A method for steering by horn switch adas function

Abstract

The application provides a method for switching ADAS function by gear lever, and belongs to the technical field of ADAS function control. The method for switching ADAS function by gear lever comprises the following steps: identifying a lever gesture according to a level signal of a gear lever rod after de-bouncing processing and a preset lever timing threshold parameter, wherein the lever gesture at least comprises one of short touch, double click, long press and continuous hit; performing state analysis on the lever gesture by a finite state machine to determine a candidate trigger action, wherein the candidate trigger action is used for switching ADAS function; determining a safety gate check result of the candidate trigger action according to a driving scene, wherein the driving scene at least comprises one of high speed, city and congestion; and executing the candidate trigger action according to the safety gate check result; wherein the preset lever timing threshold parameter is updated based on an adaptive learning mechanism according to feature data of the lever gesture recorded each time.

Description

Technical Field

[0001] This invention relates to the field of ADAS function control technology, and in particular to a method for controlling ADAS functions via a gear shift switch. Background Technology

[0002] Currently, ADAS (ACC / NOA, etc.) functions are mostly triggered via steering wheel buttons or the central control screen; column shifters (electronic gear levers) are becoming a new trend. Traditional solutions suffer from limited button space, leading to high rates of accidental or missed touches in different scenarios (highways, cities, traffic jams), resulting in inconsistent user experiences. Furthermore, current column shifter solutions have fixed parameters, lack self-learning and closed-loop optimization, and lack robust mechanisms such as CRC snapshots and soft resets, resulting in insufficient system reliability and inconsistent user experiences. Summary of the Invention

[0003] This invention aims to solve at least one of the technical problems existing in the prior art, and proposes a method for realizing ADAS intelligent triggering and self-learning through a shift lever, thereby improving interaction efficiency and safety by using a shift lever to switch ADAS functions.

[0004] In a first aspect, embodiments of the present invention provide a method for using a gear selector to switch ADAS functions, comprising: identifying a gear selector gesture based on the level signal of the de-shaking gear selector and a preset gear selector timing threshold parameter, wherein the gear selector gesture includes at least one of short touch, double tap, long press, and continuous tap; performing state analysis on the gear selector gesture using a finite state machine to determine candidate trigger actions, wherein the candidate trigger actions are used to switch ADAS functions; and determining the safety gating verification result of the candidate trigger actions based on the driving scenario to execute the candidate trigger actions, wherein the driving scenario includes at least one of highway, city, and congestion; wherein the preset gear selector timing threshold parameter is updated based on the feature data of each recorded gear selector gesture using an adaptive learning mechanism, and the preset gear selector timing threshold parameter includes de-shaking duration, upper limit of double tap interval time, lower limit of double tap interval time, and long press threshold; the feature data includes the gesture type identifier of the gear selector gesture, gesture time feature, vehicle state information at the time of triggering, driving scenario label, and safety gating verification result.

[0005] According to an embodiment of the present invention, the de-jitter processing includes: detecting the level state of the original level signal within a de-jitter time window with the current time as the endpoint and the de-jitter duration as the width; outputting a de-jitter signal at the first level in response to the original level signal remaining at the first level within the de-jitter time window; outputting a de-jitter signal at the second level in response to the original level signal remaining at the second level within the de-jitter time window; and maintaining the de-jitter signal from the previous time moment in response to a level jump in the original level signal within the de-jitter time window.

[0006] According to an embodiment of the present invention, recognizing a lever gesture includes: recognizing it as a short touch when the pressing duration is less than the upper threshold of a short touch; recognizing it as a double touch when the time interval between two short touches is between the upper limit of a double-tap interval and the lower limit of a double-tap interval; recognizing it as a long press when the pressing duration is greater than the long press threshold; and recognizing it as a combination operation of short touches in a second direction within a preset time interval after detecting a short touch in the first direction.

[0007] According to an embodiment of the present invention, a finite state machine is used to parse the state of a lever gesture and determine candidate trigger actions, including: in response to the detection of the first short touch in the idle state, a transition to a pending state is initiated; in response to the detection of a second short touch in the pending state, and the time interval between the second short touch and the first short touch being within the double-tap time window, it is determined whether the direction of the second short touch is the same as that of the first short touch; if they are the same, a transition to a double-tap confirmation state is initiated, and the candidate trigger action corresponding to the double-tap confirmation state is NOA function activation; if they are different, a transition to a double-tap confirmation state is initiated, and the candidate trigger action corresponding to the double-tap confirmation state is to temporarily suppress or restore the currently running ADAS function; in response to the absence of a second short touch within the double-tap time window in the pending state, a transition to a short touch confirmation state is initiated, and the candidate trigger action corresponding to the short touch confirmation state is to activate or cancel the ACC function; in response to the detection that the press duration reaches a long press threshold in any state, a transition to a long press confirmation state is initiated, and the candidate trigger action corresponding to the long press confirmation state is to enter configuration mode or perform a soft reset.

[0008] According to an embodiment of the present invention, determining the safety gating verification result of candidate triggering actions based on the driving scenario to execute the candidate triggering actions includes: in response to the current driving scenario being congested, disallowing NOA function activation; allowing entry into configuration mode or soft reset only when the execution layer is idle and the speed is less than a first preset speed threshold; in response to the current driving scenario being urban, allowing NOA function activation only when the speed is greater than a second preset speed threshold; allowing entry into configuration mode or soft reset only when the execution layer is idle and the speed is less than a first preset speed threshold; in response to the current driving scenario being high-speed, allowing NOA function activation only when the speed is greater than a third preset speed threshold; allowing entry into configuration mode or soft reset only when the execution layer is idle and the speed is less than a first preset speed threshold; At a preset speed threshold, entering configuration mode or performing a soft reset is allowed. In response to a candidate trigger action being activated by the NOA function, the mutual exclusion relationship between the NOA and ACC functions is checked, allowing the NOA function to take over control of the ACC function. In response to a detected strong driver intent, the candidate trigger action is suppressed, including steering wheel torque, turn signal status, and pedal input signals. In response to a failure of any one of the following checks: sensor self-test result, CAN communication status, or map validity, the candidate trigger action is reverted to a lower-level ADAS function, and a warning message is output. The ADAS function levels, from highest to lowest, are NOA function and ACC function.

