual acceleration value-theoretical acceleration calibration value

[0126] For example, the first acceleration calibration value at 1s100kph is 0.1, the second acceleration calibration value at 1s110kph is 0.2, the actual acceleration value corresponding to the collected average speed is 0.3, and the theoretical acceleration calibration value corresponding to the current average speed is calculated by inverse proportional interpolation to be 0.16. Then the first acceleration deviation = 0.3-0.16 = 0.14.

[0127] Step S306: determining a target acceleration deviation based on the first acceleration deviation.

[0128] It can be understood that the acceleration deviations at different speeds during the acceleration process can be calculated by using the calculated first acceleration deviation to determine the target acceleration deviation.

[0129] In a feasible implementation, step S306 may include: calculating a second acceleration deviation based on the first acceleration deviation, the speed average value and the first vehicle speed; calculating a third acceleration deviation based on the first acceleration deviation, the speed average value and the second vehicle speed; and obtaining a target acceleration deviation based on the second acceleration deviation and the third acceleration deviation.

[0130] It should be understood that the acceleration deviation of the first vehicle speed, i.e., the second acceleration deviation, can be calculated based on the first acceleration deviation, the speed average and the first vehicle speed, and the acceleration deviation of the second vehicle speed, i.e., the third acceleration deviation, can be calculated based on the first acceleration deviation, the speed average and the second vehicle speed.

[0131] a_error_m=[1-(V_avg-m) / (nm)]*a_error

[0132] a_error_n=[(V_avg-m) / (nm)]*a_error

[0133] a_error_m is the acceleration deviation at point m on the coordinate axis, that is, the acceleration deviation of the first vehicle speed, and a_error_n is the acceleration deviation at point n on the coordinate axis, that is, the acceleration deviation of the second vehicle speed.

[0134] For example, the first acceleration calibration value at 1s100kph is 0.1, the second acceleration calibration value at 1s110kph is 0.2, the actual acceleration value corresponding to the collected speed average value is 0.3, and the first acceleration deviation is 0.14. Then the second acceleration deviation = [1-(106-100) / (110-100)]*0.14=0.056, and the third acceleration deviation = [(106-100) / (110-100)]*a_error=0.084.

[0135] In a specific implementation, the second acceleration deviation and the third acceleration deviation may be used as target acceleration deviations.

[0136] In a specific implementation, the acceleration deviation table can be calibrated in advance. After the acceleration deviation corresponding to the vehicle speed is calculated, the acceleration deviation is filled into the acceleration deviation table, as shown in Table 2 below. Table 2 is the acceleration deviation table.

[0137] Table 2

[0138] Speed / time 1 2 3 4 5 70 a_error1 a_error2 a_error3 a_error4 a_error5 80 a_error6 a_error7 a_error8 a_error9 a_error10 90 a_error11 a_error12 a_error13 a_error14 a_error15 100 a_error16 a_error17 ... ... ... 110 ... ... ... ... ...

[0139] The initial acceleration deviation table is empty. After the acceleration deviation of the corresponding speed is calculated, the acceleration deviation is filled in Table 2.

[0140] In a feasible implementation, after obtaining the target acceleration deviation, the corresponding acceleration may be recalculated according to the target acceleration deviation, thereby updating the acceleration calibration table. Therefore, step S40 may include steps A11 to A15:

[0141] Step A11: Obtain a second acceleration deviation and a third acceleration deviation according to the target acceleration deviation.

[0142] It should be noted that, since the acceleration deviation includes the second acceleration deviation and the third acceleration deviation, the second acceleration deviation and the third acceleration deviation can be obtained according to the target acceleration deviation.

[0143] Step A12: Obtain an update coefficient, a first acceleration calibration value, and a second acceleration calibration value.

[0144] In a specific implementation, the first acceleration calibration value and the second acceleration calibration value are acceleration values ​​corresponding to the first velocity and the second velocity, and the update coefficient is f.

[0145] Step A13: updating the first acceleration calibration value in the acceleration calibration table according to the second acceleration deviation, the update coefficient, and the first acceleration calibration value to obtain an updated first acceleration calibration value.

[0146] It is understandable that the first acceleration calibration value in the acceleration calibration table can be updated by the second acceleration deviation, the update coefficient, and the first acceleration calibration value, as calculated by the following formula:

[0147] a_m'=a_error_m*f+a_m

[0148] In the above formula, a_m' is the updated first acceleration calibration value, a_error_m is the second acceleration deviation, f is the update coefficient, and a_m is the first acceleration calibration value.

