A vehicle self-localization and self-learning method

Abstract

The present invention relates to the technical field of vehicle positioning, and in particular to a vehicle self-positioning self-learning method, the method comprising the following steps: the basis of the self-learning of the driving distance is based on the parameters stored in the EEPROM which are calibrated in advance, the driving distance Dis_Cal of the vehicle during calibration and the initial wheel driving distances InitPerWIC_FL, InitPerWIC_FR, InitPerWIC_RL, InitPerWIC_RR of the single pulse are set, and the driving distance Dis_FL, Dis_FR, Dis_RL, Dis_RR of each wheel are calculated according to the initial wheel driving distance of the single pulse and the total number of driving pulses of each wheel after the vehicle runs Dis_Cal, and then the single pulse driving distance PerWIC_FL, PerWIC_FR, PerWIC_RL, PerWIC_RR of each wheel are calibrated, and the calculation is performed through a calculation formula. In the present invention, the influence of insufficient tire pressure or replacement of a small spare tire on the calculation of the driving distance during the actual use of the vehicle can be self-corrected to ensure the accuracy of the vehicle self-positioning and the consistency of the parking performance.

Description

Technical Field

[0001] The present invention relates to the technical field of vehicle positioning, and in particular to a vehicle self-positioning and self-learning method. Background Art

[0002] Currently, more and more vehicles are equipped with automatic parking functions. The vehicle self-positioning module is the basis for realizing the parking function. Calculating the vehicle's travel distance based on wheel speed pulses is the most important component of the vehicle self-positioning module and has a great impact on parking performance.

[0003] When the tire pressure is insufficient or a small spare tire is replaced, the calculated driving distance under the same wheel speed pulse is larger than the actual driving distance, resulting in inaccurate calculation of the driving distance, affecting the vehicle's self-positioning function and reducing parking performance. Summary of the invention

[0004] The purpose of the present invention is to propose a vehicle self-positioning and self-learning method to solve the problem that the existing technology adopts a fixed parameter form. When the tire pressure is insufficient or the user replaces the small spare tire, the parameter cannot be adapted, affecting the vehicle's self-positioning performance and further affecting the parking success rate.

[0005] In order to achieve the above object, the present invention adopts the following technical solutions:

[0006] A vehicle self-positioning and self-learning method, the method comprising the following steps:

[0007] Step 1: The basis of driving distance self-learning is based on the parameters stored in EEPROM that have been calibrated in advance. The driving distance Dis_Cal of the vehicle during calibration and the initial single pulse wheel driving distance InitPerWIC_FL, InitPerWIC_FR, InitPerWIC_RL, InitPerWIC_RR are set. After the vehicle runs Dis_Cal, the driving distance Dis_FL, Dis_FR, Dis_RL, Dis_RR of each wheel is calculated according to the initial single pulse wheel driving distance and the total number of driving pulses of each wheel, and then the single pulse driving distance PerWIC_FL, PerWIC_FR, PerWIC_RL, PerWIC_RR of each wheel is calibrated and calculated through the calculation formula;

[0008] After three consecutive calibrations and taking the average value, PerWIC_FL, PerWIC_FR, PerWIC_RL, PerWIC_RR and Dis_Cal are written into EEPROM and the process goes to Step 2.

[0009] Step 2: The self-learning condition judgment requires that the vehicle is in a steady state and driving in a straight line, and judges whether the vehicle driving conditions meet the expected requirements;

[0010] If the vehicle driving conditions are met, go to Step 3, otherwise continue to judge the conditions in Step 2;

[0011] Step 3: Calculate the driving distance of the four wheels respectively. When the driving distance of a wheel reaches Dis_Cal±Thd, start to judge whether the driving distance of the remaining wheels is reasonable, and complete the self-learning of unreasonable driving distances. When no wheel driving distance reaches D_Cal±Thd, it is necessary to continue to judge whether the self-learning conditions are met. If not, enter Step 2, if satisfied, enter Step 4. Only one self-learning correction is performed for each ignition cycle, where thd is the allowable error range, generally taking the minimum distance allowed by a single pulse, such as 2cm;

[0012] Step 4: Write SF_PerWIC_FL, SF_PerWIC_FR, SF_PerWIC_RL, and SF_PerWIC_RR calculated in Step 3 into EEPROM.

[0013] As a further description of the above technical solution:

[0014] The calculation formula in step 1 is as follows:

[0015] PerWIC_FL=Dis_FL*InitPerWIC_FL / Dis_Cal;

[0016] PerWIC_FR=Dis_FR*InitPerWIC_FR / Dis_Cal;

[0017] PerWIC_RL=Dis_RL*InitPerWIC_RL / Dis_Cal;

[0018] PerWIC_RR=Dis_RR*InitPerWIC_RR / Dis_Cal.