[0009] According to an embodiment of the present invention, the method for determining a driving scenario includes: in response to the existence of a user-preset driving scenario or a map-preset driving scenario, determining a driving scenario based on the user-preset driving scenario or the map-preset driving scenario; in response to the absence of a user-preset driving scenario and a map-preset driving scenario, determining a driving scenario based on vehicle speed and start-stop conditions; wherein, determining a driving scenario based on vehicle speed and start-stop conditions includes: in response to a vehicle speed greater than or equal to a fourth preset speed threshold, determining the driving scenario as a high-speed mode; in response to a vehicle speed less than a fifth preset speed threshold and the presence of start-stop conditions, determining the driving scenario as a congestion mode; in response to not belonging to either a high-speed mode or a congestion mode, determining the driving scenario as an urban mode.

[0010] According to an embodiment of the present invention, a method for updating a preset lever timing threshold parameter includes: independently recording feature data of valid events under different driving scenarios to form feature data sets corresponding to each driving scenario; responding to a situation where the number of feature data in the feature data set is greater than a minimum sample size threshold, performing outlier detection on the feature data in the feature data set using the median absolute deviation method, removing feature data that meets the outlier determination criteria from the feature data set to obtain robust feature data after outlier removal; calculating robust statistics based on the robust feature data, and updating the preset lever timing threshold parameter using an exponentially weighted moving average method, wherein the updated preset lever timing threshold parameter is a weighted combination of the original preset lever timing threshold parameter and the robust statistics; wherein a single-step change limit constraint is applied to the update of the preset lever timing threshold parameter to ensure that the absolute value of the parameter change before and after a single update does not exceed a preset maximum step size; and a cooling cycle constraint is applied to the update of the preset lever timing threshold parameter, performing a parameter update at most once within a preset cooling time.

[0011] Secondly, the present invention provides a system for implementing ADAS functionality via a gearshift lever, which can be used to implement the aforementioned method for implementing ADAS functionality via a gearshift lever, comprising: a gesture recognition module for recognizing lever gestures based on the level signal of the de-shaking processed gearshift lever and preset lever timing threshold parameters, wherein the lever gestures include at least one of short touch, double tap, long press, and continuous tap; an action determination module for performing state analysis on the lever gestures using a finite state machine to determine candidate trigger actions, wherein the candidate trigger actions are used to switch the ADAS function on and off; and a verification execution module for determining the safety gating verification results of the candidate trigger actions based on the driving scenario to execute the candidate trigger actions, wherein the driving scenario includes at least one of highway, urban, and congested driving scenarios; wherein the preset lever timing threshold parameters are updated based on the feature data of each recorded lever gesture using an adaptive learning mechanism, and the preset lever timing threshold parameters include de-shaking duration, upper limit of double tap interval time, lower limit of double tap interval time, and long press threshold; the feature data includes the gesture type identifier of the lever gesture, gesture time characteristics, vehicle state information at the time of triggering, driving scenario label, and safety gating verification results.

[0012] A third aspect of the present invention provides an electronic device comprising: one or more processors; and a memory for storing one or more programs, wherein when the one or more programs are executed by the one or more processors, the one or more processors cause the one or more processors to perform the method described above for ADAS function via a gear shift switch.

[0013] A fourth aspect of the invention also provides a computer-readable storage medium having executable instructions stored thereon, which, when executed by a processor, cause the processor to perform the above-described method for ADAS function via a shift switch.

[0014] The method for enabling ADAS functions via a column shifter provided by this invention replaces traditional steering wheel buttons and central control screen operations with multi-gesture recognition of the column shifter lever. It reuses existing hardware interfaces to achieve differentiated triggering of ADAS functions, reducing the space occupied by steering wheel buttons and lowering hardware costs. A finite state machine parses gestures into clear candidate trigger actions, avoiding accidental triggering caused by gesture ambiguity. Safety gating performs scenario-based legality verification of candidate trigger actions based on driving scenarios, ensuring that advanced functions such as NOA are only activated in appropriate scenarios, reducing the risk of accidental touches. An adaptive learning mechanism independently updates the temporal threshold parameters in each scenario bucket based on actual gesture feature data, making the recognition threshold conform to individual driving habits and gradually and smoothly follow changes in habits, improving trigger consistency and user satisfaction. Attached Figure Description

[0015] Figure 1 A flowchart illustrating a method for using ADAS function via a shift switch, as provided in an embodiment of the present invention;

[0016] Figure 2 This is a schematic diagram of the architecture and data flow provided for an embodiment of the present invention;

[0017] Figure 3 This is a schematic diagram of state machine transitions provided in an embodiment of the present invention;

[0018] Figure 4 This is a timing diagram of double-click triggering NOA provided in an embodiment of the present invention;

[0019] Figure 5 This is a schematic diagram illustrating the anti-shake effect provided in an embodiment of the present invention;

[0020] Figure 6 This is a structural block diagram of an electronic device provided in an embodiment of the present invention. Detailed Implementation

[0021] To enable those skilled in the art to better understand the technical solutions of the present invention, exemplary embodiments of the present invention are described below in conjunction with the accompanying drawings, including various details of the embodiments of the present invention to aid understanding. These should be considered merely exemplary. Therefore, those skilled in the art should recognize that various changes and modifications can be made to the embodiments described herein without departing from the scope and spirit of the present invention. Similarly, for clarity and brevity, descriptions of well-known functions and structures are omitted in the following description.

[0022] Where there is no conflict, the various embodiments of the present invention and the features thereof may be combined with each other.

