Longitudinal acceleration optimization method, device and equipment for autonomous vehicle, and computer program product

Abstract

The invention discloses a longitudinal acceleration optimization method, device and equipment of an automatic driving vehicle and a computer program product, the method comprises the steps that acceleration information, speed information and a pitch angle of the automatic driving vehicle are acquired, the acceleration information comprises X-axis acceleration, Y-axis acceleration and Z-axis acceleration, and the X-axis acceleration is longitudinal acceleration; calculating a longitudinal acceleration zero offset value according to the Y-axis acceleration, the Z-axis acceleration and the pitch angle; calculating a longitudinal acceleration compensation value by using a low-pass filtering algorithm according to the longitudinal acceleration, the longitudinal acceleration zero offset value and the speed information; and determining the longitudinal acceleration after final compensation according to the longitudinal acceleration compensation value and the longitudinal acceleration zero offset value. According to the method, the acceleration, the speed and the pitch angle information of the automatic driving vehicle are combined, the low-pass filtering algorithm is used for online compensation calculation of the longitudinal acceleration, the calculation precision of the longitudinal acceleration is improved, and the overall performance and safety of the automatic driving vehicle are improved.

Description

Technical Field

[0001] The present application relates to the field of autonomous driving technology, and in particular to a method, device and equipment, and a computer program product for optimizing the longitudinal acceleration of an autonomous driving vehicle. Background Art

[0002] Autonomous driving technology, a cutting-edge advancement in both the automotive industry and artificial intelligence, has made significant progress in recent years. The PNC (Planning and Control) algorithm is a key technology in autonomous driving systems. It plans the vehicle's trajectory and controls it to ensure safe and comfortable driving. The PNC algorithm relies on a variety of sensor data, including the vehicle's longitudinal acceleration.

[0003] Longitudinal acceleration information is crucial for autonomous driving systems because it reflects changes in vehicle speed during motion and provides a key input parameter for the PNC algorithm. However, in practical applications, existing methods for obtaining longitudinal acceleration information have significant problems, especially when driving on sloped roads.

[0004] Traditional longitudinal acceleration sensors typically measure longitudinal acceleration based on vehicle dynamic models or kinematic principles. However, on sloped roads, the measured longitudinal acceleration is affected by the component of gravity along the slope, resulting in significant errors between the measured and actual values. This error not only affects the autonomous driving system's ability to accurately assess the vehicle's motion state but also further impairs the planning and control effectiveness of the PNC algorithm, thereby reducing the safety and comfort of autonomous driving.

[0005] To overcome this problem, some studies have proposed methods that use information fusion from multiple sensors, such as GPS systems, wheel speed sensors, and slope sensors, to estimate the vehicle's longitudinal velocity and, in turn, derive longitudinal acceleration. However, while this method improves the accuracy of longitudinal acceleration information to a certain extent, it is still affected by various factors, such as GPS signal stability and wheel speed sensor signal noise, resulting in a certain degree of error in the resulting longitudinal acceleration information. Summary of the Invention

[0006] The embodiments of the present application provide a method, apparatus, and device, as well as a computer program product, for optimizing the longitudinal acceleration of an autonomous vehicle to improve the accuracy and stability of the longitudinal acceleration calculation of the autonomous vehicle.

[0007] The embodiments of this application adopt the following technical solutions:

[0008] In a first aspect, an embodiment of the present application provides a method for optimizing the longitudinal acceleration of an autonomous vehicle. The method for optimizing the longitudinal acceleration of the autonomous vehicle includes:

[0009] Obtain the acceleration information, speed information, and pitch angle of the autonomous vehicle. The acceleration information includes the X-axis acceleration, Y-axis acceleration, and Z-axis acceleration, and the X-axis acceleration is the longitudinal acceleration.

[0010] Calculate the zero bias value of the longitudinal acceleration according to the Y-axis acceleration, the Z-axis acceleration, and the pitch angle.

[0011] Calculate the longitudinal acceleration compensation value by using a low-pass filter algorithm according to the longitudinal acceleration, the zero bias value of the longitudinal acceleration, and the speed information.

[0012] Determine the finally compensated longitudinal acceleration according to the longitudinal acceleration compensation value and the zero bias value of the longitudinal acceleration.

[0013] Optionally, the speed information includes the longitudinal speed output by the integrated positioning. The step of calculating the longitudinal acceleration compensation value by using a low-pass filter algorithm according to the longitudinal acceleration, the zero bias value of the longitudinal acceleration, and the speed information includes:

[0014] When the acceleration information and the speed information are not the first-frame data, determine the first longitudinal acceleration compensation value of the current frame according to the longitudinal speed output by the integrated positioning of the current frame, the longitudinal speed output by the integrated positioning of the previous frame, and the first longitudinal acceleration compensation value of the previous frame.

[0015] When the acceleration information and the speed information are not the first-frame data, determine the second longitudinal acceleration compensation value of the current frame according to the longitudinal acceleration of the current frame, the zero bias value of the longitudinal acceleration of the current frame, and the second longitudinal acceleration compensation value of the previous frame.

[0016] Calculate the integrated longitudinal acceleration compensation value of the current frame according to the first longitudinal acceleration compensation value and the second longitudinal acceleration compensation value of the current frame.

[0017] Optionally, the step of determining the first longitudinal acceleration compensation value of the current frame according to the longitudinal speed output by the integrated positioning of the current frame, the longitudinal speed output by the integrated positioning of the previous frame, and the first longitudinal acceleration compensation value of the previous frame includes:

[0018] Calculate the initial first longitudinal acceleration compensation value of the current frame according to the difference between the longitudinal speed output by the integrated positioning of the current frame and the longitudinal speed output by the integrated positioning of the previous frame, and the time interval between two adjacent frames.

[0019] The initial first longitudinal acceleration compensation value of the current frame and the first longitudinal acceleration compensation value of the previous frame are weightedly fused to obtain a fused first longitudinal acceleration compensation value of the current frame.

