A high performance scalable electric side step control method and apparatus

Abstract

The present application relates to the technical field of side step of vehicle, and particularly relates to a high-performance telescopic electric side step control method and device. In a preset time period after the opening of the vehicle door, ranging data of each measuring point on the side step connecting plate at each monitoring time is obtained, all suspected obstacle influence time periods and obstacle influence parameters of each measuring point are obtained, all obstacle influence time periods are screened out, and then the time interval between adjacent obstacle influence time periods at each measuring point is combined to analyze and obtain the obstacle confidence coefficient at each measuring point, so as to control the telescoping of the electric side step. The present application can monitor the abnormal continuous situation of the ranging data at each measuring point, comprehensively evaluate the possibility of the electric side step being affected by fixed obstacles such as road edges, reduce the influence of other random interference factors, improve the evaluation accuracy of the influence of the telescoping environment obstacles on the electric side step, and further improve the control effect of the electric side step.

Description

Technical Field

[0001] This invention relates to the field of vehicle side pedal technology, and specifically to a high-performance retractable electric side pedal control method and device. Background Technology

[0002] Retractable electric side steps are electric auxiliary devices installed on the side of a car. When the door is opened, the side step automatically extends, providing a step; when the door is closed, the side step automatically retracts, maintaining a neat and aesthetically pleasing appearance. The extension and retraction of the side step is controlled by a telescopic rod, which in turn controls the movement of a connecting plate. The connecting plate moves along a fixed groove, thereby moving the side step. During the actual extension and retraction of the side step, obstacles may exist on the roadside. For example, when parking close to the curb, due to vehicle chassis height limitations or parking clearance limitations, the electric side step may bump against the curb during extension and retraction.

[0003] Currently, ultrasonic or radar sensors can be installed on the connecting plate to detect the environment and obstacles around the vehicle in real time, indicating whether the side pedal should stop or adjust its direction to avoid collisions. However, sensors usually detect obstacles by reflecting signals. If there are flying insects, rain, fallen leaves or other noise interference around the sensor, it may cause inaccurate signal reflection or misjudgment, which will make it impossible to accurately determine the existence of obstacles and thus affect the control of the electric side pedal. Summary of the Invention

[0004] To address the technical problem of poor control performance of retractable electric side pedals, the present invention aims to provide a high-performance retractable electric side pedal control method and device, the specific technical solution of which is as follows:

[0005] A high-performance retractable electric side pedal control method, the method comprising:

[0006] Within a preset time period after the car door is opened, the distance measurement data of each measuring point on the side step connecting plate at each monitoring time is acquired. The measuring points are divided into two groups: one group is evenly distributed on the side of the moving side of the connecting plate, and the other group is evenly distributed on the bottom surface of the connecting plate.

[0007] For each measuring point, the preset time period is divided according to the change of the distance measurement data within the preset time period, all suspected obstacle-affected time periods are obtained, the obstacle-affected parameters of the corresponding time period are determined according to the length of each suspected obstacle-affected time period, and all obstacle-affected time periods are filtered out from all suspected obstacle-affected time periods.

[0008] Based on the obstacle influence parameters at each measuring point for each obstacle influence period, and the time interval between adjacent obstacle influence periods in time sequence, combined with the discrete characteristics of the obstacle influence parameters for all suspected obstacle influence periods, the obstacle confidence coefficient at each measuring point is obtained; the extension and retraction of the electric side pedal is controlled based on the obstacle confidence coefficients at all measuring points.

[0009] Furthermore, the method for obtaining the suspected period of impact of the obstacle includes:

[0010] For each measurement point, the ranging data that is less than the corresponding preset distance threshold is regarded as abnormal data, and the abnormal parameters of each abnormal data are determined according to the abnormality of the ranging data in the local time domain.

[0011] Based on the abnormal parameters, all abnormal moments within a preset time period are determined, and the time period corresponding to consecutive abnormal moments is considered as a suspected obstacle-affected period.

[0012] Furthermore, the preset distance threshold of the distance measurement data of the measuring points on the side of the connecting plate is a preset multiple of the extension limit of the telescopic rod in the electric side pedal; the preset distance threshold of the distance measurement data of the measuring points on the bottom of the connecting plate is a preset multiple of the descent limit of the pedal in the electric side pedal; the preset multiple is greater than 1.