[0023] As used herein, the term “and / or” includes any and all combinations of one or more related enumerated entries.

[0024] The terminology used herein is for the purpose of describing particular embodiments only and is not intended to limit the invention. As used herein, the singular forms “a” and “the” are intended to include the plural forms as well, unless the context clearly indicates otherwise. It will also be understood that when the terms “comprising” and / or “made of” are used in this specification, the presence of the stated feature, integral, step, operation, element, and / or component is specified, but the presence or addition of one or more other features, integrals, steps, operations, elements, components, and / or groups thereof is not excluded. Terms such as “connected” or “linked” are not limited to physical or mechanical connections but can include electrical connections, whether direct or indirect.

[0025] Unless otherwise specified, all terms used herein (including technical and scientific terms) have the same meaning as commonly understood by one of ordinary skill in the art. It will also be understood that terms such as those defined in commonly used dictionaries should be interpreted as having the meaning consistent with their meaning in the context of the relevant art and the invention, and will not be interpreted as having an idealized or overly formal meaning unless expressly so defined herein.

[0026] In the technical solution of this invention, the collection, storage, use, processing, transmission, provision, and disclosure of user personal information all comply with relevant laws and regulations and do not violate public order and good morals. The use of user data in this technical solution follows relevant national laws and regulations (e.g., the "Information Security Technology - Personal Information Security Specification"). For example: appropriate measures are taken for personal information access control; restrictions are imposed on the display of personal information; the purpose of using personal information does not exceed the scope of direct or reasonable association; and explicit identity targeting is eliminated when using personal information to avoid precisely locating a specific individual.

[0027] In related technologies, most existing technologies enable and disable ADAS functions via physical buttons on the steering wheel or via buttons on the central control MP5. With increasing intelligence, physical buttons on the steering wheel are becoming insufficient and require continuous cost reduction; the central control is not suitable for operation while driving.

[0028] To address at least one of the technical problems existing in the aforementioned related technologies, this invention aims to utilize a column shifter (combinations of upshifting, downshifting, long press, and double-click) to implement ACC and NOA functions. Combined with a self-learning and scenario recognition mechanism, it achieves adaptive adjustment of trigger parameters, balancing individual driving habits with system safety gating, thereby improving functional consistency and reliability. The column shifter is an electronic shifter mounted on the steering column, also known as a column-type electronic shifter.

[0029] Figure 1A flowchart illustrating a method for using the ADAS (Advanced Driver Assistance Systems) function via a column shifter switch, as provided in an embodiment of the present invention, is shown below. Figure 1 As shown, this embodiment of the invention provides a method for using a gear selector to switch ADAS functions, comprising: S1, identifying a gear selector gesture based on the level signal of the de-shaking gear selector and a preset gear selector timing threshold parameter, wherein the gear selector gesture includes at least one of short touch, double tap, long press, and continuous tap; S2, performing state analysis on the gear selector gesture using a finite state machine to determine candidate trigger actions, which are used to switch ADAS functions; S3, determining the safety gating verification result of the candidate trigger actions based on the driving scenario to execute the candidate trigger actions, wherein the driving scenario includes at least one of highway, city, and congestion; wherein the preset gear selector timing threshold parameter is updated based on the feature data of each recorded gear selector gesture using an adaptive learning mechanism, and the preset gear selector timing threshold parameter includes de-shaking duration, upper limit of double tap interval time, lower limit of double tap interval time, and long press threshold; the feature data includes the gesture type identifier of the gear selector gesture, gesture time feature, vehicle state information at the time of triggering, driving scenario label, and safety gating verification result.

[0030] Figure 2 The schematic diagram of the architecture and data flow provided for the embodiments of the present invention is as follows: Figure 2As shown, the key input signals of the system include: s(t) is the original level input of the lever (type 0 / 1, 1 when the lever is down, 0 when the lever is up, sampled by hardware); VehicleSpeed ​​is the vehicle speed input (unit km / h, from CAN, Controller Area Network, used for transmission of signals such as vehicle speed and sensor status); RoadType / MapCls is the road level / map classification input (categorical, covering highway / urban expressway / city / ramp); LaneAvail is the lane availability input (Boolean); UserOverride is the user-selected scenario input (categorical, highest priority). The key output signals of the system include: ACC_cmd / NOA_cmd is the event flag output that triggers the activation or deactivation of ACC or NOA; Hist(bucket) is the cached output of the features of the last N events (list type, stored in scenario buckets). The system obtains the raw level signal s(t) from the gear shift lever, which is then processed by the debouncing and windowing modules before being input to the gesture parser. The parser outputs gesture types such as Single, Double, Long-press, or Combo. The gesture parsing results then enter the scenario recognition module, which classifies the scenario into City, Highway, or Traffic Jam based on user manual overlay, map road level, or vehicle speed / start-stop conditions. The scenario information, along with the gesture information, enters the policy and safety gating module to perform action arbitration, legality verification, and downgrade rollback. Actions that pass verification are issued via the ADAS API to activate / deactivate ACC or NOA. Simultaneously, the data recording and self-learning module caches historical event features according to scenario buckets and learns and updates timing threshold parameters online based on MAD anomaly removal, EWMA smoothing update, and limiting and cooling mechanisms, forming a closed loop of perception, parsing, decision-making, execution, and learning.

[0031] Through the embodiments of the present invention, the operation of the column shifter enables and disables ADAS functions. At the same time, through self-learning and driving scenarios, driving safety is ensured while improving the accuracy of function triggering.

[0032] Based on the above embodiments, the de-jitter processing includes: detecting the level state of the original level signal within a de-jitter time window with the current time as the endpoint and the de-jitter duration as the width; in response to the original level signal remaining at a first level within the de-jitter time window, outputting a de-jitter signal at a first level; in response to the original level signal remaining at a second level within the de-jitter time window, outputting a de-jitter signal at a second level; and in response to a level jump in the original level signal within the de-jitter time window, maintaining the de-jitter signal from the previous moment.