[0020] Optionally, determining the second longitudinal acceleration compensation value of the current frame according to the longitudinal acceleration of the current frame, the longitudinal acceleration zero bias value of the current frame, and the second longitudinal acceleration compensation value of the previous frame includes:

[0021] Calculating an initial second longitudinal acceleration compensation value of the current frame according to the longitudinal acceleration of the current frame and the longitudinal acceleration zero bias value of the current frame;

[0022] The initial second longitudinal acceleration compensation value of the current frame and the second longitudinal acceleration compensation value of the previous frame are weightedly fused to obtain a fused second longitudinal acceleration compensation value of the current frame.

[0023] Optionally, calculating the fused longitudinal acceleration compensation value of the current frame according to the first longitudinal acceleration compensation value of the current frame and the second longitudinal acceleration compensation value of the current frame includes:

[0024] Calculating a deviation between a first longitudinal acceleration compensation value of the current frame and a second longitudinal acceleration compensation value of the current frame;

[0025] If the deviation value is greater than a preset deviation threshold, weighted fusion is performed on the first longitudinal acceleration compensation value of the current frame and the second longitudinal acceleration compensation value of the current frame to obtain a fused longitudinal acceleration compensation value of the current frame;

[0026] Otherwise, the second longitudinal acceleration compensation value of the current frame is used as the fused longitudinal acceleration compensation value of the current frame.

[0027] Optionally, the calculating the longitudinal acceleration compensation value by using a low-pass filtering algorithm according to the longitudinal acceleration, the longitudinal acceleration zero bias value, and the speed information includes:

[0028] In a case where the acceleration information and the speed information are first frame data, the longitudinal acceleration of the current frame and the longitudinal acceleration zero bias value of the current frame are summed to serve as the longitudinal acceleration compensation value of the current frame.

[0029] Optionally, the method for optimizing the longitudinal acceleration of the autonomous driving vehicle further includes:

[0030] Determine whether the autonomous vehicle is currently in a parked state;

[0031] When the vehicle is in a parking state, a longitudinal acceleration zero bias value in a parking state is calculated using an optimization algorithm for the longitudinal acceleration zero bias value in the parking state.

[0032] In a second aspect, an embodiment of the present application further provides a longitudinal acceleration optimization device for an autonomous driving vehicle. The longitudinal acceleration optimization device for the autonomous driving vehicle includes:

[0033] An acquisition unit, configured to acquire acceleration information, speed information, and pitch angle of the autonomous driving vehicle. The acceleration information includes X-axis acceleration, Y-axis acceleration, and Z-axis acceleration, and the X-axis acceleration is the longitudinal acceleration;

[0034] A first calculation unit, configured to calculate a longitudinal acceleration zero-offset value according to the Y-axis acceleration, the Z-axis acceleration, and the pitch angle;

[0035] A second calculation unit, configured to calculate a longitudinal acceleration compensation value according to the longitudinal acceleration, the longitudinal acceleration zero-offset value, and the speed information by using a low-pass filtering algorithm;

[0036] A compensation unit, configured to determine a finally compensated longitudinal acceleration according to the longitudinal acceleration compensation value and the longitudinal acceleration zero-offset value.

[0037] In a third aspect, an embodiment of the present application further provides a device, including:

[0038] A processor; and a memory arranged to store computer-executable instructions, where the executable instructions, when executed, cause the processor to execute any one of the foregoing longitudinal acceleration optimization methods for the autonomous driving vehicle.

[0039] In a fourth aspect, an embodiment of the present application further provides a computer program product, including a computer program / instructions, where the computer program / instructions, when executed by a processor, implement any one of the foregoing longitudinal acceleration optimization methods for the autonomous driving vehicle.

[0040] The above at least one technical solution adopted in the embodiments of the present application can achieve the following beneficial effects: In the longitudinal acceleration optimization method of the autonomous driving vehicle in the embodiments of the present application, first, the acceleration information, speed information, and pitch angle of the autonomous driving vehicle are obtained. The acceleration information includes the X-axis acceleration, Y-axis acceleration, and Z-axis acceleration, and the X-axis acceleration is the longitudinal acceleration. Then, the longitudinal acceleration zero offset value is calculated according to the Y-axis acceleration, the Z-axis acceleration, and the pitch angle. After that, according to the longitudinal acceleration, the longitudinal acceleration zero offset value, and the speed information, the longitudinal acceleration compensation value is calculated by using a low-pass filter algorithm. Finally, the finally compensated longitudinal acceleration is determined according to the longitudinal acceleration compensation value and the longitudinal acceleration zero offset value. The longitudinal acceleration optimization method of the autonomous driving vehicle in the embodiments of the present application combines the acceleration information, speed information, and pitch angle information of the autonomous driving vehicle, and uses a low-pass filter algorithm to perform online compensation calculation of the longitudinal acceleration, improving the calculation accuracy of the longitudinal acceleration of the autonomous driving vehicle, providing more reliable data support for the control of the autonomous driving vehicle, and enhancing the overall performance and safety of the autonomous driving vehicle. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] The drawings described herein are used to provide a further understanding of the present application, and constitute a part of the present application. The illustrative embodiments and descriptions thereof of the present application are used to explain the present application, and do not constitute an improper limitation of the present application. In the drawings:

[0042] Figure 1 It is a schematic flow chart of a longitudinal acceleration optimization method for an autonomous driving vehicle in an embodiment of the present application;

[0043] Figure 2 It is a schematic structural diagram of a longitudinal acceleration optimization device for an autonomous driving vehicle in an embodiment of the present application;

[0044] Figure 3 It is a schematic structural diagram of a device in an embodiment of the present application. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0045] To make the objectives, technical solutions, and advantages of the present application clearer, the technical solutions of the present application will be clearly and completely described below in conjunction with the specific embodiments of the present application and the corresponding drawings. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present application.

[0046] The technical solutions provided in each embodiment of the present application are described in detail below with reference to the drawings.