[0013] Furthermore, the method for obtaining the abnormal parameters includes:

[0014] A preset time window is constructed with the monitoring time corresponding to each of the abnormal data as the center, and the proportion of the number of abnormal data in each preset time window is used as the abnormal parameter of the abnormal data corresponding to the center of the preset time window.

[0015] Furthermore, the method for obtaining the obstacle influence parameters includes:

[0016] The length percentage of each suspected obstacle-affected period within a preset period is used as the obstacle-affected parameter for each suspected obstacle-affected period.

[0017] Furthermore, the method for obtaining the time period of the obstacle's influence includes:

[0018] The suspected obstacle impact period when the obstacle impact parameter is greater than a preset parameter threshold is taken as the obstacle impact period.

[0019] Furthermore, the method for obtaining the barrier confidence coefficient includes:

[0020] For each measuring point, the negative correlation mapping result of the coefficient of variation of the obstacle influence parameter of all the suspected obstacle influence time periods within the preset time period is used as the first obstacle confidence parameter of the measuring point;

[0021] For each measuring point, the second obstacle confidence parameter of the measuring point is obtained based on the obstacle influence parameters of all temporally adjacent obstacle influence time periods and the time interval between temporally adjacent obstacle influence time periods.

[0022] The first obstacle confidence parameter and the second obstacle confidence parameter are fused together, and the normalized result of the fusion result is used as the obstacle confidence coefficient of the corresponding measurement point.

[0023] Furthermore, the method for obtaining the second barrier confidence parameter includes:

[0024] Within a preset time period, all the suspected obstacle-affected time periods are sorted in chronological order, and adjacent obstacle-affected time periods are taken as a time period to be analyzed.

[0025] For each time period to be analyzed, the sum of the obstacle influence parameters of all the obstacle influence time periods is used as the obstacle confidence parameter, and the negative correlation mapping result of the mean time interval between all adjacent obstacle influence time periods is used as the reference weight. The obtained obstacle confidence parameter is weighted using the reference weight to obtain the confidence sub-parameter of the corresponding time period to be analyzed.

[0026] By combining the confidence sub-parameters of all time periods to be analyzed, the second obstacle confidence parameter of the corresponding measuring point is obtained.

[0027] Furthermore, controlling the extension and retraction of the electric side pedal based on the obstacle confidence coefficient at all measuring points includes:

[0028] When the obstacle confidence coefficient at any measuring point is greater than the preset coefficient threshold, an obstacle warning is issued and the electric side pedal remains stationary; when the obstacle confidence coefficients at all measuring points are less than or equal to the preset coefficient threshold, the electric side pedal is controlled to extend normally.

[0029] A high-performance retractable electric side pedal control device includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements the steps of the high-performance retractable electric side pedal control method.

[0030] The present invention has the following beneficial effects:

[0031] This invention acquires distance measurement data at each monitoring point on the side pedal connection plate during a preset time period after the car door is opened, providing data support for subsequent judgment on whether the electric side pedal of the car has a suitable extension and retraction environment. Then, for each measuring point, the preset time period is initially divided according to the changes in the distance measurement data within the preset time period to initially filter out the influence of random abnormal distance measurement data caused by some sensor noise, etc., to acquire all suspected obstacle-affected time periods, and determine the obstacle-affected parameters reflecting the abnormal continuity of the corresponding time period, thereby screening out all obstacle-affected time periods. Then, based on the obstacle-affected parameters of each obstacle-affected time period at each measuring point, the probability of obstacle influence is initially assessed. At the same time, the time interval between adjacent obstacle-affected time periods is analyzed to assess the degree of interruption of the obstacle-affected time period, thereby assessing the continuity of the obstacle. Combining the discrete characteristics of the obstacle-affected parameters of all suspected obstacle-affected time periods, the probability of influence of random obstacles such as flying insects and raindrops is further analyzed, and the obstacle confidence coefficient at each measuring point is comprehensively obtained. The obstacle confidence coefficient reflects the probability of fixed obstacles such as curbs hindering the extension and retraction of the electric side pedal. Finally, the extension and retraction of the electric side pedal is controlled based on the obstacle confidence coefficients at all measuring points. This invention monitors the abnormal continuity of distance measurement data at each measuring point, comprehensively evaluates the impact of fixed obstacles such as curbs on the electric side pedal, reduces the influence of other random interference factors, improves the accuracy of the assessment of the impact of environmental obstacles on the extension and retraction of the electric side pedal, and thus improves the control effect of the electric side pedal. Attached Figure Description