[0149] Step A14: updating the second acceleration calibration value in the acceleration calibration table according to the third acceleration deviation, the update coefficient, and the second acceleration calibration value to obtain an updated second acceleration calibration value.

[0150] It is understandable that the second acceleration calibration value in the acceleration calibration table can be updated by the third acceleration deviation, the update coefficient, and the second acceleration calibration value, as calculated by the following formula:

[0151] a_n'=a_error_n*f+a_n

[0152] In the above formula, a_n' is the updated second acceleration calibration value, a_error_n is the third acceleration deviation, f is the update coefficient, and a_n is the second acceleration calibration value.

[0153] For example, the second acceleration deviation is 0.056, the third acceleration deviation is 0.084, the update coefficient is 0.01, the first acceleration calibration value is 0.1, the second acceleration calibration value is 0.2, the first speed is 100, and the second speed is 110, then the first acceleration calibration value a_100 is updated to 0.056*0.01+0.1=0.10056, and the second acceleration calibration value a_110 is updated to 0.084*0.01+0.2=0.20084.

[0154] Step A15: obtaining an updated acceleration calibration table according to the updated first acceleration calibration value and the updated second acceleration calibration value.

[0155] In a specific implementation, an updated acceleration calibration table can be obtained according to the updated acceleration calibration value, so that when lane change acceleration is performed next time, the acceleration in the updated acceleration calibration table is used to perform lane change acceleration control.

[0156] This embodiment obtains the historical speed average of the previous cycle and calculates the current speed average based on the historical speed average; determines the first and second speeds of the driver during the lane change process within the interval corresponding to the speed average from the lane change data based on the speed average, where the first speed is less than the second speed; queries an acceleration calibration table based on the first and second speeds to obtain a first acceleration calibration value and a second acceleration calibration value at a preset time, wherein the acceleration calibration table stores the correspondence between vehicle speed, lane change acceleration time value, and acceleration calibration value; obtains the actual acceleration value of the speed average; calculates a first acceleration deviation of the speed average using the first acceleration calibration value, the second acceleration calibration value, and the actual acceleration value; and determines a target acceleration deviation based on the first acceleration deviation. By statistically analyzing the vehicle speed data in the lane change data, the acceleration deviation at different vehicle speeds during the acceleration process is calculated, facilitating subsequent correction of the acceleration based on the acceleration deviation to achieve the purpose of adapting to user habits.

[0157] Based on the first and third embodiments of the present application, in the fourth embodiment of the present application, the same or similar contents as those in the first and third embodiments can be referred to above and will not be described in detail. Figure 4 , after step S50, further comprising steps S51 to S53:

[0158] Step S51: using the updated waiting acceleration time value as the initial waiting acceleration time value and replacing the acceleration calibration table with the updated acceleration calibration table.

[0159] It should be noted that after the self-learning is completed, the updated waiting acceleration time value can be used as the initial waiting acceleration time value, and the updated acceleration calibration table can replace the acceleration calibration table.

[0160] Step S52: When the ACC function of the vehicle is activated and the vehicle meets the lane change acceleration activation conditions, the target acceleration calibration value is searched from the acceleration calibration table according to the current lane change acceleration time value and the current vehicle speed.

[0161] In specific implementations, after the acceleration calibration table and the waiting time acceleration value are updated, the vehicle can subsequently perform ACC lane change control. Specifically, the system determines whether the vehicle's ACC function is active and whether the vehicle meets the lane change acceleration activation conditions. Based on the current lane change acceleration time and current vehicle speed, the most appropriate acceleration calibration value is retrieved from the acceleration calibration table. After the waiting acceleration time, the vehicle begins accelerating according to the calibration table. At this point, timer count2 is set to 0 and begins counting. This time is defined as the lane change acceleration time.

[0162] The current lane change acceleration time can be directly detected. For example, if the current lane change acceleration time is 2s and the current vehicle speed is 90kph, the target acceleration calibration value is a22'.

[0163] In a feasible implementation, before step S52, it is necessary to determine whether the vehicle meets the lane change acceleration activation condition. Therefore, before step S52, the following steps are further included:

[0164] After the vehicle's ACC function is activated, obtain the current vehicle status, current speed, vehicle turn signal status, and lane change lane status;

[0165] When the current vehicle state is that the vehicle is in a following vehicle driving state, the vehicle turn signal state is that the left turn signal of the vehicle is on, the current vehicle speed is greater than a preset vehicle speed threshold, the deviation between the current vehicle speed and the set vehicle speed is greater than a preset deviation value, and the lane change lane state is that there is no preceding vehicle in the lane change lane within a preset range, the timing is started;

[0166] When the timing number is greater than the initial waiting acceleration time value, it is determined that the vehicle meets the lane change acceleration activation condition.