[0047] The embodiments of the present application provide a method for optimizing the longitudinal acceleration of an autonomous vehicle. As Figure 1 shown, a flowchart of a method for optimizing the longitudinal acceleration of an autonomous vehicle in the embodiments of the present application is provided. The method for optimizing the longitudinal acceleration of the autonomous vehicle at least includes the following steps S110 to S140:

[0048] Step S110: Obtain the acceleration information, speed information, and pitch angle of the autonomous vehicle. The acceleration information includes the X-axis acceleration, Y-axis acceleration, and Z-axis acceleration, and the X-axis acceleration is the longitudinal acceleration.

[0049] When dynamically compensating the longitudinal acceleration of an autonomous vehicle, it is necessary to first obtain the original acceleration information output by an inertial measurement unit (IMU) or other acceleration sensors installed on the vehicle. These sensors can provide the acceleration data of the vehicle in three-dimensional space, that is, the X-axis acceleration (longitudinal acceleration, the acceleration in the same direction as the vehicle head direction), the Y-axis acceleration (the acceleration in the same plane as the X-axis and perpendicular to the X-axis pointing to the left), and the Z-axis acceleration (perpendicular to the XY plane and pointing upward). The object to be optimized online in the embodiments of the present application is the X-axis acceleration (longitudinal acceleration).

[0050] In addition, it is also necessary to obtain the speed information of the vehicle through the vehicle's wheel speed sensor, radar, or GPS, etc., and obtain the pitch angle information of the vehicle through the vehicle's attitude sensor or by calculating through an algorithm combining the IMU and other sensor data. The pitch angle reflects the inclination degree of the vehicle in the vertical plane.

[0051] Step S120: Calculate the zero bias value of the longitudinal acceleration according to the Y-axis acceleration, the Z-axis acceleration, and the pitch angle.

[0052] Utilize the relationship between the vehicle dynamics model and the sensor data, analyze the influence of the Y-axis acceleration and the Z-axis acceleration on the longitudinal acceleration measurement through an algorithm, and combine the pitch angle information to calculate the zero bias value of the longitudinal acceleration. The zero bias value is the acceleration measurement deviation caused by the sensor's own error or the vehicle's attitude change. For example, the zero bias value of the longitudinal acceleration can be calculated in the following manner:

[0053] tmp_acc_bias = tan(ori_pitch) * sqrt(accy * accy + accz * accz)

[0054] where tmp_acc_bias is the zero bias value of the longitudinal acceleration, ori_pitch is the pitch angle, accy is the Y-axis acceleration, and accz is the Z-axis acceleration.

[0055] Calculating the zero bias value of the longitudinal acceleration provides reliable data support for the online compensation of the subsequent longitudinal acceleration.

[0056] Step S130: According to the longitudinal acceleration, the zero bias value of the longitudinal acceleration, and the speed information, use a low-pass filter algorithm to calculate the longitudinal acceleration compensation value.

[0057] After calculating the zero bias value of the longitudinal acceleration, based on the designed low-pass filter algorithm, the original longitudinal acceleration can be compensated using the zero bias value of the longitudinal acceleration to filter out the influence of noise. Considering the zero bias change of the accelerometer, unstable situations may occur, which may lead to inaccurate calculated zero bias values. In the embodiments of the present application, it is necessary to further calculate the longitudinal acceleration compensation value in combination with the speed information on this basis, so as to improve the accuracy of calculating the longitudinal acceleration compensation value in various scenarios.

[0058] Step S140: Determine the finally compensated longitudinal acceleration according to the longitudinal acceleration compensation value and the zero bias value of the longitudinal acceleration.

[0059] After calculating the longitudinal acceleration compensation value, add the longitudinal acceleration compensation value to the zero bias value of the longitudinal acceleration to obtain the finally compensated longitudinal acceleration value, and output the finally compensated longitudinal acceleration value to the vehicle control system or other modules that require this information.

[0060] The longitudinal acceleration optimization method for an autonomous vehicle in the embodiments of the present application combines the acceleration information, speed information, and pitch angle information of the autonomous vehicle, and uses a low-pass filter algorithm to perform online compensation calculation of the longitudinal acceleration, improving the calculation accuracy of the longitudinal acceleration of the autonomous vehicle, providing more reliable data support for the control of the autonomous vehicle, and enhancing the overall performance and safety of the autonomous vehicle.

[0061] In some embodiments of the present application, the speed information includes the longitudinal speed output by the integrated positioning. The calculating the longitudinal acceleration compensation value according to the longitudinal acceleration, the zero bias value of the longitudinal acceleration, and the speed information using a low-pass filter algorithm includes: when the acceleration information and the speed information are not the first-frame data, determining the first longitudinal acceleration compensation value of the current frame according to the longitudinal speed output by the integrated positioning of the current frame, the longitudinal speed output by the integrated positioning of the previous frame, and the first longitudinal acceleration compensation value of the previous frame; when the acceleration information and the speed information are not the first-frame data, determining the second longitudinal acceleration compensation value of the current frame according to the longitudinal acceleration of the current frame, the zero bias value of the longitudinal acceleration of the current frame, and the second longitudinal acceleration compensation value of the previous frame; calculating the integrated longitudinal acceleration compensation value of the current frame according to the first longitudinal acceleration compensation value and the second longitudinal acceleration compensation value of the current frame.

[0062] The speed information in the embodiments of this application can be divided into the longitudinal speed of the vehicle chassis and the longitudinal speed output by the integrated positioning. The longitudinal speed of the vehicle chassis can be obtained through the vehicle's own sensors (such as wheel speed sensors, vehicle speed sensors, etc.), and the longitudinal speed output by the integrated positioning can be the longitudinal speed obtained by integrating multiple positioning technologies such as GPS and inertial navigation.

[0063] When calculating the longitudinal acceleration compensation value, it can be divided into two cases. One is that the currently obtained data is the first-frame data, and the other is that the currently obtained data is not the first-frame data. It is necessary to make a judgment first and adopt different calculation logics.

[0064] If the currently obtained acceleration information and speed information are not the first-frame data, the calculation of the longitudinal acceleration compensation value can be further carried out from two aspects. On the one hand, based on the longitudinal speed of the vehicle chassis in the current frame, the longitudinal speed output by the integrated positioning, and the first longitudinal acceleration compensation value of the previous frame, the low-pass filtering parameters set by the low-pass filtering algorithm are used for smoothing processing to obtain the first longitudinal acceleration compensation value of the current frame. This step is mainly to calculate the longitudinal acceleration compensation value based on the change of the speed information.