[0032] To more clearly illustrate the technical solutions and advantages in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0033] Figure 1 A flowchart illustrating a high-performance retractable electric side pedal control method provided in one embodiment of the present invention;

[0034] Figure 2 This is a flowchart illustrating a method for obtaining a barrier confidence coefficient according to an embodiment of the present invention. Detailed Implementation

[0035] To further illustrate the technical means and effects adopted by the present invention to achieve its intended purpose, the following, in conjunction with the accompanying drawings and preferred embodiments, details the specific implementation, structure, features, and effects of a high-performance retractable electric side pedal control method and device proposed according to the present invention. In the following description, different "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. Furthermore, specific features, structures, or characteristics in one or more embodiments can be combined in any suitable form.

[0036] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains.

[0037] The following description, in conjunction with the accompanying drawings, details the specific scheme of a high-performance retractable electric side pedal control method and device provided by the present invention.

[0038] Please see Figure 1 The diagram illustrates a flowchart of a high-performance retractable electric side pedal control method according to an embodiment of the present invention, specifically including:

[0039] Step S1: During a preset time period after the car door is opened, acquire distance measurement data of each measuring point on the side pedal connecting plate at each monitoring time. The measuring points are divided into two groups: one group is evenly distributed on the side of the moving side of the connecting plate, and the other group is evenly distributed on the bottom surface of the connecting plate.

[0040] It should be noted that the embodiments of the present invention are aimed at existing electric side steps in automobiles; when the vehicle is in motion, the electric side steps are usually retracted to the bottom of the vehicle; after parking and opening the door, the electric side steps will automatically extend outward and downward to provide a step; the general structure and working principle of the electric side steps are briefly described here:

[0041] The extension and retraction of the electric side steps are controlled by a telescopic rod, which is connected to a connecting plate, and the connecting plate is connected to the step assembly. When the telescopic rod extends or retracts, it drives the connecting plate to move within a fixed slide groove, which in turn drives the step assembly to move, thus allowing the step assembly to move horizontally, such as extending to the outer edge of the vehicle. The step assembly includes a side step and a step telescopic rod. When the step telescopic rod extends or retracts, it drives the side step to move vertically, such as descending to form a step for people to get on and off.

[0042] It should be noted that electric side steps may be installed on both sides of the car or under each door. The extension and retraction control method for each electric side step is the same. Here, we will only use one as an example for analysis and description.

[0043] To determine whether the electric side step of a car has a suitable environment for extension and retraction after the car door is opened, one embodiment of the present invention first evenly distributes measuring points on the connecting plate of the electric side step to monitor the surrounding environment, thereby controlling the automatic extension and retraction of the step; wherein, an infrared ranging sensor or a radar sensor is installed on each measuring point to monitor the distance relative to surrounding obstacles, and a sensor with a ranging distance of not less than 2 meters is selected to avoid affecting the subsequent obstacle analysis effect;

[0044] The measuring points are then divided into two groups. One group is evenly distributed on the side of the connecting plate on the moving side to collect information on the horizontal extension and retraction obstacles of the electric side pedal. The other group is evenly distributed on the bottom surface of the connecting plate to collect information on the vertical extension and retraction obstacles of the electric side pedal. For example, a measuring point is set up every 5cm on the side of the connecting plate, and the same is done on the bottom surface of the connecting plate. The spacing of each group of measuring points can also be adjusted according to the length of the connecting plate.

[0045] The preset time period is set to 5 seconds. Within 5 seconds after the car door is opened (the moment the door open button is pressed, but before the door is fully open), the distance measurement data of each measuring point on the side pedal connecting plate is collected in real time at each monitoring moment in order to assess the surrounding environmental obstacles. The acquisition frequency of all sensors is set to 50 Hz and is collected synchronously at the moment the car door is opened. The implementer can also customize the acquisition frequency.