[0167] When the vehicle's ACC function is activated, it can obtain lane change acceleration calibration parameters matched to the driver's number from the DMS (Driver Monitoring System). Multiple sets of calibration data can be preset, for example three, with each set matching a specific driver. If the calibration data exceeds the set range, the ACC lane change acceleration function is disabled, meaning that ACC lane change control does not accelerate, but instead follows the ACC following distance. If a match is found, the system can then determine whether the conditions for lane change acceleration activation are met by obtaining the vehicle's current state, speed, turn signal status, and lane change lane status.

[0168] The current vehicle state indicates whether the vehicle is in a following vehicle state, and the lane change lane state indicates whether there is a preceding vehicle or a following vehicle in the lane change lane.

[0169] It can be understood that if the vehicle is in a following state and the left turn signal of the vehicle is on, it means that the driver has a need to change lanes to the left. The set speed can be set to 50kph or other values. This embodiment does not limit this. When the current vehicle speed is greater than the preset speed threshold and the deviation between the current vehicle speed and the set vehicle speed is greater than the preset deviation value and there is no vehicle in front of the lane changing within the preset range (if there is a rear radar, there must also be no vehicle behind the lane changing within the preset range), it means that the lane can be changed to the lane changing lane at this time, and the timing is started at this time.

[0170] For example, if the vehicle is following a vehicle, the current speed is greater than 70 kph, the left turn signal is on, the deviation between the current speed and the set speed is greater than 5 kph, and there is no vehicle ahead in the left lane within the preset range, the counter starts at count = 0 and increments by count = count + 1 each cycle. The preset range can be calibrated based on the specific vehicle speed. For example, if the preset range is set to 100 m, the higher the vehicle speed, the larger the preset range.

[0171] When it is detected that the counter is greater than count_accel, it is determined that the vehicle meets the lane change acceleration activation condition. At this time, the corresponding target acceleration calibration value can be queried according to the updated acceleration calibration table to control the vehicle to accelerate.

[0172] Step S53: performing lane change acceleration control on the vehicle using the target acceleration calibration value.

[0173] In practice, after determining the target acceleration calibration value, it can be output to control lane change acceleration. Specifically, when the counter count is greater than count_accel, count2 is set to 0, at which point ACC begins controlling vehicle acceleration. The counter increments by count2 = count2 + 1 each cycle. This target acceleration calibration value is then sent to the Integrated Brake Control (IBC), requesting the IBC to execute it. Depending on the current architecture and interface definitions, if the actuator can directly respond to the ACC's driving force, torque is calculated based on the target acceleration calibration value and sent to the actuator.

[0174] In a feasible implementation, it is also necessary to detect in real time whether the lane change is successful. Therefore, step S53 includes steps S531 to S534:

[0175] Step S531: Accelerate the vehicle during lane change using the target acceleration calibration value.

[0176] It should be noted that after determining the target acceleration calibration value, the target acceleration calibration value can be sent to the IBC first, requesting the IBC to execute the acceleration to accelerate the vehicle during lane change.

[0177] Step S532: During vehicle acceleration, obtain vehicle steering wheel angle and lane line data.

[0178] It should be noted that during vehicle acceleration, the vehicle steering wheel angle and lane line data operated by the driver can be detected in real time. The steering wheel angle can be detected by the angle sensor, and the lane line data including the distance between the vehicle and the lane change line can be obtained through the lane line equation.

[0179] Step S533: When it is detected within the preset steering wheel operation time that the distance between the vehicle and the lane change lane line in the lane line data is shortened and the vehicle steering wheel angle is greater than or equal to a preset angle value, the current vehicle position is obtained.

[0180] It should be noted that the preset steering wheel operation time, steertime, can be pre-calibrated, for example, to 2s or 3s. If no steering wheel movement is detected after accelerating for longer than the preset steering wheel operation time, the following distance between the ego vehicle and the preceding vehicle continues to decrease (or the overlap ratio between the ego vehicle and the preceding vehicle exceeds the preset overlap ratio), and the distance between the ego vehicle and the lane change lane remains unchanged, acceleration control is discontinued and following traffic is re-entered. The preset steering wheel operation time, steertime, needs to be calibrated based on the following gear and vehicle speed. Because different gears require different following distances, the value of steertime varies. A longer following distance allows for a longer steertime, while a higher speed requires longer braking time. Maintaining a safe braking distance from the preceding vehicle is crucial because the preceding vehicle may engage in sudden braking, such as emergency braking. In this case, even if the current count2 is less than steertime, if the following distance reaches the minimum allowed following distance for ACC under the current operating conditions, lane change acceleration should be discontinued and following distance control should be re-entered to further ensure following safety.