[0065] On the other hand, based on the longitudinal acceleration in the current frame, the zero bias value of the longitudinal acceleration in the current frame, and the second longitudinal acceleration compensation value of the previous frame, the low-pass filtering parameters set by the low-pass filtering algorithm are also used for smoothing processing to obtain the second longitudinal acceleration compensation value of the current frame. This step is mainly to calculate the acceleration compensation value based on the zero bias value of the longitudinal acceleration information.

[0066] When the accelerometer is stable, the second longitudinal acceleration compensation value calculated based on the zero bias value of the longitudinal acceleration is also accurate. However, when the accelerometer fluctuates, resulting in the possible inaccuracy of the calculated zero bias value of the longitudinal acceleration, the second longitudinal acceleration compensation value calculated based on this zero bias value of the longitudinal acceleration may also be inaccurate. Therefore, the above first aspect of calculating the longitudinal acceleration compensation value based on the change of the speed information is mainly to compensate for the problem that the calculated zero bias value of the longitudinal acceleration is inaccurate due to the instability of the accelerometer. By integrating the longitudinal acceleration compensation values of the above two aspects to calculate the final longitudinal acceleration compensation value, the accuracy and stability of the longitudinal acceleration compensation value are improved.

[0067] In the embodiments of the present application, by fusing the longitudinal speed of the vehicle chassis, the longitudinal speed output by the fused positioning, and the data of the acceleration sensor, and performing smoothing processing using a low-pass filtering algorithm, noise and errors can be significantly reduced, and the accuracy of calculating the longitudinal acceleration compensation value is improved. Since the method of multi-source data fusion is adopted, when an abnormal or large-error data source appears, other data sources can supplement and correct it, thereby enhancing the robustness and reliability of the system. The above solution can process each frame of data in real time, and perform iterative calculations based on the data of the current frame and the compensation value of the previous frame to ensure the real-time update and accuracy of the longitudinal acceleration compensation value.

[0068] In some embodiments of the present application, determining the first longitudinal acceleration compensation value of the current frame according to the longitudinal speed output by the fused positioning of the current frame, the longitudinal speed output by the fused positioning of the previous frame, and the first longitudinal acceleration compensation value of the previous frame includes: calculating the initial first longitudinal acceleration compensation value of the current frame according to the difference between the longitudinal speed output by the fused positioning of the current frame and the longitudinal speed output by the fused positioning of the previous frame, and the time interval between two adjacent frames; performing weighted fusion on the initial first longitudinal acceleration compensation value of the current frame and the first longitudinal acceleration compensation value of the previous frame to obtain the fused first longitudinal acceleration compensation value of the current frame.

[0069] When calculating the fused first longitudinal acceleration compensation value of the current frame, according to the definition of acceleration (acceleration = change in speed / time interval), using the longitudinal speed output by the fused positioning of the current frame and the longitudinal speed output by the fused positioning of the previous frame, and the time interval between two adjacent frames, calculate the initial first longitudinal acceleration compensation value of the current frame. This value reflects the acceleration compensation requirement due to the speed difference.

[0070] Based on the principle of the low-pass filtering algorithm, perform weighted fusion on the initial first longitudinal acceleration compensation value of the current frame and the first longitudinal acceleration compensation value of the previous frame to obtain the fused first longitudinal acceleration compensation value of the current frame, which can be expressed in the following form, for example:

[0071] acc_processed(i) = q * (longs_F(i) - longs_F(i - 1)) / dt_vel + (1 - q) * acc_processed(i - 1);

[0072] Wherein, acc_processed(i) and acc_processed(i - 1) are the fused first longitudinal acceleration compensation values of the current frame and the previous frame respectively, longs_F(i) and longs_F(i - 1) are the speeds output by the fused positioning of the current frame and the previous frame respectively, and q is the filtering coefficient, which can be adjusted according to the actual application scenario to achieve the best smoothing effect.

[0073] By calculating the initial acceleration compensation value based on the variation difference of the fusion positioning speed and the time interval, it provides strong verification information for the longitudinal acceleration compensation value calculated based on the zero bias value, thereby improving the accuracy and stability of the calculation of the acceleration compensation value.

[0074] In some embodiments of the present application, determining the second longitudinal acceleration compensation value of the current frame according to the longitudinal acceleration of the current frame, the longitudinal acceleration zero bias value of the current frame, and the second longitudinal acceleration compensation value of the previous frame includes: calculating the initial second longitudinal acceleration compensation value of the current frame according to the longitudinal acceleration of the current frame and the longitudinal acceleration zero bias value of the current frame; performing weighted fusion on the initial second longitudinal acceleration compensation value of the current frame and the second longitudinal acceleration compensation value of the previous frame to obtain the fused second longitudinal acceleration compensation value of the current frame.

[0075] When calculating the fused second longitudinal acceleration compensation value of the current frame, it is necessary to further obtain the fused second longitudinal acceleration compensation value of the previous frame, which is used for the acceleration compensation calculation of the current frame.

[0076] According to the longitudinal acceleration of the current frame and the longitudinal acceleration zero bias value of the current frame, calculate the initial second longitudinal acceleration compensation value of the current frame, aiming to remove the zero bias component in the acceleration signal and obtain an acceleration compensation value closer to the true value. Then perform weighted fusion on the initial second longitudinal acceleration compensation value of the current frame and the second longitudinal acceleration compensation value of the previous frame to obtain the fused second longitudinal acceleration compensation value of the current frame.

[0077] The implementation of the above process can be expressed in the following form, for example:

[0078] acc_raw_processed(i) = q * (accx0 + tmp_acc_bias) + (1 - q) * acc_raw_processed(i - 1);

[0079] Where, acc_raw_processed(i) and acc_raw_processed(i - 1) are the fused second longitudinal acceleration compensation values of the current frame and the previous frame respectively, accx0 is the initial longitudinal acceleration of the current frame, tmp_acc_bias is the longitudinal acceleration zero bias value of the current frame, and q is the filtering coefficient, which can be adjusted according to the actual application scenario to achieve the best smoothing effect.