[0046] Each sensor corresponds to a series of time-series ranging data. The ranging data collected by all sensors is then uploaded to the existing onboard computing unit for subsequent data analysis. After receiving the ranging data, the onboard computing unit also needs to perform standardization processing such as dimension removal on the ranging data for subsequent analysis and calculation. The above operations are all existing technologies and will not be described in detail.

[0047] It should be noted that the preset time period needs to be set according to the full opening time of the car door. For example, it usually takes 5 seconds for a traditional manual car door to be fully opened and ready for people to get out after pressing the open button, while an electric car door may take 7 seconds. Therefore, the implementer can set it according to the actual situation.

[0048] Step S2: For each measuring point, the preset time period is divided according to the change of distance measurement data within the preset time period, all suspected obstacle-affected time periods are obtained, the obstacle-affected parameters of the corresponding time period are determined according to the length of each suspected obstacle-affected time period, and all obstacle-affected time periods are selected from all suspected obstacle-affected time periods.

[0049] Considering that the distance measurement data of each measuring point within the preset time period can reflect the environmental obstacle information around the vehicle, for each measuring point, when there are obstacles around and it is not conducive to the extension and retraction of the electric side pedal, the distance measurement data is relatively small and remains stable for a certain period of time; conversely, when there are random interference factors such as sensor noise, flying insects or raindrops, the distance measurement data may show more obvious random fluctuations.

[0050] Based on the different variation characteristics of ranging data under the above different scenarios, the preset time period can be divided first to obtain all suspected obstacle-affected time periods. Then, combined with the characteristic that fixed obstacles such as curbs cause the ranging data to be stable in time series, the length of each suspected obstacle-affected time period can be analyzed to evaluate the obstacle-affected parameters of the suspected obstacle-affected time period. The obstacle-affected parameters reflect the short-term persistence of abnormal data and further reflect the possibility of obstacles existing in the suspected obstacle-affected time period, so as to screen out all obstacle-affected time periods.

[0051] Considering that for each set of measuring points, the electric side pedal can only be safely retracted if the distance measurement data exceeds a certain safe extension distance, that is, if no other object is detected within the safe extension range; otherwise, the distance measurement data can be regarded as abnormal or dangerous data under the condition that it is not safe to retract.

[0052] Considering that both random and fixed obstacles will result in a large amount of abnormal data distributed within a preset time period, we can further evaluate the abnormal parameters of each abnormal data by checking whether there are other abnormal data in the local time domain of each abnormal data. The larger the abnormal parameters, the more likely the abnormal data is not caused by sensor noise, but has a certain degree of persistence, and is more likely to be caused by an obstacle. Therefore, based on the continuity of abnormal data within the preset time period, we can preliminarily determine the period of suspected obstacle influence.

[0053] Based on this, in a preferred embodiment of the present invention, the method for obtaining the suspected period of impact of the obstacle includes:

[0054] For each measuring point, ranging data that is less than the corresponding preset distance threshold is regarded as abnormal data. Based on the abnormality of ranging data in the local time domain of each abnormal data, the abnormal parameters of each abnormal data are determined. Based on the abnormal parameters, all abnormal moments in the preset time period are determined, and the time period corresponding to consecutive abnormal moments is regarded as a suspected obstacle influence period.

[0055] As an example, taking any measuring point as an example, determine all the suspected obstacle-affected periods within the preset time period; first, determine the preset distance threshold corresponding to each group of measuring points, and then regard the ranging data that is less than the corresponding preset distance threshold as abnormal data;

[0056] In a preferred embodiment of the present invention, considering that the extension distance of the telescopic rod of the electric side pedal and the movement distance of the connecting plate in the slide groove are approximately equal to the movement distance of the pedal assembly, and the movement distance of the pedal assembly determines whether it can collide with the obstacle after being fully extended; based on this, a corresponding preset distance threshold can be determined according to the movement limit (horizontal extension limit) of the pedal assembly. At the same time, to ensure a certain level of safety, a certain margin can be reserved, and the preset distance threshold can be set slightly larger than the movement limit; similarly, a corresponding preset distance threshold can be determined according to the vertical movement limit of the pedal assembly.

[0057] Based on this, the preset distance threshold of the distance measurement data of the measuring points on the side of the connecting plate is a preset multiple of the extension limit of the telescopic rod in the electric side pedal; the preset distance threshold of the distance measurement data of the measuring points on the bottom of the connecting plate is a preset multiple of the descent limit of the pedal in the electric side pedal; the preset multiple is greater than 1.