[0181] In practice, if the driver's steering is detected within a preset steering wheel operation time and the distance between the vehicle and the lane change lane decreases, indicating that the driver is gradually approaching the lane change lane, lane change control can be performed normally and the current vehicle position can be obtained in real time. This process requires maintaining a safe distance from the vehicle ahead based on the predicted trajectory of the vehicle and the preceding vehicle to ensure safe braking.

[0182] Step S534: When the current vehicle position is in the lane changing lane and within the target range of the lane changing lane, control the vehicle to complete lane changing acceleration.

[0183] In practice, if the vehicle is currently in the lane change lane and within the target range of the lane change lane, the lane change is complete, and ACC lane change control is exited and re-entered. The target range is when the vehicle is in the middle of the lane change lane, with a small difference between the left lane marking and the right lane marking.

[0184] This embodiment uses the updated acceleration waiting time value as the initial acceleration waiting time value and replaces the acceleration calibration table with the updated acceleration calibration table. When the vehicle's ACC function is activated and the vehicle meets the lane change acceleration activation conditions, a target acceleration calibration value is retrieved from the acceleration calibration table based on the current lane change acceleration time value and the current vehicle speed. Lane change acceleration control of the vehicle is performed using the target acceleration calibration value. The most appropriate target acceleration calibration value is determined using the updated acceleration calibration table, and lane change acceleration control of the vehicle is performed using the target acceleration calibration value, thereby achieving a more consistent and effective lane change control effect and meeting the driver's expectations.

[0185] It should be noted that the above examples are only used to understand the present application and do not constitute a limitation on the ACC lane change acceleration self-learning method of the present application. More simple transformations based on this technical concept are all within the scope of protection of the present application.

[0186] This application also provides an ACC lane change acceleration self-learning device, please refer to Figure 5 The ACC lane change acceleration self-learning device includes:

[0187] The acquisition module 10 is used to obtain the lane change data of the vehicle during the lane change process after the vehicle completes the lane change when the vehicle meets the ACC lane change acceleration self-learning activation conditions and detects that the vehicle changes lanes by the driver in the human driving mode.

[0188] The updating module 20 is configured to update the initial waiting acceleration time value based on the lane change data to obtain an updated waiting acceleration time value.

[0189] The determination module 30 is configured to determine a target acceleration deviation according to the lane change data.

[0190] The updating module 20 is further configured to update the acceleration calibration table according to the target acceleration deviation to obtain an updated acceleration calibration table.

[0191] The lane change acceleration self-learning module 40 is configured to complete the lane change acceleration self-learning by updating the waiting acceleration time value and the updating acceleration calibration table.

[0192] The ACC lane change acceleration self-learning device provided in this application utilizes the ACC lane change acceleration self-learning method described in the aforementioned embodiment, resolving the technical issue of lane change acceleration control failing to adapt to user driving habits. Compared to the prior art, the ACC lane change acceleration self-learning device provided in this application offers the same beneficial effects as the ACC lane change acceleration self-learning method described in the aforementioned embodiment. Other technical features of the ACC lane change acceleration self-learning device are the same as those disclosed in the aforementioned embodiment and are not further elaborated upon here.

[0193] In one embodiment, the update module 20 is further used to obtain the accelerator pedal time of the driver when changing lanes based on the lane change data; obtain an update coefficient and an initial waiting acceleration time value; and update the initial waiting acceleration time value by using the initial waiting acceleration time value, the accelerator pedal time and the update coefficient to obtain an updated waiting acceleration time value.

[0194] In one embodiment, the determination module 30 is further used to obtain a historical speed average value of a previous cycle and calculate a current speed average value based on the historical speed average value; determine a first vehicle speed and a second vehicle speed of the driver during the lane change process within a corresponding interval of the speed average value from the lane change data based on the speed average value, the first vehicle speed being less than the second vehicle speed; query an acceleration calibration table based on the first vehicle speed and the second vehicle speed to obtain a first acceleration calibration value and a second acceleration calibration value at a preset time, the acceleration calibration table storing a correspondence between vehicle speed, lane change acceleration time value and acceleration calibration value; obtain an actual acceleration value of the speed average value; calculate a first acceleration deviation of the speed average value through the first acceleration calibration value, the second acceleration calibration value and the actual acceleration value; and determine a target acceleration deviation based on the first acceleration deviation.