[0080] By removing the zero bias component in the acceleration signal through the longitudinal acceleration zero bias value and performing smoothing filtering processing in combination with the acceleration compensation value of the previous frame, more accurate and stable longitudinal acceleration compensation data can be obtained.

[0081] In some embodiments of the present application, calculating the fused longitudinal acceleration compensation value of the current frame based on the first longitudinal acceleration compensation value of the current frame and the second longitudinal acceleration compensation value of the current frame includes: calculating a deviation value between the first longitudinal acceleration compensation value of the current frame and the second longitudinal acceleration compensation value of the current frame; if the deviation value is greater than a preset deviation threshold, performing weighted fusion on the first longitudinal acceleration compensation value of the current frame and the second longitudinal acceleration compensation value of the current frame to obtain the fused longitudinal acceleration compensation value of the current frame; otherwise, using the second longitudinal acceleration compensation value of the current frame as the fused longitudinal acceleration compensation value of the current frame.

[0082] Based on the first longitudinal acceleration compensation value and the second longitudinal acceleration compensation value of the current frame calculated according to the foregoing embodiments, the two longitudinal acceleration compensation values are calculated by different algorithms or data sources respectively, and are used to reflect the longitudinal acceleration compensation situation of the vehicle. Calculate the deviation value between the first longitudinal acceleration compensation value and the second longitudinal acceleration compensation value of the current frame, and then compare the deviation value with the set preset deviation threshold to determine whether the difference between the two acceleration compensation values is significant.

[0083] If the deviation value is greater than the preset deviation threshold, it indicates that there is a large difference between the two acceleration compensation values, indicating that the current second longitudinal acceleration compensation value may be affected by the acceleration zero bias change. At this time, weighted fusion can be performed on the first longitudinal acceleration compensation value and the second longitudinal acceleration compensation value of the current frame to obtain a more reliable fused longitudinal acceleration compensation value. The specific formula for weighted fusion can be expressed as:

[0084] comp_fused_current = w1 * acc_processed(i) + w2 * acc_raw_processed(i);

[0085] where comp_fused_current is the fused longitudinal acceleration compensation value of the current frame, acc_processed(i) is the fused first longitudinal acceleration compensation value of the current frame, acc_raw_processed(i) is the fused second longitudinal acceleration compensation value of the current frame, w1 and w2 are weight coefficients, and w1 + w2 = 1. The selection of the weight coefficients can be adjusted according to the actual situation to balance the influence of the two acceleration compensation values on the fusion result.

[0086] If the deviation value is not greater than the preset deviation threshold, it indicates that the difference between the two acceleration compensation values is small, and it can be considered that the second longitudinal acceleration compensation value calculated based on the bias value is accurate enough. At this time, in order to simplify the calculation and reduce the complexity of data processing, the second longitudinal acceleration compensation value of the current frame can be directly used as the fused longitudinal acceleration compensation value of the current frame.

[0087] By comparing the differences between the two acceleration compensation values and performing weighted fusion when the differences are significant, the advantages of different data sources or algorithms can be fully utilized to improve the accuracy of the fused longitudinal acceleration compensation value. Directly using one of the acceleration compensation values when the deviation value is small can reduce the complexity of data processing and the consumption of system resources. At the same time, when an abnormality occurs in a certain data source or algorithm, the system can still rely on the data provided by another data source or algorithm to work properly, thereby enhancing the robustness of the calculation result of the longitudinal acceleration compensation value.

[0088] In some embodiments of the present application, calculating the longitudinal acceleration compensation value by using the low-pass filter algorithm according to the longitudinal acceleration, the longitudinal acceleration bias value, and the speed information includes: when the acceleration information and the speed information are the first-frame data, summing the longitudinal acceleration of the current frame and the longitudinal acceleration bias value of the current frame as the longitudinal acceleration compensation value of the current frame.

[0089] If the currently acquired acceleration information and speed information are the first-frame data, since there is no historical data at this time and it is not applicable to the low-pass filter algorithm, the longitudinal acceleration of the current frame can be directly compensated by using the calculated longitudinal acceleration bias value of the current frame to obtain the longitudinal acceleration compensation value of the current frame.

[0090] To correspond to the fused first longitudinal acceleration compensation value acc_processed and the fused second longitudinal acceleration compensation value acc_raw_processed calculated in the above embodiments for the first-frame data, acc_processed and acc_raw_processed can also be calculated respectively here. For example, it can be expressed in the following form:

[0091] acc_processed = accx0 + tmp_acc_bias;

[0092] acc_raw_processed = accx0 + tmp_acc_bias.

[0093] In some embodiments of the present application, the longitudinal acceleration optimization method for the autonomous vehicle further includes: determining whether the autonomous vehicle is currently in a parked state; in the case of being in a parked state, calculating the zero bias value of the longitudinal acceleration in the parked state by using the zero bias value optimization algorithm for the longitudinal acceleration in the parked state.

[0094] When the autonomous vehicle is in a parked state, there will be a problem that the zero bias value of the acceleration is estimated inaccurately. To solve this problem, the embodiments of the present application further design an identification of the parked state and an optimization strategy for the longitudinal acceleration in the parked state.

[0095] For the identification of the parked state, it can be judged by obtaining the chassis speed of the vehicle. If the chassis speed of the vehicle is less than a certain speed threshold, it can be considered that the vehicle is in a stationary state. Of course, those skilled in the art can also identify whether the vehicle is in a parked state by other means, which is not specifically limited herein.