[0033] In this embodiment, the original level is set. Debouncing output Defined as:

[0034] If s(t') is always 1 within [t-D_db,t], then let =1;

[0035] If s(t') is always 0 within [t-D_db,t], then let =0;

[0036] Otherwise, maintain the previous output state. .

[0037] Where D_db is the dejittering duration, initially 40 ms by default, and subsequently adjusted by self-learning (with upper and lower limits and single-step amplitude limiting).

[0038] In this embodiment, optionally, the first level is a high level state when the lever is operated, and the second level is a low level state when the lever is not operated.

[0039] Through the embodiments of the present invention Figure 5 This is a schematic diagram of the anti-shake effect provided in an embodiment of the present invention, such as... Figure 5 As shown, the debouncing process eliminates level glitches caused by mechanical contact jitter and electromagnetic interference of the lever through time window filtering, preventing transient jumps from being misjudged as valid gesture inputs; the window width, i.e. the debouncing duration, is an adaptive learning parameter that can be adaptively adjusted according to individual differences in operating rhythm.

[0040] Based on the above embodiments, the recognition of lever gestures includes: recognizing a short touch when the pressing duration is less than the upper threshold of a short touch; recognizing a double touch when the time interval between two short touches is between the upper limit and the lower limit of a double-tap interval; recognizing a long press when the pressing duration is greater than the long press threshold; and recognizing a combination operation of short touches in a second direction within a preset time interval after detecting a short touch in the first direction as a double tap.

[0041] In this embodiment, the press-release duration of a short touch ,like Then it is a short touch, if If you press and hold, then it's a long press.

[0042] in, The long press threshold (default 800 ms, can be adaptively learned). The upper limit of short-touch (which can be implemented with) (Form mutually exclusive intervals).

[0043] In this embodiment, the double-click interval satisfy: This will be considered a double-click. (Default) , Both can learn adaptively.

[0044] In this embodiment, the shift lever is mounted on the steering column and can be moved in two directions: upward (towards the steering wheel) and downward (away from the steering wheel). The lever automatically returns to its original position after being moved. The recognition of the lever gesture is based on these two directions of movement and their timing sequence. Specifically, a short touch refers to a single, quick release after a single movement; a double touch refers to two consecutive short touches in the same direction within a preset time interval; and a continuous tap refers to a short touch in one direction followed immediately by a short touch in the opposite direction, with different directions but a short interval between the two short touches. The system recognizes this sequence of movements—either downward followed by upward or upward followed by downward—as a complete combined gesture.

[0045] Through the embodiments of the present invention, multiple gesture types achieve gesture differentiation recognition through time and space dimensions (lever direction), enabling a single lever hardware to trigger multiple ADAS functions without the need for additional buttons.

[0046] Based on the above embodiments, a finite state machine is used to parse the state of the lever gesture and determine candidate trigger actions, including: In response to the detection of the first short touch in the idle state, a transition to a pending state is initiated; in response to the detection of a second short touch in the pending state, and the time interval between the second short touch and the first short touch being within the double-tap time window, it is determined whether the direction of the second short touch is the same as that of the first short touch. If they are the same, a transition to a double-tap confirmation state is initiated, and the candidate trigger action corresponding to the double-tap confirmation state is NOA function activation; if they are different, a transition to a double-tap confirmation state is initiated, and the candidate trigger action corresponding to the double-tap confirmation state is to temporarily suppress or restore the currently running ADAS function; in response to the absence of a second short touch within the double-tap time window in the pending state, a transition to a short-tap confirmation state is initiated, and the candidate trigger action corresponding to the short-tap confirmation state is ACC (Adaptive Cruise Control). The ADAS (Adaptive Cruise Control) function (maintaining a set speed and adjusting distance to the vehicle in front) can be activated or deactivated. In response to a press duration reaching a long-press threshold in any state, the system transitions to a long-press confirmation state. The candidate trigger actions for the long-press confirmation state are entering configuration mode or performing a soft reset. The double-click time window is a time interval consisting of a lower limit and an upper limit for the double-click interval. Temporary suppression refers to pausing the longitudinal and lateral control outputs of the currently running ADAS function while keeping the function suspended. Resumption refers to restoring the longitudinal and lateral control outputs from the suspended state.

[0047] In this embodiment, Figure 3 This is a schematic diagram of state machine transitions provided in an embodiment of the present invention, such as... Figure 3As shown, the states and transition paths during gesture parsing are illustrated using an ASCII state machine. The system initially operates in an idle state (IDLE); upon detecting the first short tap, the state transitions from IDLE to PENDING, remaining in this state while waiting for the second short tap; if within the double-tap time window (… If a second short press is detected, the system enters the double-click confirmation state (DOUBLE_OK), triggering subsequent security gating and NOA activation processes; if the press duration reaches the long press threshold T_lp directly from IDLE, the system will proceed as planned. If the second short press occurs, the device will directly enter the long press confirmation state (LONGPRESS); if the timeout occurs in the PENDING state or the second short press occurs outside the window, the device will also switch to the LONGPRESS state.

[0048] Through the embodiments of the present invention, the finite state machine structures the gesture parsing process into explicit states and transition paths, making the candidate trigger actions output by the finite state machine unambiguous.