[0058] Specifically, the preset multiplier is set to 1.2, but the implementer can also customize it. Among them, 1.2 times the extension limit of the telescopic rod in the electric side pedal is used as the preset distance threshold for the distance measurement data of the measuring point on the side of the connecting plate; 1.2 times the descent limit of the electric side pedal (the extension limit of the pedal telescopic rod) is used as the preset distance threshold for the distance measurement data of the measuring point on the bottom surface of the connecting plate. The extension limits of the telescopic rod corresponding to the connecting plate and the pedal telescopic rod can be obtained by referring to the relevant product information of the electric side pedal, and will not be elaborated here.

[0059] In a preferred embodiment of the present invention, in order to facilitate the analysis of the continuous distribution of abnormal data, a preset time window is first constructed to accurately determine the continuous time period corresponding to the abnormal data suspected to be caused by the obstacle. The method for obtaining the abnormal parameters includes: constructing a preset time window with the monitoring time corresponding to each abnormal data as the center, and taking the proportion of the number of abnormal data in each preset time window as the abnormal parameter of the abnormal data corresponding to the center of the preset time window.

[0060] Specifically, the length of the preset window is set to 51. Taking the monitoring time corresponding to any abnormal data as the center, 25 ranging data points are obtained from both sides to obtain a time period, which is the time period corresponding to the preset time window. When there are less than 25 ranging data points on either side, they can be supplemented from the other side. The implementer can also customize the length of the preset time window. Furthermore, within each preset time window, the proportion of the total number of abnormal data points in the preset time window is counted. The larger the proportion, the greater the abnormal frequency of the ranging data corresponding to the center of the preset time window, and the larger the abnormal parameter.

[0061] Furthermore, when the side pedal extension faces an obstacle, there will be obvious periods of continuous abnormal data. When the abnormal parameters of the continuous abnormal data within the corresponding preset time window are larger and closer, it indicates that the corresponding time period is more likely to be the period when the extension faces an obstacle. Therefore, the monitoring time corresponding to the abnormal data with abnormal parameters greater than a preset threshold, such as 0.6, is first regarded as the abnormal time. The implementer can also adjust the preset threshold himself. Then, the time period corresponding to the continuous abnormal time within the preset time period is regarded as a suspected obstacle-affected time period, so that all suspected obstacle-affected time periods can be obtained.

[0062] In another embodiment of the present invention, the abnormal parameters can also be clustered, such as by using the DBSCAN clustering algorithm, which is a well-known technology and will not be described in detail here. The abnormal parameters in each cluster correspond to abnormal data with similar local time-domain anomalies. Then, the monitoring time of each abnormal data in the cluster with the largest abnormal parameter corresponding to the cluster center is taken as the abnormal time, thereby determining the continuous time period corresponding to the abnormal time and obtaining the suspected obstacle impact time period.

[0063] Furthermore, considering that the longer the suspected obstacle impact period is, the more likely it is to be caused by the continuous impact of fixed obstacles such as curbs, the obstacle impact parameters of each suspected obstacle impact period can be determined, and then all obstacle impact periods can be screened out from all suspected obstacle impact periods.

[0064] Preferably, in one embodiment of the present invention, the method for obtaining obstacle influence parameters includes:

[0065] The length percentage of each suspected obstacle-affected period within the preset time period is used as the obstacle-affected parameter for each suspected obstacle-affected period.

[0066] Preferably, in one embodiment of the present invention, the method for obtaining the period of time affected by the obstacle includes:

[0067] The period during which the obstacle impact parameter is greater than the preset parameter threshold is considered the obstacle impact period.

[0068] Specifically, the preset parameter threshold is set to 0.2, which implementers can also adjust themselves, thereby filtering out the periods of impact from all suspected periods of impact.

[0069] Step S3: Based on the obstacle influence parameters of each obstacle influence period at each measuring point, and the time interval between adjacent obstacle influence periods in time sequence, combined with the discrete characteristics of the obstacle influence parameters of all suspected obstacle influence periods, obtain the obstacle confidence coefficient at each measuring point; control the extension and retraction of the electric side pedal based on the obstacle confidence coefficients at all measuring points.