[0195] In one embodiment, the determination module 30 is further used to calculate a second acceleration deviation based on the first acceleration deviation, the speed average value and the first vehicle speed; calculate a third acceleration deviation based on the first acceleration deviation, the speed average value and the second vehicle speed; and obtain a target acceleration deviation based on the second acceleration deviation and the third acceleration deviation.

[0196] In one embodiment, the update module 20 is further used to obtain a second acceleration deviation and a third acceleration deviation based on the target acceleration deviation; obtain an update coefficient, a first acceleration calibration value and a second acceleration calibration value; update the first acceleration calibration value in the acceleration calibration table according to the second acceleration deviation, the update coefficient and the first acceleration calibration value to obtain an updated first acceleration calibration value; update the second acceleration calibration value in the acceleration calibration table according to the third acceleration deviation, the update coefficient and the second acceleration calibration value to obtain an updated second acceleration calibration value; and obtain an updated acceleration calibration table according to the updated first acceleration calibration value and the updated second acceleration calibration value.

[0197] In one embodiment, the acquisition module 10 is further used to obtain the current vehicle state, current vehicle speed, vehicle acceleration, vehicle turn signal state, and lane change lane state; when the current vehicle state is that the vehicle is in a stable following state, the current vehicle speed is greater than a preset vehicle speed threshold, the vehicle turn signal state is that the vehicle's left turn signal is on, and the lane change lane state is that there is no vehicle ahead in the lane change lane within a preset range, the timing is started and the current throttle opening is recorded; when the rate of change of the current throttle opening is greater than a preset rate of change value, the current throttle opening is greater than a preset throttle opening threshold, and the vehicle acceleration is greater than a preset value, it is determined that the vehicle meets the ACC lane change acceleration self-learning activation conditions.

[0198] In one embodiment, the device further includes a lane change control module, which is configured to use the updated waiting acceleration time value as the initial waiting acceleration time value and replace the acceleration calibration table through the updated acceleration calibration table; when the vehicle ACC function is activated and the vehicle meets the lane change acceleration activation conditions, query the target acceleration calibration value from the acceleration calibration table according to the current lane change acceleration time value and the current vehicle speed; and perform lane change acceleration control on the vehicle through the target acceleration calibration value.

[0199] In one embodiment, the lane change control module is further configured to obtain, after the ACC function of the vehicle is activated, a current vehicle state, a current vehicle speed, a vehicle turn signal state, and a lane change lane state; start timing when the current vehicle state is that the vehicle is in a stable following driving state, the vehicle turn signal state is that the left turn signal of the vehicle is on, the current vehicle speed is greater than a preset vehicle speed threshold, the deviation between the current vehicle speed and the set vehicle speed is greater than a preset deviation value, and the lane change lane state is that there is no preceding vehicle in the lane change lane within a preset range; and determine that the vehicle meets the lane change acceleration activation conditions when the timing number is greater than the initial waiting acceleration time value.

[0200] In one embodiment, the lane change control module is further used to accelerate the vehicle during lane change using the target acceleration calibration value; during vehicle acceleration, obtain the vehicle steering wheel angle and lane line data; obtain the current vehicle position when it is detected that the distance between the vehicle and the lane change lane line in the lane line data is shortened and the vehicle steering wheel angle is greater than or equal to a preset angle value within a preset steering wheel operation time; and control the vehicle to complete lane change acceleration when the current vehicle position is in the lane change lane and within the target range of the lane change lane.

[0201] The present application provides an ACC lane change acceleration self-learning device, which includes: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute the ACC lane change acceleration self-learning method in the above-mentioned embodiment one.

[0202] Reference below Figure 6 , which shows a schematic diagram of the structure of an ACC lane change acceleration self-learning device suitable for implementing an embodiment of the present application. The ACC lane change acceleration self-learning device in the embodiment of the present application may include, but is not limited to, mobile terminals such as mobile phones, laptop computers, digital broadcast receivers, PDAs (Personal Digital Assistants), PADs (Portable Application Descriptions), PMPs (Portable Media Players), in-vehicle terminals (e.g., in-vehicle navigation terminals), and fixed terminals such as digital TVs and desktop computers. Figure 6 The ACC lane change acceleration self-learning device shown is only an example and should not bring any limitation to the functions and scope of use of the embodiments of the present application.