[0049] Based on the above embodiments, the safety gating verification result of the candidate triggering action is determined according to the driving scenario to execute the candidate triggering action, including: in response to the current driving scenario being congested, NOA (Navigate On Autopilot, which combines navigation / map to perform more advanced longitudinal and lateral assistance on specific roads) function activation is not allowed; entering configuration mode or performing a soft reset is allowed only when the execution layer is idle and the speed is less than a first preset speed threshold; in response to the current driving scenario being urban, NOA function activation is allowed only when the speed is greater than a second preset speed threshold; entering configuration mode or performing a soft reset is allowed only when the execution layer is idle and the speed is less than a first preset speed threshold; in response to the current driving scenario being highway, NOA function activation is allowed only when the speed is greater than a third preset speed threshold; entering configuration mode or performing a soft reset is allowed only when the execution layer is idle and the speed is less than a first preset speed threshold. The system enters configuration mode or performs a soft reset. If the candidate trigger action to be executed is NOA (Noise, Arrival, and Assist) activation, the system checks the functional mutual exclusion relationship between NOA and ACC (Adaptive Cruise Control) functions, allowing NOA to take over control of ACC. If a strong driver intent is detected, the system suppresses the candidate trigger action, which includes steering wheel torque, turn signal status, and pedal input signals. If any of the following checks fails: sensor self-test result, CAN communication status, or map validity, the candidate trigger action to be executed is rolled back to a lower-level ADAS function, and a prompt message is output. The ADAS function levels, from highest to lowest, are NOA and ACC. Rollback means canceling the currently pending candidate trigger action and instead executing the activation command of a lower-level ADAS function. The timing diagram for double-clicking to trigger NOA is shown below.Figure 4 As shown.

[0050] In this embodiment, the triggering strategies for each driving scenario are as follows: In the Highway scenario, a short touch is allowed, provided the vehicle speed is not less than 30 km / h; a double touch is allowed, provided the vehicle speed is not less than 50 km / h, the navigation path exists, the sensors and map are healthy, and there is no strong driver intent coverage. The double-tap window is slightly widened to improve response speed. If rejected, ACC is reverted and the reason is displayed; a long press is only allowed when the vehicle speed is less than 5 km / h and the execution layer is idle; a double-tap is allowed, used to follow or follow related switches. In the City scenario, a short touch is allowed, provided the vehicle speed is not less than 10 km / h; a double-tap is restricted by default, only open on urban expressways and when the vehicle speed is not less than 40 km / h, and navigation must be available; a long press is the same as the Highway scenario; a double-tap is allowed; throttling and de-shaking are enhanced to reduce accidental touches caused by congestion or bumps, and ACC is reverted if rejected. In traffic jam scenarios, short touches are allowed (speed range 0~30km / h); double-tap is disabled by default (conservative strategy); long press is the same as in high-speed conditions; continuous taps are allowed and are often used in conjunction with follow-up suppression or recovery; the self-learning tendency is to tighten the double-tap window and increase the de-shake duration, prioritizing stability and comfort.

[0051] Furthermore, define the pass rate and rejection rate for the last N NOA attempts:

[0052]

[0053] When the RejectRate consistently exceeds the threshold (e.g., 0.3) and the rejection reasons are concentrated on "incompatible scenario or insufficient speed", the Double window can be gently tightened during self-learning to reduce accidental touches.

[0054] Through embodiments of this invention, safety gating performs scenario-based legality verification on candidate trigger actions according to driving scenarios. In congested scenarios, NOA activation is disabled to avoid accidental activation; in urban scenarios, NOA is enabled only on urban expressways when speeds meet standards; and in highway scenarios, NOA is enabled only when speed and navigation conditions are met. Thus, these three scenario-differentiated verifications prevent functions from being activated in inappropriate scenarios, reducing accidental activation rates and safety risks. Secondly, function mutual exclusion verification ensures that NOA takes over ACC control when activated, avoiding longitudinal and lateral control conflicts caused by parallel output from dual controllers. The driver's strong intent priority mechanism suppresses automatic triggering when active operations such as steering and braking conflict with ADAS intents, ensuring the driver always has the highest control priority. Finally, system health verification reverts advanced functions to lower-level functions when sensors, communication, or maps malfunction, achieving gradual degradation rather than functional interruption, maintaining basic driving assistance capabilities, and informing the driver of the current function degradation status through the HMI (Human-Machine Interface).

[0055] Based on the above embodiments, the method for determining the driving scenario includes: in response to the existence of a user-preset driving scenario or a map-preset driving scenario, determining the driving scenario according to the user-preset driving scenario or the map-preset driving scenario; in response to the absence of a user-preset driving scenario and a map-preset driving scenario, determining the driving scenario according to vehicle speed and start-stop conditions; wherein, determining the driving scenario according to vehicle speed and start-stop conditions includes: in response to the vehicle speed being greater than or equal to a fourth preset speed threshold, determining the driving scenario as a high-speed mode; in response to the vehicle speed being less than a fifth preset speed threshold and start-stop conditions being present, determining the driving scenario as a congestion mode; in response to not belonging to either the high-speed mode or the congestion mode, determining the driving scenario as an urban mode.

[0056] In this embodiment, the driving scenario is determined based on the user's manual input, the map road level (e.g., highway or urban expressway in highway mode), and the priority of vehicle speed conditions. For example, based on vehicle speed conditions, Highway: v greater than 50km / h; Traffic Jam: v less than 30km / h with stop-and-go traffic; City: other conditions. It should be noted that there is a lag when switching between modes; a 5-10 second lag is set when entering or exiting each mode to avoid jitter.

[0057] Through the embodiments of the present invention, a three-level priority determination mechanism is adopted for driving scenarios: user-manual preset priority is given to map road level priority, which is given to vehicle speed and start-stop condition heuristic, so as to achieve both accuracy and flexibility in scenario recognition; the heuristic determination of vehicle speed and start-stop condition covers scenarios without navigation data, so as to accurately determine the driving scenario.

[0058] Based on the above embodiments, the method for updating the preset lever timing threshold parameter includes: independently recording feature data of valid events under different driving scenarios to form feature data sets corresponding to each driving scenario; responding to the fact that the number of feature data in the feature data set is greater than the minimum sample size threshold, using the median absolute deviation method to detect outliers in the feature data set, removing feature data that meets the outlier determination criteria from the feature data set, and obtaining robust feature data after removing outliers; calculating robust statistics based on robust feature data, and updating the preset lever timing threshold parameter using an exponentially weighted moving average method, wherein the updated preset lever timing threshold parameter is a weighted combination of the original preset lever timing threshold parameter and the robust statistics; wherein a single-step change limit constraint is applied to the update of the preset lever timing threshold parameter, so that the absolute value of the parameter change before and after a single update does not exceed the preset maximum step size; a cooling cycle constraint is applied to the update of the preset lever timing threshold parameter, and parameter updates are performed at most once within a preset cooling time.