[0019] As a further description of the above technical solution:

[0020] The judgment conditions in step 2 are as follows:

[0021] 1) The vehicle speed is lower than a threshold, such as 10 kph;

[0022] 2) The steering wheel angle is within the threshold range, such as 10°;

[0023] 3) The gear position is D;

[0024] 4) The vehicle has no faults, such as steering failure, brake system failure, etc.;

[0025] 5) No dynamic intervention, such as TCS, VDC, ABS, etc.

[0026] In summary, due to the adoption of the above technical solution, the beneficial effects of the present invention are:

[0027] In the present invention, the vehicle is used to perform self-learning correction on the driving distance under a single wheel speed pulse during actual use, thereby reducing the impact of insufficient tire pressure or replacement of a small spare tire on the vehicle's driving distance and ensuring the consistency of parking performance. BRIEF DESCRIPTION OF THE DRAWINGS

[0028] Figure 1 This is a schematic diagram of a driving distance self-learning method of a vehicle self-positioning self-learning method proposed by the present invention;

[0029] Figure 2 A driving distance self-learning flow chart of a vehicle self-positioning self-learning method of the present invention;

[0030] Figure 3 The present invention provides a self-learning correction flow chart of a vehicle self-positioning and self-learning method. DETAILED DESCRIPTION

[0031] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0032] See also Figure 1-3 The present invention provides a technical solution: the system architecture of the present invention is as follows Figure 1 As shown: The inputs to the system include:

[0033] VehSpd: Vehicle speed signal.

[0034] WIC_FR: Right front wheel speed pulse signal.

[0035] WIC_RL: Left rear wheel speed pulse signal.

[0036] WIC_RR: Right rear wheel speed pulse signal.

[0037] WIC_FL: Left front wheel speed pulse signal.

[0038] SteerAng: Steering wheel angle signal.

[0039] GearPos: gear position signal.

[0040] DynamicIntvt: Dynamic intervention signal, including active intervention functions of the chassis, such as TCS (traction control), VDC (body stability control), etc.

[0041] DigStatus: Diagnose system status.

[0042] PerWIC_FL / FR / RL / RR: Travel distance signal of a single wheel speed pulse read from EEPROM.

[0043] The output of the system is:

[0044] SF_WIC_FL / Valid: Single pulse driving distance and validity signal after self-learning of the left front wheel.

[0045] SF_WIC_FR / Valid: Single pulse driving distance and validity signal after right front wheel self-learning.

[0046] SF_WIC_RL / Valid: Single pulse driving distance and validity signal after left rear wheel self-learning.

[0047] SF_WIC_RR / Valid: Single pulse driving distance and validity signal after right rear wheel self-learning.

[0048] Dis_Cal: Actual vehicle distance used during initial calibration.

[0049] Based on the above system architecture, the specific implementation of the present invention is described as follows:

[0050] The applicable condition of the present invention is that at least one tire pressure is normal. Based on this condition, the key point of the present invention is to judge that the travel distance of one or more wheels is unreasonable based on the travel distance of the four wheels and make corrections. The specific control process is as follows: Figure 2 As shown;

[0051] Specifically, refer to Figure 2 and Figure 3 A vehicle self-positioning and self-learning method, the method comprising the following steps:

[0052] Step 1: The basis of driving distance self-learning is based on the parameters stored in EEPROM that have been calibrated in advance. The vehicle's driving distance Dis_Cal (such as 10m) and the initial single pulse wheel driving distance InitPerWIC_FL, InitPerWIC_FR, InitPerWIC_RL, InitPerWIC_RR are set during calibration. After the vehicle runs Dis_Cal, the driving distance Dis_FL, Dis_FR, Dis_RL, Dis_RR of each wheel is calculated according to the initial single pulse wheel driving distance and the total number of driving pulses of each wheel, and then the single pulse driving distance PerWIC_FL, PerWIC_FR, PerWIC_RL, PerWIC_RR of each wheel is calibrated. The calculation formula is as follows:

[0053] PerWIC_FL=Dis_FL*InitPerWIC_FL / Dis_Cal;

[0054] PerWIC_FR=Dis_FR*InitPerWIC_FR / Dis_Cal;

[0055] PerWIC_RL=Dis_RL*InitPerWIC_RL / Dis_Cal;

[0056] PerWIC_RR=Dis_RR*InitPerWIC_RR / Dis_Cal;

[0057] After three consecutive calibrations and taking the average value, PerWIC_FL, PerWIC_FR, PerWIC_RL, PerWIC_RR and Dis_Cal are written into EEPROM and the process goes to Step 2.