[0203] like Figure 6As shown, the ACC lane change acceleration self-learning device may include a processing device 1001 (such as a central processing unit, a graphics processor, etc.), which can perform various appropriate actions and processes according to the program stored in the ROM (Read Only Memory) 1002 or the program loaded from the storage device 1003 to the RAM (Random Access Memory) 1004. Various programs and data required for the operation of the ACC lane change acceleration self-learning device are also stored in the RAM 1004. The processing device 1001, ROM 1002 and RAM 1004 are connected to each other via a bus 1005. The input / output (I / O) interface 1006 is also connected to the bus. Typically, the following systems can be connected to the I / O interface 1006: input devices 1007 including, for example, a touch screen, touchpad, keyboard, mouse, image sensor, microphone, accelerometer, gyroscope, etc.; output devices 1008 including, for example, an LCD (Liquid Crystal Display), speaker, vibrator, etc.; storage devices 1003 including, for example, a magnetic tape, hard disk, etc.; and communication devices 1009. The communication device 1009 can allow the ACC lane change acceleration self-learning device to communicate wirelessly or by wire with other devices to exchange data. Although the figure shows an ACC lane change acceleration self-learning device with various systems, it should be understood that it is not required to implement or have all of the systems shown. More or fewer systems may be implemented or have instead.

[0204] In particular, according to the embodiments disclosed in the present application, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, the embodiments disclosed in the present application include a computer program product comprising a computer program carried on a computer-readable medium, the computer program comprising program code for executing the method shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from a network via a communication device, or installed from a storage device 1003, or installed from a ROM 1002. When the computer program is executed by the processing device 1001, the above-mentioned functions defined in the method of the embodiment disclosed in the present application are executed.

[0205] The ACC lane change acceleration self-learning device provided in this application utilizes the ACC lane change acceleration self-learning method described in the aforementioned embodiment, resolving the technical issue of lane change acceleration control failing to adapt to user driving habits. Compared to the prior art, the ACC lane change acceleration self-learning device provided in this application achieves the same beneficial effects as the ACC lane change acceleration self-learning method described in the aforementioned embodiment. Other technical features of the ACC lane change acceleration self-learning device are the same as those disclosed in the aforementioned embodiment and are not further elaborated here.

[0206] It should be understood that the various parts disclosed in this application can be implemented using hardware, software, firmware, or a combination thereof. In the description of the above embodiments, specific features, structures, materials, or characteristics can be combined in any one or more embodiments or examples in a suitable manner.

[0207] The above description is merely a specific embodiment of the present application, but the scope of protection of the present application is not limited thereto. Any changes or substitutions that can be easily conceived by a person skilled in the art within the technical scope disclosed in this application should be included in the scope of protection of this application. Therefore, the scope of protection of this application should be based on the scope of protection of the claims.

[0208] The present application provides a computer-readable storage medium having computer-readable program instructions (i.e., a computer program) stored thereon, and the computer-readable program instructions are used to execute the ACC lane change acceleration self-learning method in the above-mentioned embodiment.

[0209] The computer-readable storage medium provided in this application may be, for example, a USB flash drive, but is not limited to electrical, magnetic, optical, electromagnetic, infrared, or semiconductor systems, systems or devices, or any combination thereof. More specific examples of computer-readable storage media may include, but are not limited to: an electrical connection with one or more wires, a portable computer disk, a hard disk, RAM (Random Access Memory), ROM (Read Only Memory), EPROM (Erasable Programmable Read Only Memory or Flash memory), optical fiber, CD-ROM (CD-Read Only Memory, portable compact disk read-only memory), optical storage device, magnetic storage device, or any suitable combination thereof. In this embodiment, the computer-readable storage medium may be any tangible medium that contains or stores a program that can be used by or in conjunction with an instruction execution system, system or device. The program code contained on the computer-readable storage medium may be transmitted using any appropriate medium, including but not limited to: wires, optical cables, RF (Radio Frequency), etc., or any suitable combination thereof.

[0210] The above-mentioned computer-readable storage medium may be included in the ACC lane change acceleration self-learning device; or it may exist independently without being assembled into the ACC lane change acceleration self-learning device.

[0211] The above-mentioned computer-readable storage medium carries one or more programs. When the above-mentioned one or more programs are executed by the ACC lane change acceleration self-learning device, the ACC lane change acceleration self-learning device: when the vehicle meets the ACC lane change acceleration self-learning activation conditions and detects that the vehicle changes lanes in the human driving mode, obtains the lane change data of the vehicle during the lane change process after the vehicle completes the lane change; updates the initial waiting acceleration time value based on the lane change data to obtain an updated waiting acceleration time value; determines the target acceleration deviation according to the lane change data; updates the acceleration calibration table according to the target acceleration deviation to obtain an updated acceleration calibration table; and completes the lane change acceleration self-learning through the updated waiting acceleration time value and the updated acceleration calibration table.