[0059] In this embodiment, the target parameter set includes It learns independently in each scenario bucket (City, Highway, Traffic Jam) to make the triggering stable and in line with the driver's habits.

[0060] The feature vector for the i-th valid event is ,in, The interval between two short touches; Whether to attempt a double-click (0 / 1); For long press percentage ( ); The vehicle speed at which the trigger is located (also used as a bucket-splitting aid); For contextual tags; For SUCCESS / REJECT (from security gating results or execution feedback).

[0061] For a set of statistics X, MAD (Median Absolute Deviation, used for robust outlier removal) is defined as: median(X) is the median of the dataset. Anomaly detection is performed on each data point in the set (k is usually 2~3): Values ​​whose difference from the median is greater than a preset multiple of the median difference are considered outliers.

[0062] Finally, perform EWMA (Exponential Weighted Moving Average, used for smooth parameter updates (self-learning)) updates, paying attention to amplitude limiting and cooling during the updates.

[0063] Specifically, with upper limit parameters For example:

[0064]

[0065] in, Robust statistics after outlier removal (e.g., quantiles 0.5 / 0.7); (Learning rate recommendations vary depending on the scenario; Highways are more conservative); Single-step variation limit: Cooling cycle: The unit is minutes, and updates can only be performed once per cooldown period. Activation threshold: Updates are only allowed if the number of samples per bucket exceeds the sample size threshold (default 10).

[0066] In addition, we conducted accidental / missed touch rate monitoring, and performed statistics on the last N times:

[0067]

[0068] When FPR / FNR exceeds the threshold and becomes stable, the learning module performs gradual adjustments based on the priority of "tightening the window / increasing debouncing / increasing the long-press threshold". Three consecutive REJECTs or FPR > 10%: learning is paused and reverts to the default value; snapshot: If verification fails, revert to default settings. Manual recovery: Press and hold for >3 seconds to enter the configuration page and restore factory settings with one click. The adaptive learning mechanism adopts a two-layer feedback architecture: The first layer is the personalization layer, which independently records the feature data of lever gestures in each driving scenario bucket. After removing anomalies using the median absolute deviation method, robust statistics are calculated. Then, the exponential weighted moving average method is used to update the timing parameters such as de-shaking duration, upper and lower limits of double-tap interval time, and long-press threshold, so that the recognition threshold fits the individual's gesture habits. The second layer is the bottom layer, which counts the false touch rate and missed touch rate of the last N trigger results. When the false touch rate or missed touch rate continuously exceeds the preset threshold, adaptive learning is controlled and intervened. The parameter update direction is adjusted in a priority sequence of tightening the recognition window, increasing the de-shaking duration, and increasing the long-press threshold. When multiple consecutive triggers are rejected by the safety gating or the false touch rate exceeds the circuit breaker threshold, EWMA learning is paused and reverted to factory default parameters. Learning resumes after the data returns to normal.

[0069] Through the embodiments of the present invention, scenario-specific bucket independent learning enables the time-series threshold parameters of each driving scenario to evolve independently, avoiding cross-scenario data mixing, accurately updating the parameters of each scenario, and making the trigger threshold more in line with user operating habits.

[0070] It should be noted that the calibrable parameters and their default values, limiting ranges, and recommended learning rates / step sizes are as follows: Dejitter duration D_db defaults to 40ms, limiting range [20, 100]ms, learning rate α=0.10, maximum single-step change δ_max=10ms; Double-click interval lower limit T_dc_min defaults to 120ms, limiting range [80, 200]ms, learning rate α=0.10, δ_max=20ms; Double-click interval upper limit T_dc_max defaults to 400ms, limiting range [280, 600]ms, learning rate α=0.10, maximum single-step change δ_max=10ms. Rate α=0.10, δ_max=20ms; Long press threshold T_lp defaults to 800ms, limit range [600,1500]ms; Learning rate α=0.05, δ_max=50ms; Hysteresis H defaults to 150ms, limit range [100,300]ms, fixed or low-speed learning; Sample window N defaults to 50, limit range [20,200], fixed; Minimum number of samples M defaults to 10, limit range [5,20], fixed; Cooling period τ_cool defaults to 10min, limit range [5,30]min, fixed.

[0071] Furthermore, the fault modes and handling are as follows: F01 is sticking or jamming of the lever. The detection logic is that the high level lasts for more than 2 seconds (e.g., 2 seconds) and the handling is to record a DTC (Diagnostic Trouble Code), roll back the parameters to the default value, and issue an HMI alarm. F02 is abnormal jitter. The detection logic is that the edge count λ_edge exceeds the threshold. The handling is to increase the de-jitter duration D_db and freeze double-click recognition. F03 is corrupted learning snapshot. The detection logic is that the CRC (Cyclic Redundancy Check) verification fails. The handling is to roll back the default parameters and freeze the learning. F04 is too high false touch rate. The detection logic is to detect FPR (False Positive)... If the false touch rate (R) is continuously higher than the threshold (e.g., 10%) for three consecutive evaluation cycles, the action is to pause learning, tighten the double-click interval window T_dc and the debouncing duration D_db; F05 indicates frequent failures of security gating. The detection logic is that the NOA rejection rate (RejectRate) exceeds the threshold and the rejection reasons are concentrated in scene or speed-related conditions. The action is to tighten the NOA opening conditions and provide calibration suggestions.