[0096] If it is identified that the vehicle is currently in a parked state, enter the optimization process of the zero bias value of the longitudinal acceleration in the parked state. Specifically, the fused second longitudinal acceleration compensation value calculated in the foregoing embodiments can be obtained first, and then a specific optimization algorithm is used to calculate a new zero bias value (acc_bias) of the longitudinal acceleration. The core of the algorithm is to compare the current fused second longitudinal acceleration compensation value with the theoretically longitudinal zero acceleration value, and perform weighted fusion of this difference with the previous zero bias value. Specifically, it can be implemented in the following manner:

[0097] if(fabs(vehicle_vel)< speed threshold){

[0098] acc_bias = α*(0 - acc_raw_processed)+acc_bias*(1 - α);

[0099] }

[0100] else

[0101] {

[0102] acc_bias = 0;

[0103] }

[0104] Wherein, α is the fusion weight, which can be adjusted according to the actual situation to achieve fine-tuning of the zero bias value of the longitudinal acceleration in the parked state.

[0105] The above processing strategy helps to improve the accuracy of the zero bias value estimation of the longitudinal acceleration in the parked state, thereby providing more accurate data support for the subsequent start of the vehicle.

[0106] The embodiment of the present application further provides a longitudinal acceleration optimization device 200 for an autonomous vehicle, as follows Figure 2 As shown, it provides a schematic structural diagram of a longitudinal acceleration optimization device for an autonomous vehicle in an embodiment of the present application. The longitudinal acceleration optimization device 200 of the autonomous vehicle includes: an acquisition unit 210, a first calculation unit 220, a second calculation unit 230, and a compensation unit 240, where:

[0107] The acquisition unit 210 is configured to acquire the acceleration information, speed information, and pitch angle of the autonomous vehicle. The acceleration information includes the X-axis acceleration, Y-axis acceleration, and Z-axis acceleration, and the X-axis acceleration is the longitudinal acceleration;

[0108] The first calculation unit 220 is configured to calculate the longitudinal acceleration zero bias value according to the Y-axis acceleration, the Z-axis acceleration, and the pitch angle;

[0109] The second calculation unit 230 is configured to calculate the longitudinal acceleration compensation value according to the longitudinal acceleration, the longitudinal acceleration zero bias value, and the speed information by using a low-pass filter algorithm;

[0110] The compensation unit 240 is configured to determine the finally compensated longitudinal acceleration according to the longitudinal acceleration compensation value and the longitudinal acceleration zero bias value.

[0111] In some embodiments of the present application, the speed information includes the longitudinal speed output by the integrated positioning. The second calculation unit is specifically configured to: when the acceleration information and the speed information are not the first-frame data, determine the first longitudinal acceleration compensation value of the current frame according to the longitudinal speed output by the integrated positioning of the current frame, the longitudinal speed output by the integrated positioning of the previous frame, and the first longitudinal acceleration compensation value of the previous frame; when the acceleration information and the speed information are not the first-frame data, determine the second longitudinal acceleration compensation value of the current frame according to the longitudinal acceleration of the current frame, the longitudinal acceleration zero bias value of the current frame, and the second longitudinal acceleration compensation value of the previous frame; calculate the integrated longitudinal acceleration compensation value of the current frame according to the first longitudinal acceleration compensation value of the current frame and the second longitudinal acceleration compensation value of the current frame.

[0112] In some embodiments of the present application, the second calculation unit is specifically configured to: calculate the initial first longitudinal acceleration compensation value of the current frame according to the difference between the longitudinal speed output by the integrated positioning of the current frame and the longitudinal speed output by the integrated positioning of the previous frame and the time interval between two adjacent frames; perform weighted fusion on the initial first longitudinal acceleration compensation value of the current frame and the first longitudinal acceleration compensation value of the previous frame to obtain the integrated first longitudinal acceleration compensation value of the current frame.

[0113] In some embodiments of the present application, the second calculation unit is specifically configured to: calculate an initial second longitudinal acceleration compensation value of the current frame according to the longitudinal acceleration of the current frame and the longitudinal acceleration zero bias value of the current frame; perform weighted fusion on the initial second longitudinal acceleration compensation value of the current frame and the second longitudinal acceleration compensation value of the previous frame to obtain a fused second longitudinal acceleration compensation value of the current frame.

[0114] In some embodiments of the present application, the second calculation unit is specifically configured to: calculate a deviation value between the first longitudinal acceleration compensation value and the second longitudinal acceleration compensation value of the current frame; if the deviation value is greater than a preset deviation threshold, perform weighted fusion on the first longitudinal acceleration compensation value and the second longitudinal acceleration compensation value of the current frame to obtain a fused longitudinal acceleration compensation value of the current frame; otherwise, use the second longitudinal acceleration compensation value of the current frame as the fused longitudinal acceleration compensation value of the current frame.

[0115] In some embodiments of the present application, the second calculation unit is specifically configured to: when the acceleration information and the speed information are the first-frame data, sum the longitudinal acceleration of the current frame and the longitudinal acceleration zero bias value of the current frame as the longitudinal acceleration compensation value of the current frame.

[0116] In some embodiments of the present application, the longitudinal acceleration optimization device of the autonomous vehicle further includes: a determination unit, configured to determine whether the autonomous vehicle is currently in a parking state; and in the case of being in the parking state, calculate the longitudinal acceleration zero bias value in the parking state by using the longitudinal acceleration zero bias value optimization algorithm in the parking state.

[0117] It can be understood that the above longitudinal acceleration optimization device of the autonomous vehicle can implement each step of the longitudinal acceleration optimization method of the autonomous vehicle provided in the foregoing embodiments. The relevant explanations regarding the longitudinal acceleration optimization method of the autonomous vehicle are applicable to the longitudinal acceleration optimization device of the autonomous vehicle, and will not be elaborated herein.

[0118] Figure 3 is a schematic structural diagram of a device in an embodiment of the present application. As Figure 3 shown, the device includes one or more processors (or processing units), and may further include one or more memories coupled to the processor, and may further include a communication module coupled to the processor.

[0119] The communication module can be used to communicate with other devices or apparatuses, such as sending or receiving data and / or signals. The communication module can have at least one communication module for communication. The communication module can include any interfaces necessary for communicating with other devices. Exemplarily, the communication module can be a transceiver, a circuit, a bus, a module, or other types of communication modules.