[0058] Step 2: The judgment of self-learning conditions requires that the vehicle is in a steady state and driving in a straight line. The judgment conditions are as follows:

[0059] 1) The vehicle speed is lower than a threshold, such as 10 kph;

[0060] 2) The steering wheel angle is within the threshold range, such as 10°;

[0061] 3) The gear position is D;

[0062] 4) The vehicle has no faults, such as steering failure, brake system failure, etc.;

[0063] 5) No dynamic intervention, such as TCS (traction control), VDC (vehicle stability control), ABS (anti-lock braking system), etc.;

[0064] If all the above conditions are met, go to Step 3, otherwise continue to judge the conditions in Step 2;

[0065] Step 3: Calculate the driving distance of the four wheels respectively. When the driving distance of a wheel reaches Dis_Cal±Thd, start to judge whether the driving distance of the remaining wheels is reasonable, and complete the self-learning of unreasonable driving distances. When no wheel driving distance reaches D_Cal±Thd, it is necessary to continue to judge whether the self-learning conditions are met. If not, enter Step 2, if satisfied, enter Step 4. Only one self-learning correction is performed for each ignition cycle, where thd is the allowable error range, generally taking the minimum distance allowed by a single pulse, such as 2cm;

[0066] The following is an example of the self-learning process when the tire pressure of the left rear wheel and the right front wheel is insufficient and the distance traveled by the left front wheel reaches Dis_Cal±Thd. Figure 3 As shown:

[0067] Among them, Dis_FL is the distance traveled by the left front wheel, Dis_FR is the distance traveled by the right front wheel, Dis_RL is the distance traveled by the left rear wheel, and Dis_RR is the distance traveled by the right rear wheel;

[0068] Step 4: Write SF_PerWIC_FL, SF_PerWIC_FR, SF_PerWIC_RL, and SF_PerWIC_RR calculated in Step 3 into EEPROM.

[0069] The above description is only a preferred specific implementation manner of the present invention, but the protection scope of the present invention is not limited thereto. Any technician familiar with the technical field can make equivalent replacements or changes according to the technical scheme and inventive concept of the present invention within the technical scope disclosed by the present invention, which should be covered by the protection scope of the present invention.

Claims

1. A vehicle self-positioning and self-learning method, characterized in that: The method comprises the following steps: Step 1: The basis of driving distance self-learning is based on the parameters stored in EEPROM that have been calibrated in advance. The driving distance Dis_Cal of the vehicle during calibration and the initial single pulse wheel driving distance InitPerWIC_FL, InitPerWIC_FR, InitPerWIC_RL, InitPerWIC_RR are set. After the vehicle runs Dis_Cal, the driving distance Dis_FL, Dis_FR, Dis_RL, Dis_RR of each wheel is calculated according to the initial single pulse wheel driving distance and the total number of driving pulses of each wheel, and then the single pulse driving distance PerWIC_FL, PerWIC_FR, PerWIC_RL, PerWIC_RR of each wheel is calibrated and calculated through the calculation formula; After three consecutive calibrations and taking the average value, PerWIC_FL, PerWIC_FR, PerWIC_RL, PerWIC_RR and Dis_Cal are written into EEPROM and enter Step 2; Step 2: The self-learning condition judgment requires that the vehicle is in a steady state and driving in a straight line, and judges whether the vehicle driving conditions meet the expected requirements; If the vehicle driving conditions are met, go to Step 3, otherwise continue to judge the conditions in Step 2; Step 3: Calculate the driving distance of the four wheels respectively. When the driving distance of a wheel reaches Dis_Cal±Thd, start to judge whether the driving distance of the remaining wheels is reasonable, and complete the self-learning of unreasonable driving distances. When no wheel driving distance reaches D_Cal±Thd, it is necessary to continue to judge whether the self-learning conditions are met. If not, enter Step 2, if satisfied, enter Step 4. Only one self-learning correction is performed for each ignition cycle, where thd is the allowable error range, and the minimum distance allowed for a single pulse is 2cm; Step 4: Write SF_PerWIC_FL, SF_PerWIC_FR, SF_PerWIC_RL, and SF_PerWIC_RR calculated in Step 3 into EEPROM; The calculation formula in Step 1 is as follows: PerWIC_FL = Dis_FL * InitPerWIC_FL / Dis_Cal; PerWIC_FR = Dis_FR * InitPerWIC_FR / Dis_Cal; PerWIC_RL = Dis_RL * InitPerWIC_RL / Dis_Cal; PerWIC_RR = Dis_RR * InitPerWIC_RR / Dis_Cal.

2. A vehicle self-positioning and self-learning method according to claim 1, characterized in that: The judgment conditions in Step 2 are as follows: 1) The vehicle speed is lower than the threshold of 10kph; 2) The threshold of the steering wheel angle is 10°; 3) The gear position is D; 4) The vehicle has no steering fault or braking system fault; 5) No TCS, VDC and ABS dynamic intervention.

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