[0072] Based on the same inventive concept, this invention also provides a system for implementing ADAS functions via a gearshift lever, which can be used to achieve the aforementioned method for implementing ADAS functions via a gearshift lever. The system includes: a gesture recognition module for recognizing lever gestures based on the level signal of the de-shaking gearshift lever and preset lever timing threshold parameters. The lever gestures include at least one of short touch, double tap, long press, and continuous tap. An action determination module is used to analyze the lever gesture state using a finite state machine to determine candidate trigger actions, which are used to switch the ADAS function on and off. A verification execution module is used to determine the safety gating verification result of the candidate trigger actions based on the driving scenario, which includes at least one of highway, urban, and congested driving scenarios. The preset lever timing threshold parameters are updated based on the feature data of each recorded lever gesture using an adaptive learning mechanism. The preset lever timing threshold parameters include de-shaking duration, upper limit of double tap interval time, lower limit of double tap interval time, and long press threshold. The feature data includes the lever gesture type identifier, gesture time characteristics, vehicle state information at the time of triggering, driving scenario label, and safety gating verification result.

[0073] Based on the same inventive concept, embodiments of the present invention also provide an electronic device. Figure 6 This is a structural block diagram of an electronic device provided in an embodiment of the present invention. Figure 6As shown, an embodiment of the present invention provides an electronic device including: one or more processors 101, a memory 102, and one or more I / O interfaces 103. The memory 102 stores one or more programs, which, when executed by the one or more processors, cause the one or more processors to implement any of the methods described in the above embodiments for ADAS functions via a gear shift switch; the one or more I / O interfaces 103 are connected between the processor and the memory, configured to enable information interaction between the processor and the memory.

[0074] The processor 101 is a device with data processing capabilities, including but not limited to a central processing unit (CPU); the memory 102 is a device with data storage capabilities, including but not limited to random access memory (RAM, more specifically SDRAM, DDR, etc.), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), and flash memory (FLASH); the I / O interface (read / write interface) 103 is connected between the processor 101 and the memory 102, and can realize information interaction between the processor 101 and the memory 102, including but not limited to a data bus (Bus).

[0075] In some embodiments, the processor 101, memory 102, and I / O interface 103 are interconnected via bus 104, and thus connected to other components of the computing device.

[0076] In some embodiments, the one or more processors 101 include a field-programmable gate array.

[0077] This invention also provides a computer-readable medium. The computer-readable medium stores a computer program, which, when executed by a processor, implements the steps in any of the methods for ADAS functions via a shift switch as described in the above embodiments. The computer-readable storage medium can be volatile or non-volatile.

[0078] This invention also provides a computer program product, including computer-readable code, or a non-volatile computer-readable storage medium carrying computer-readable code. When the computer-readable code is run in the processor of an electronic device, the processor in the electronic device executes the above-described method for ADAS function via the gear shift switch.

[0079] Those skilled in the art will understand that all or some of the steps, systems, and apparatuses disclosed above, and their functional modules / units, can be implemented as software, firmware, hardware, or suitable combinations thereof. In hardware implementations, the division between functional modules / units mentioned above does not necessarily correspond to the division of physical components; for example, a physical component may have multiple functions, or a function or step may be performed collaboratively by several physical components. Some or all physical components may be implemented as software executed by a processor, such as a central processing unit, digital signal processor, or microprocessor, or as hardware, or as an integrated circuit, such as an application-specific integrated circuit (ASIC). Such software can be distributed on a computer-readable storage medium, which may include computer storage media (or non-transitory media) and communication media (or transient media).

[0080] As is known to those skilled in the art, the term computer storage medium includes volatile and non-volatile, removable and non-removable media implemented in any method or technology for storing information, such as computer-readable program instructions, data structures, program modules, or other data. Computer storage media includes, but is not limited to, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM), static random access memory (SRAM), flash memory or other memory technologies, portable compact disc read-only memory (CD-ROM), digital versatile disc (DVD) or other optical disc storage, magnetic cartridges, magnetic tape, disk storage or other magnetic storage devices, or any other medium that can be used to store desired information and is accessible to a computer. Furthermore, it is known to those skilled in the art that communication media typically contain computer-readable program instructions, data structures, program modules, or other data in modulated data signals such as carrier waves or other transmission mechanisms, and may include any information delivery medium.

[0081] The computer-readable program instructions described herein can be downloaded from computer-readable storage media to various computing / processing devices, or downloaded via a network, such as the Internet, local area network, wide area network, and / or wireless network, to an external computer or external storage device. The network may include copper transmission cables, fiber optic transmission, wireless transmission, routers, firewalls, switches, gateway computers, and / or edge servers. A network adapter card or network interface in each computing / processing device receives the computer-readable program instructions from the network and forwards them to the computer-readable storage media in the respective computing / processing device.

[0082] The computer program instructions used to perform the operations of this invention may be assembly instructions, instruction set architecture (ISA) instructions, machine instructions, machine-dependent instructions, microcode, firmware instructions, state setting data, or source code or object code written in any combination of one or more programming languages, including object-oriented programming languages ​​such as Smalltalk, C++, etc., and conventional procedural programming languages ​​such as the "C" language or similar programming languages. The computer-readable program instructions may be executed entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving a remote computer, the remote computer may be connected to the user's computer via any type of network—including a local area network (LAN) or a wide area network (WAN)—or may be connected to an external computer (e.g., via the Internet using an Internet service provider). In some embodiments, electronic circuitry, such as programmable logic circuitry, field-programmable gate arrays (FPGAs), or programmable logic arrays (PLAs), is personalized by utilizing state information from the computer-readable program instructions. This electronic circuitry can execute the computer-readable program instructions to implement various aspects of the invention.

[0083] The computer program product described herein can be implemented specifically through hardware, software, or a combination thereof. In one alternative embodiment, the computer program product is specifically embodied in a computer storage medium; in another alternative embodiment, the computer program product is specifically embodied in a software product, such as a software development kit (SDK), etc.

[0084] Various aspects of the present invention are described herein with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It should be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer-readable program instructions.