[0212] The computer program code for performing the operations of the present application can be written in one or more programming languages ​​or a combination thereof, including object-oriented programming languages ​​such as Java, Smalltalk, C++, and conventional procedural programming languages ​​such as "C" or similar programming languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as a separate software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving a remote computer, the remote computer can be connected to the user's computer through any type of network, including a LAN (Local Area Network) or a WAN (Wide Area Network), or can be connected to an external computer (e.g., using an Internet service provider to connect via the Internet).

[0213] The flow charts and block diagrams in the accompanying drawings illustrate the possible architecture, functions and operations of the systems, methods and computer program products according to various embodiments of the present application. In this regard, each box in the flow chart or block diagram can represent a module, program segment or a part of code, and the module, program segment or a part of code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the box can also occur in a different order than that marked in the accompanying drawings. For example, two boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the block diagram and / or flow chart, and the combination of the boxes in the block diagram and / or flow chart can be implemented by a dedicated hardware-based system that performs the specified function or operation, or can be implemented by a combination of dedicated hardware and computer instructions.

[0214] The modules described in the embodiments of the present application may be implemented in software or hardware, wherein the name of a module does not necessarily limit the unit itself.

[0215] The computer-readable storage medium provided in this application stores computer-readable program instructions (i.e., a computer program) for executing the aforementioned ACC lane change acceleration self-learning method. This computer-readable storage medium can address the technical issue of lane change acceleration control failing to adapt to user driving habits. Compared to the prior art, the beneficial effects of the computer-readable storage medium provided in this application are similar to those of the ACC lane change acceleration self-learning method provided in the aforementioned embodiment and are not further elaborated here.

[0216] The present application also provides a computer program product, including a computer program, which, when executed by a processor, implements the steps of the above-mentioned ACC lane change acceleration self-learning method.

[0217] The computer program product provided in this application can address the technical issue of lane change acceleration control failing to adapt to user driving habits. Compared to the prior art, the beneficial effects of the computer program product provided in this application are similar to those of the ACC lane change acceleration self-learning method provided in the aforementioned embodiment, and are not further elaborated here.

[0218] The above description is only part of the embodiments of the present application and does not limit the patent scope of the present application. All equivalent structural transformations made by using the contents of the present application specification and drawings under the technical concept of the present application, or direct / indirect application in other related technical fields are included in the patent protection scope of the present application.

Claims

1. An ACC lane change acceleration self-learning method, characterized in that: The ACC lane change acceleration self-learning method includes: When the vehicle meets the ACC lane change acceleration self-learning activation conditions and detects that the vehicle is changing lanes in human driving mode, the lane change data of the vehicle during the lane change process is obtained after the vehicle completes the lane change; updating the initial waiting acceleration time value based on the lane change data to obtain an updated waiting acceleration time value; determining a target acceleration deviation based on the lane change data; Updating the acceleration calibration table according to the target acceleration deviation to obtain an updated acceleration calibration table; The lane change acceleration self-learning is completed by updating the waiting acceleration time value and the updating acceleration calibration table.

2. The method according to claim 1, wherein The step of updating the initial waiting acceleration time value based on the lane change data to obtain an updated waiting acceleration time value comprises: obtaining, based on the lane change data, a driver's accelerator pedal pressing time when changing lanes; Get the update coefficient and initial waiting acceleration time value; The initial waiting acceleration time value is updated according to the initial waiting acceleration time value, the accelerator pedal time, and the update coefficient to obtain an updated waiting acceleration time value.

3. The method according to claim 1, wherein The step of determining the target acceleration deviation according to the lane change data includes: Obtain the historical speed average of the previous period, and calculate the current speed average based on the historical speed average; determining, from the lane change data based on the speed average value, a first vehicle speed and a second vehicle speed of the driver during the lane change process within a range corresponding to the speed average value, wherein the first vehicle speed is less than the second vehicle speed; querying an acceleration calibration table according to the first vehicle speed and the second vehicle speed to obtain a first acceleration calibration value and a second acceleration calibration value at a preset time, wherein the acceleration calibration table stores a correspondence between vehicle speed, lane change acceleration time value, and acceleration calibration value; Obtaining an actual acceleration value of the velocity average; Calculating a first acceleration deviation of the velocity average value by using the first acceleration calibration value, the second acceleration calibration value, and the actual acceleration value; A target acceleration deviation is determined based on the first acceleration deviation.

4. The method according to claim 3, wherein The step of determining a target acceleration deviation based on the first acceleration deviation comprises: calculating a second acceleration deviation according to the first acceleration deviation, the speed average, and the first vehicle speed; calculating a third acceleration deviation according to the first acceleration deviation, the speed average, and the second vehicle speed; A target acceleration deviation is obtained according to the second acceleration deviation and the third acceleration deviation.