[0070] Considering that under normal fixed obstacle conditions, ranging data is relatively stable and abnormal data has high continuity, the possibility of segmentation of the obstacle-affected time period is low, and it usually appears as a long continuous period. However, instantaneous or random interference such as raindrops falling, flying insects passing by, or sensor noise may cause the abnormal data at the monitoring moment to deviate from the abnormal data caused by fixed obstacles, thus causing the obstacle-affected time period to be segmented.

[0071] Furthermore, considering that the shorter the time interval between adjacent obstacle influence periods within the preset time period, and the larger the obstacle influence parameter of the obstacle influence period, it indicates that the obstacle influence period is more likely to be segmented due to random factors.

[0072] It is also considered that the more discrete the distribution of the obstacle impact parameters during the suspected obstacle impact period is within the preset time period, the greater the possibility that it is the impact of random interference such as dust or continuous raindrops, and the relatively smaller the possibility that it is the impact of fixed obstacles.

[0073] Based on this, the obstacle confidence coefficient at each measuring point can be comprehensively evaluated. The obstacle confidence coefficient reflects the probability of detecting a fixed obstacle at that measuring point. The larger the obstacle confidence coefficient, the more it indicates that the electric side pedal does not have a suitable extension and retraction environment.

[0074] Preferably, in one embodiment of the present invention, the method for obtaining the barrier confidence coefficient includes:

[0075] Please see Figure 2 The flowchart illustrates a method for obtaining a barrier confidence coefficient according to an embodiment of the present invention, specifically including:

[0076] Step S301: For each measuring point, the negative correlation mapping result of the coefficient of variation of the obstacle influence parameter of all suspected obstacle influence periods within the preset time period is used as the first obstacle confidence parameter of the measuring point.

[0077] Considering that the coefficient of variation can reflect the discrete characteristics of data to a certain extent, and that the larger the coefficient of variation of the obstacle influence parameter, the less stable the obstacle is in time series, the lower the probability that it is a fixed obstacle, and thus the logical relationship can be adjusted through negative correlation mapping.

[0078] As an example, for each measurement point, the coefficient of variation is calculated based on the obstacle impact parameters of all suspected obstacle impact periods within a preset time period. The acquisition of the coefficient of variation is a well-known technique and will not be elaborated further. The coefficient of variation is then mapped to the sigmoid function to adjust the value range. Then, the mapping value is subtracted from the constant 1 to adjust the logical relationship with negative correlation, thus obtaining the first obstacle confidence parameter at that measurement point. This makes the larger the coefficient of variation, the closer the sigmoid function is to 1, and the smaller the first obstacle confidence parameter is.

[0079] Step S302: For each measuring point, obtain the second obstacle confidence parameter of the measuring point based on the obstacle influence parameters of all adjacent obstacle influence time periods in time sequence and the time interval between adjacent obstacle influence time periods in time sequence.

[0080] Since the smaller the time interval between adjacent obstacle influence periods in time sequence, the greater the possibility that the obstacle influence period is segmented due to random factors; at the same time, the larger the obstacle influence parameter of the obstacle influence period, the greater the possibility that it is affected by a fixed obstacle.

[0081] Based on this, in a preferred embodiment of the present invention, the method for obtaining the second barrier confidence parameter includes:

[0082] Within a preset time period, all suspected obstacle-affected time periods are sorted in chronological order, and adjacent obstacle-affected time periods are grouped together as a time period to be analyzed.

[0083] For each time period to be analyzed, the sum of the obstacle influence parameters of all obstacle influence time periods is used as the obstacle confidence parameter. The negative correlation mapping result of the mean time interval between all adjacent obstacle influence time periods is used as the reference weight. The obtained obstacle confidence parameter is weighted using the reference weight to obtain the confidence sub-parameter of the corresponding time period to be analyzed.

[0084] By combining the confidence sub-parameters of all time periods to be analyzed, the second obstacle confidence parameter of the corresponding measuring point is obtained.

[0085] As an example, for ease of understanding, the suspected obstacle-affected periods within the preset time period are first sorted in chronological order. The label of the obstacle-affected period is set to 1, and the labels of the remaining periods are set to 0, resulting in a label sequence such as [0,1,1,0,1,1]. The obstacle-affected periods corresponding to the 2nd and 3rd labels, and the obstacle-affected periods corresponding to the 5th and 6th labels in the label sequence can be regarded as a continuous obstacle-affected period, i.e., the period to be analyzed. The period to be analyzed also includes the time interval between obstacle-affected periods, which may be divided into two obstacle-affected periods due to random noise.