[0120] The processor can include but is not limited to at least one of the following: a general-purpose computer, a special-purpose computer, a microcontroller, a Digital Signal Processor (DSP), or one or more in a multi-core controller architecture based on a controller. The device can have multiple processors, such as an application-specific integrated circuit chip, which is subordinate to a clock synchronized with the main processor in time.

[0121] The memory can include one or more non-volatile memories and one or more volatile memories. Examples of non-volatile memories include but are not limited to at least one of the following: Read-Only-Memory (ROM), Electrically Programmable Read-Only-Memory (EPROM), flash memory, a hard disk, a Compact Disc (CD), a Digital Video Disk (DVD), or other magnetic storage and / or optical storage. Examples of volatile memories include but are not limited to at least one of the following: Random Access Memory (RAM), or other volatile memories that do not persist during a power-off duration.

[0122] The computer program includes computer-executable instructions executed by an associated processor. The program can be stored in the ROM. The processor can execute any appropriate actions and processes by loading the program into the RAM.

[0123] Possible implementations of the present application can be realized by means of a program, such that the communication device can execute any process discussed in the foregoing embodiments. Possible implementations of the present application can also be realized by hardware or by a combination of software and hardware.

[0124] In some embodiments, the program can be tangibly embodied in a computer-readable storage medium, which can be included in the device (such as in the memory) or other storage devices accessible by the device. The program can be loaded from the computer-readable storage medium into the RAM for execution. The computer-readable storage medium can include any type of tangible non-volatile memory, such as ROM, EPROM, flash memory, a hard disk, a CD, a DVD, etc.

[0125] The embodiments of the present application also provide a computer-readable storage medium, on which computer instructions or program codes are stored. When the processor runs the instructions or the program codes, the processor is caused to execute the methods and functions involved in any of the above embodiments. The computer-readable medium can be any tangible medium that contains or stores a program for or related to an instruction execution system, apparatus, or device. The computer-readable medium can be a computer-readable signal medium or a computer-readable storage medium. The computer-readable medium can include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatuses, or devices, or any suitable combination thereof. The computer-readable storage medium can be any available medium that the computer can access or a data storage device such as a server or a data center that includes one or more integrated available media. More specific examples of the computer-readable storage medium include electrical connections with one or more wires, magnetic media (such as disks, floppy disks, hard disks, magnetic tapes, magnetic storage devices), optical media (such as optical storage devices, DVDs), semiconductor media (such as solid-state drives), random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), or any suitable combination thereof, etc.

[0126] In the above embodiments, it can be implemented in whole or in part by software, hardware, firmware, or any combination thereof. When implemented using software, it can be implemented in whole or in part in the form of a computer program product. The embodiments of the present application also provide at least one computer program product tangibly stored on a non-transitory computer-readable storage medium. The computer program product includes one or more computer-executable instructions, such as instructions included in program modules, which are executed in a device on a target real or virtual processor to execute the processes, methods, and functions involved in any of the above embodiments. When the computer program instructions are loaded and executed on a computer, the processes or functions according to the embodiments of the present application are generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable devices. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, the computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center by wire (such as coaxial cable, optical fiber, digital subscriber line) or wirelessly (such as infrared, wireless, microwave, etc.).

[0127] The embodiments of the present application also propose a computer program product, including a computer program or instructions. When the computer program or instructions run on a computer, the computer is caused to execute the processes, methods, and functions in the above embodiments. Generally, program modules include routines, programs, libraries, objects, classes, components, data structures, etc. that execute specific tasks or implement specific abstract data types. In various embodiments, the functions of program modules can be combined or divided as needed. Machine-executable instructions for program modules can be executed within local or distributed devices. In a distributed device, program modules can be located in local and remote storage media.

[0128] Generally, the various embodiments of the present application can be implemented in hardware or special-purpose circuits, software, logic, or any combination thereof. Some aspects can be implemented in hardware, while other aspects can be implemented in firmware or software, which can be executed by a controller, a microprocessor, or other computing devices. Although the various aspects of the embodiments of the present disclosure are shown and described as block diagrams, flowcharts, or using some other graphical representation, it should be understood that the blocks, devices, systems, techniques, or methods described herein can be implemented as, by way of non-limiting example, hardware, software, firmware, special-purpose circuits or logic, general-purpose hardware or controllers or other computing devices, or some combination thereof.

[0129] It should be noted that although the embodiments of the present application are described above in conjunction with the accompanying drawings respectively, the above embodiments are not independent of each other, and they can also be combined to obtain other embodiments. The manners, situations, categories, and the division of the embodiments in the embodiments of the present application are only for the convenience of description and should not constitute a special limitation. The features in various manners, categories, situations, and embodiments can be combined with each other under logical conditions. The various embodiments of the present application can be combined arbitrarily to achieve different technical effects. The embodiments of the present application no longer list various combinations.

[0130] In addition, although the operations of the method of the present disclosure are described in a specific order in the drawings, this does not require or imply that these operations must be performed in that specific order, or that all of the illustrated operations must be performed to achieve the desired result. On the contrary, the steps depicted in the flowchart can be changed in the order of execution. Additionally or alternatively, some steps can be omitted, multiple steps can be combined into one step for execution, and / or one step can be decomposed into multiple steps for execution. It should also be noted that the features and functions of two or more devices according to the present disclosure can be embodied in one device. Conversely, the features and functions of one device described above can be further divided and embodied by multiple devices.

[0131] It should also be noted that the term "comprising", "including" or any other variant thereof is intended to cover non-exclusive inclusion, such that a process, method, commodity or device comprising a series of elements not only includes those elements but also includes other elements not expressly listed, or further includes elements inherent to such process, method, commodity or device. Without further limitation, an element defined by the statement "comprising one..." does not exclude the existence of additional identical elements in the process, method, commodity or device comprising said element.

[0132] The above description is only for the embodiments of the present application and is not intended to limit the present application. For those skilled in the art, various changes and modifications can be made to the present application. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application shall be included within the scope of the claims of the present application.