5. The method according to claim 1, wherein The step of updating the acceleration calibration table according to the target acceleration deviation to obtain an updated acceleration calibration table includes: Obtaining a second acceleration deviation and a third acceleration deviation according to the target acceleration deviation; Obtaining an update coefficient, a first acceleration calibration value, and a second acceleration calibration value; updating the first acceleration calibration value in the acceleration calibration table according to the second acceleration deviation, the update coefficient, and the first acceleration calibration value to obtain an updated first acceleration calibration value; updating the second acceleration calibration value in the acceleration calibration table according to the third acceleration deviation, the update coefficient, and the second acceleration calibration value to obtain an updated second acceleration calibration value; An updated acceleration calibration table is obtained according to the updated first acceleration calibration value and the updated second acceleration calibration value.

6. The method according to claim 1, wherein Before the step of obtaining lane change data of the vehicle during the lane change process after the vehicle completes the lane change when the vehicle satisfies the ACC lane change acceleration self-learning activation conditions and detects that the vehicle changes lanes by the driver in the human driving mode, the method further includes: Get the current vehicle status, current speed, vehicle acceleration, vehicle turn signal status, and lane change status; When the current vehicle state is that the vehicle is in a stable following state, the current vehicle speed is greater than a preset vehicle speed threshold, the vehicle turn signal state is that the left turn signal is on, and the lane change lane state is that there is no preceding vehicle within a preset range, start timing and record the current throttle opening; When the change rate of the current throttle opening is greater than a preset change rate value, the current throttle opening is greater than a preset throttle opening threshold, and the vehicle acceleration is greater than a preset value, it is determined that the vehicle meets the ACC lane change acceleration self-learning activation condition.

7. The method according to claim 1, wherein After completing the lane change acceleration self-learning step by updating the waiting acceleration time value and updating the acceleration calibration table, the method further includes: Using the updated waiting acceleration time value as the initial waiting acceleration time value and replacing the acceleration calibration table with the updated acceleration calibration table; When the ACC function of the vehicle is activated and the vehicle meets the lane change acceleration activation conditions, querying the target acceleration calibration value from the acceleration calibration table according to the current lane change acceleration time value and the current vehicle speed; The vehicle is subjected to lane change acceleration control by using the target acceleration calibration value.

8. The method according to claim 7, wherein Before the step of obtaining the current waiting acceleration time value and the current vehicle speed when the ACC function of the vehicle is activated and the vehicle meets the lane change acceleration activation condition, the method further includes: After the vehicle's ACC function is activated, obtain the current vehicle status, current speed, vehicle turn signal status, and lane change status; When the current vehicle state is that the vehicle is in a following vehicle driving state, the vehicle turn signal state is that the left turn signal of the vehicle is on, the current vehicle speed is greater than a preset vehicle speed threshold, the deviation between the current vehicle speed and the set vehicle speed is greater than a preset deviation value, and the lane change lane state is that there is no preceding vehicle in the lane change lane within a preset range, the timing is started; When the timing number is greater than the initial waiting acceleration time value, it is determined that the vehicle meets the lane change acceleration activation condition.

9. The method according to claim 7, wherein The step of performing lane change acceleration control on the vehicle using the target acceleration calibration value includes: performing lane change acceleration on the vehicle using the target acceleration calibration value; During vehicle acceleration, obtain vehicle steering wheel angle and lane line data; When it is detected within a preset steering wheel operation time that the distance between the vehicle and the lane change lane in the lane line data is shortened and the vehicle steering wheel angle is greater than or equal to a preset angle value, obtaining the current vehicle position; When the current vehicle position is in the lane change lane and within the target range of the lane change lane, the vehicle is controlled to complete lane change acceleration.

10. An ACC lane change acceleration self-learning device, characterized in that: The device comprises: An acquisition module is used to acquire lane change data of the vehicle during the lane change process after the vehicle completes the lane change when the vehicle meets the ACC lane change acceleration self-learning activation conditions and detects that the vehicle changes lanes by the driver in the human driving mode; an updating module, configured to update the initial waiting acceleration time value based on the lane change data to obtain an updated waiting acceleration time value; a determination module, configured to determine a target acceleration deviation based on the lane change data; The updating module is further configured to update the acceleration calibration table according to the target acceleration deviation to obtain an updated acceleration calibration table; The lane change acceleration self-learning module is used to complete the lane change acceleration self-learning by updating the waiting acceleration time value and the updating acceleration calibration table.

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