[0086] Then, taking any time period to be analyzed as an example, the sum of the obstacle influence parameters of all obstacle influence time periods is used as the obstacle confidence parameter; the mean of the time interval between all adjacent obstacle influence time periods is mapped to the exponential function exp(-x) with the natural constant e as the base and the logic is adjusted by negative correlation mapping to obtain the reference weight; then the reference weight is multiplied by the obstacle confidence parameter to obtain the confidence sub-parameter of the corresponding time period to be analyzed.

[0087] Finally, the confidence sub-parameters of all the time periods to be analyzed are summed to obtain the second obstacle confidence parameter for the corresponding measurement point.

[0088] Step S303: Fuse the first obstacle confidence parameter and the second obstacle confidence parameter, and use the normalized result of the fusion result as the obstacle confidence coefficient of the corresponding measurement point.

[0089] As an example, for each measuring point, the first obstacle confidence parameter and the second obstacle confidence parameter are multiplied and fused together. Then, the product is mapped to the sigmoid function and the value range is adjusted to 0-1 to obtain the obstacle confidence coefficient of the corresponding measuring point. The obstacle confidence coefficient is the resistance confidence coefficient of fixed obstacles such as curbs to the extension and retraction of the electric side pedal, which provides a basis for subsequent control of its extension and retraction.

[0090] After obtaining the obstacle confidence coefficient at each measuring point, the extension and retraction of the electric side pedal can be further controlled based on the obstacle confidence coefficients at all measuring points.

[0091] Preferably, in one embodiment of the present invention, considering that the location distribution of fixed obstacles may not be able to be detected by all measuring points, but when any measuring point in the two sets of measuring points detects a fixed obstacle, it may cause the extension and retraction of the electric side pedal to be restricted, thereby causing a collision, then when the obstacle confidence coefficient of any measuring point is greater than a preset coefficient threshold, an obstacle warning is issued and the electric side pedal remains stationary; when the obstacle confidence coefficient of all measuring points is less than or equal to the preset coefficient threshold, the electric side pedal is controlled to extend normally.

[0092] As an example, the preset coefficient threshold is set to 0.3, but implementers can adjust it themselves.

[0093] The present invention also proposes a high-performance retractable electric side pedal control device, the device including a memory, a processor and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the high-performance retractable electric side pedal control method described in steps S1-S3 above.

[0094] In summary, this invention acquires distance measurement data at each monitoring point on the side step connecting plate at each monitoring moment within a preset time period after the vehicle door is opened; then, it analyzes and determines all suspected obstacle-affected time periods and their obstacle-affected parameters for each measuring point within the preset time period, and filters out all obstacle-affected time periods; furthermore, based on the obstacle-affected parameters of each obstacle-affected time period at each measuring point, and the time interval between adjacent obstacle-affected time periods in time sequence, combined with the discrete characteristics of the obstacle-affected parameters of all suspected obstacle-affected time periods, it obtains the obstacle confidence coefficient at each measuring point; finally, it controls the extension and retraction of the electric side step based on the obstacle confidence coefficients at all measuring points. This invention, by monitoring the abnormal continuity of distance measurement data at each measuring point, comprehensively evaluates the impact of fixed obstacles such as curbs on the electric side step, reduces the influence of other random interference factors, improves the accuracy of the assessment of the environmental obstacle impact on the extension and retraction of the electric side step, and thus improves the control effect of the electric side step.

[0095] It should be noted that the order of the above embodiments of the present invention is merely for descriptive purposes and does not represent the superiority or inferiority of the embodiments. The processes depicted in the accompanying drawings do not necessarily require a specific or sequential order to achieve the desired result. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0096] The various embodiments in this specification are described in a progressive manner. The same or similar parts between the various embodiments can be referred to each other. Each embodiment focuses on describing the differences from other embodiments.