Claims

1. A longitudinal acceleration optimization method for an autonomous vehicle, characterized in that The longitudinal acceleration optimization method for the autonomous vehicle includes: Obtaining the acceleration information, speed information, and pitch angle of the autonomous vehicle, where the acceleration information includes the X-axis acceleration, Y-axis acceleration, and Z-axis acceleration, and the X-axis acceleration is the longitudinal acceleration; Calculating the longitudinal acceleration zero bias value based on the Y-axis acceleration, Z-axis acceleration, and pitch angle; Calculating the longitudinal acceleration compensation value using a low-pass filter algorithm based on the longitudinal acceleration, longitudinal acceleration zero bias value, and speed information; Determining the finally compensated longitudinal acceleration based on the longitudinal acceleration compensation value and the longitudinal acceleration zero bias value.

2. The longitudinal acceleration optimization method for an autonomous vehicle according to claim 1, characterized in that, The speed information includes the longitudinal speed output by the integrated positioning. The calculating the longitudinal acceleration compensation value using a low-pass filter algorithm based on the longitudinal acceleration, longitudinal acceleration zero bias value, and speed information includes: When the acceleration information and the speed information are not the first-frame data, determining the first longitudinal acceleration compensation value of the current frame based on the longitudinal speed output by the integrated positioning of the current frame, the longitudinal speed output by the integrated positioning of the previous frame, and the first longitudinal acceleration compensation value of the previous frame; When the acceleration information and the speed information are not the first-frame data, determining the second longitudinal acceleration compensation value of the current frame based on the longitudinal acceleration of the current frame, the longitudinal acceleration zero bias value of the current frame, and the second longitudinal acceleration compensation value of the previous frame; Calculating the integrated longitudinal acceleration compensation value of the current frame based on the first longitudinal acceleration compensation value and the second longitudinal acceleration compensation value of the current frame.

3. The longitudinal acceleration optimization method for an autonomous vehicle according to claim 2, wherein The determining the first longitudinal acceleration compensation value of the current frame based on the longitudinal speed output by the integrated positioning of the current frame, the longitudinal speed output by the integrated positioning of the previous frame, and the first longitudinal acceleration compensation value of the previous frame includes: Calculating the initial first longitudinal acceleration compensation value of the current frame based on the difference between the longitudinal speed output by the integrated positioning of the current frame and the longitudinal speed output by the integrated positioning of the previous frame, and the time interval between adjacent frames; Performing weighted fusion on the initial first longitudinal acceleration compensation value of the current frame and the first longitudinal acceleration compensation value of the previous frame to obtain the integrated first longitudinal acceleration compensation value of the current frame.

4. The longitudinal acceleration optimization method for an autonomous vehicle according to claim 2, characterized in that, The determining the second longitudinal acceleration compensation value of the current frame based on the longitudinal acceleration of the current frame, the longitudinal acceleration zero bias value of the current frame, and the second longitudinal acceleration compensation value of the previous frame includes: Calculating the initial second longitudinal acceleration compensation value of the current frame based on the longitudinal acceleration of the current frame and the longitudinal acceleration zero bias value of the current frame; Performing weighted fusion on the initial second longitudinal acceleration compensation value of the current frame and the second longitudinal acceleration compensation value of the previous frame to obtain the integrated second longitudinal acceleration compensation value of the current frame.

5. The longitudinal acceleration optimization method for an autonomous vehicle according to claim 2, wherein The calculating the integrated longitudinal acceleration compensation value of the current frame based on the first longitudinal acceleration compensation value and the second longitudinal acceleration compensation value of the current frame includes: Calculating the deviation value between the first longitudinal acceleration compensation value and the second longitudinal acceleration compensation value of the current frame; If the deviation value is greater than a preset deviation threshold, perform weighted fusion on the first longitudinal acceleration compensation value of the current frame and the second longitudinal acceleration compensation value of the current frame to obtain the fused longitudinal acceleration compensation value of the current frame; Otherwise, use the second longitudinal acceleration compensation value of the current frame as the fused longitudinal acceleration compensation value of the current frame.

6. The longitudinal acceleration optimization method for an autonomous vehicle according to claim 1, characterized in that, The calculating the longitudinal acceleration compensation value by using a low-pass filter algorithm according to the longitudinal acceleration, the longitudinal acceleration zero bias value, and the speed information includes: In the case where the acceleration information and the speed information are the first-frame data, sum the longitudinal acceleration of the current frame and the longitudinal acceleration zero bias value of the current frame as the longitudinal acceleration compensation value of the current frame.

7. The longitudinal acceleration optimization method for an autonomous vehicle according to claim 1, wherein The longitudinal acceleration optimization method of the autonomous vehicle further includes: Determine whether the autonomous vehicle is currently in a parked state; In the case of being in a parked state, calculate the longitudinal acceleration zero bias value in the parked state by using the longitudinal acceleration zero bias value optimization algorithm in the parked state.

8. An apparatus for optimizing the longitudinal acceleration of an autonomous vehicle, characterized in that, The longitudinal acceleration optimization device of the autonomous vehicle includes: An acquisition unit, configured to acquire the acceleration information, speed information, and pitch angle of the autonomous vehicle, where the acceleration information includes the X-axis acceleration, Y-axis acceleration, and Z-axis acceleration, and the X-axis acceleration is the longitudinal acceleration; A first calculation unit, configured to calculate the longitudinal acceleration zero bias value according to the Y-axis acceleration, the Z-axis acceleration, and the pitch angle; A second calculation unit, configured to calculate the longitudinal acceleration compensation value by using a low-pass filter algorithm according to the longitudinal acceleration, the longitudinal acceleration zero bias value, and the speed information; A compensation unit, configured to determine the finally compensated longitudinal acceleration according to the longitudinal acceleration compensation value and the longitudinal acceleration zero bias value.

9. A device, comprising: A processor; And a memory arranged to store computer-executable instructions, which when executed cause the processor to execute the longitudinal acceleration optimization method of the autonomous vehicle according to any one of claims 1 to 7.

10. A computer program product, comprising a computer program / instructions, characterized in that, When the computer program / instructions are executed by the processor, the longitudinal acceleration optimization method of the autonomous vehicle according to any one of claims 1 to 7 is implemented.

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