Claims

1. A high-performance retractable electric side pedal control method, characterized in that, The method includes: Within a preset time period after the car door is opened, the distance measurement data of each measuring point on the side step connecting plate at each monitoring time is acquired. The measuring points are divided into two groups: one group is evenly distributed on the side of the moving side of the connecting plate, and the other group is evenly distributed on the bottom surface of the connecting plate. For each measuring point, the preset time period is divided according to the change of the distance measurement data within the preset time period, all suspected obstacle-affected time periods are obtained, the obstacle-affected parameters of the corresponding time period are determined according to the length of each suspected obstacle-affected time period, and all obstacle-affected time periods are filtered out from all suspected obstacle-affected time periods. Based on the obstacle influence parameters at each measuring point for each obstacle influence period, and the time interval between adjacent obstacle influence periods in time sequence, combined with the discrete characteristics of the obstacle influence parameters for all suspected obstacle influence periods, the obstacle confidence coefficient at each measuring point is obtained; the extension and retraction of the electric side pedal is controlled based on the obstacle confidence coefficients at all measuring points. The methods for obtaining the suspected period of impact of the obstacle include: For each measurement point, the ranging data that is less than the corresponding preset distance threshold is regarded as abnormal data, and the abnormal parameters of each abnormal data are determined according to the abnormality of the ranging data in the local time domain. Based on the abnormal parameters, all abnormal moments within a preset time period are determined, and the time period corresponding to consecutive abnormal moments is taken as a suspected obstacle-affected time period. The methods for obtaining the abnormal parameters include: A preset time window is constructed with the monitoring time corresponding to each of the abnormal data as the center, and the proportion of the number of abnormal data in each preset time window is used as the abnormal parameter of the abnormal data corresponding to the center of the preset time window. The method for obtaining the obstacle influence parameters includes: The length percentage of each suspected obstacle-affected period within a preset period is used as the obstacle-affected parameter for each suspected obstacle-affected period. The method for obtaining the period of time affected by the obstacle includes: The suspected obstacle impact period when the obstacle impact parameter is greater than a preset parameter threshold is defined as the obstacle impact period. The method for obtaining the barrier confidence coefficient includes: For each measuring point, the negative correlation mapping result of the coefficient of variation of the obstacle influence parameter of all the suspected obstacle influence time periods within the preset time period is used as the first obstacle confidence parameter of the measuring point; For each measuring point, the second obstacle confidence parameter of the measuring point is obtained based on the obstacle influence parameters of all temporally adjacent obstacle influence time periods and the time interval between temporally adjacent obstacle influence time periods. The first obstacle confidence parameter and the second obstacle confidence parameter are fused together, and the normalized result of the fusion result is used as the obstacle confidence coefficient of the corresponding measurement point; The method for obtaining the second barrier confidence parameter includes: Within a preset time period, all the suspected obstacle-affected time periods are sorted in chronological order, and adjacent obstacle-affected time periods are taken as a time period to be analyzed. For each time period to be analyzed, the sum of the obstacle influence parameters of all the obstacle influence time periods is used as the obstacle confidence parameter, and the negative correlation mapping result of the mean time interval between all adjacent obstacle influence time periods is used as the reference weight. The obtained obstacle confidence parameter is weighted using the reference weight to obtain the confidence sub-parameter of the corresponding time period to be analyzed. By combining the confidence sub-parameters of all time periods to be analyzed, the second obstacle confidence parameter for the corresponding measurement point is obtained.

2. The high-performance retractable electric side pedal control method according to claim 1, characterized in that, The preset distance threshold of the distance measurement data from the measuring points on the side of the connecting plate is a preset multiple of the extension limit of the telescopic rod in the electric side pedal; the preset distance threshold of the distance measurement data from the measuring points on the bottom of the connecting plate is a preset multiple of the descent limit of the pedal in the electric side pedal; the preset multiple is greater than 1.

3. The high-performance retractable electric side pedal control method according to claim 1, characterized in that, The extension and retraction of the electric side pedal are controlled based on the obstacle confidence coefficients at all measuring points, including: When the obstacle confidence coefficient at any measuring point is greater than the preset coefficient threshold, an obstacle warning is issued and the electric side pedal remains stationary; when the obstacle confidence coefficients at all measuring points are less than or equal to the preset coefficient threshold, the electric side pedal is controlled to extend normally.

4. A high-performance retractable electric side pedal control device, the device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the steps of the high-performance retractable electric side pedal control method as described in any one of claims 1 to 3.

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