[0085] These computer-readable program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing apparatus to produce a machine such that, when executed by the processor of the computer or other programmable data processing apparatus, they create means for implementing the functions / actions specified in one or more blocks of the flowchart and / or block diagram. These computer-readable program instructions can also be stored in a computer-readable storage medium that causes a computer, programmable data processing apparatus, and / or other device to operate in a particular manner; thus, the computer-readable medium storing the instructions comprises an article of manufacture that includes instructions for implementing aspects of the functions / actions specified in one or more blocks of the flowchart and / or block diagram.

[0086] Computer-readable program instructions may also be loaded onto a computer, other programmable data processing apparatus, or other device to cause a series of operational steps to be performed on the computer, other programmable data processing apparatus, or other device to produce a computer-implemented process, thereby causing the instructions executed on the computer, other programmable data processing apparatus, or other device to perform the functions / actions specified in one or more boxes of a flowchart and / or block diagram.

[0087] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of the present invention. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of an instruction, which contains one or more executable instructions for implementing a specified logical function. In some alternative implementations, the functions marked in the blocks may occur in a different order than those shown in the drawings. For example, two consecutive blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, may be implemented using a dedicated hardware-based system that performs the specified function or action, or using a combination of dedicated hardware and computer instructions.

[0088] Example embodiments have been disclosed herein, and while specific terminology has been used, it is for illustrative purposes only and should be construed as such, and is not intended to be limiting. In some instances, it will be apparent to those skilled in the art that features, characteristics, and / or elements described in conjunction with particular embodiments may be used alone, or in combination with features, characteristics, and / or elements described in conjunction with other embodiments, unless otherwise expressly indicated. Therefore, those skilled in the art will understand that various changes in form and detail may be made without departing from the scope of the invention as set forth in the appended claims.

Claims

1. A method for using ADAS functionality via a shift switch, characterized in that, include: Based on the level signal of the de-shaking shift lever and the preset shift lever timing threshold parameters, the shift lever gesture is identified. The shift lever gesture includes at least one of the following: short touch, double tap, long press, and continuous tap. The state of the lever gesture is analyzed by a finite state machine to determine candidate trigger actions, which are used to turn the ADAS function on or off. The safety gating verification result of the candidate triggering action is determined based on the driving scenario to execute the candidate triggering action. The driving scenario includes at least one of highway, city, and traffic congestion. The preset lever timing threshold parameter is updated based on the feature data of each recorded lever gesture using an adaptive learning mechanism. The preset lever timing threshold parameter includes de-shaking duration, upper limit of double-click interval time, lower limit of double-click interval time, and long-press threshold. The feature data includes the gesture type identifier of the lever gesture, gesture time feature, vehicle status information at the time of triggering, driving scenario label, and safety gate verification result.

2. The method according to claim 1, wherein, The shake reduction process includes: Within a de-jitter time window that ends at the current time and has a de-jitter duration as its width, the level state of the original level signal is detected; If the original level signal remains at the first level within the de-jittering time window, then the output de-jittering signal is at the first level. If the original level signal remains at the second level within the de-jittering time window, then the output de-jittering signal is the second level; If the original level signal experiences a level transition within the dejittering time window, the dejittering signal from the previous moment is retained.

3. The method according to claim 1, wherein, The recognition of lever gestures includes: A short touch is identified when the duration of the press is less than the upper threshold of the short touch. If the time interval between two short touches is between the upper limit and the lower limit of the double-tap interval, it is recognized as a double-tap. If the duration of the press exceeds the long press threshold, it is identified as a long press; If a combination of short touches in the second direction occurs within a preset time interval after a short touch in the first direction, it is identified as a double tap.

4. The method according to claim 1, wherein, The step of using a finite state machine to analyze the state of the lever gesture and determine candidate trigger actions includes: In response to the detection of the first short touch in the idle state, the system transitions to the pending state; In response to the detection of a second short touch in the pending state, and the time interval between the second short touch and the first short touch being within the double-tap time window, it is determined whether the direction of the second short touch is the same as that of the first short touch. If they are the same, the process moves to the double-tap confirmation state, and the candidate trigger action corresponding to the double-tap confirmation state is NOA function activation; if they are different, the process moves to the continuous tap confirmation state, and the candidate trigger action corresponding to the continuous tap confirmation state is to temporarily suppress or restore the currently running ADAS function. If no second short touch is detected within the double-click time window in the pending state, the system transitions to the short touch confirmation state. The candidate trigger action corresponding to the short touch confirmation state is ACC function activation or deactivation. In response to the detection that the press duration reaches the long press threshold in any state, the system transitions to the long press confirmation state. The candidate trigger action corresponding to the long press confirmation state is to enter the configuration mode or to perform a soft reset.

5. The method according to claim 4, wherein, The step of determining the safety gate verification result of the candidate triggering action based on the driving scenario and executing the candidate triggering action includes: If the current driving scenario is congested, NOA function activation is not allowed; only when the execution layer is idle and the speed is less than the first preset speed threshold, entering configuration mode or performing a soft reset is allowed. In response to the current driving scenario being urban, NOA function activation is allowed only when the speed is greater than the second preset speed threshold; entering configuration mode or performing a soft reset is allowed only when the execution layer is idle and the speed is less than the first preset speed threshold. In response to the current driving scenario being high speed, NOA function activation is allowed only when the speed is greater than the third preset speed threshold; entering configuration mode or performing a soft reset is allowed only when the execution layer is idle and the speed is less than the first preset speed threshold. When the candidate trigger action to be executed is NOA function activation, the mutual exclusion relationship between NOA function and ACC function is checked, and NOA function takes over the control of ACC function. In response to the detection of a strong driver intent operation, the candidate triggering action is suppressed in response to the strong driver intent operation, the strong intent operation including steering wheel torque, turn signal status and pedal input signal; If any one of the following checks fails: sensor self-test result, CAN communication status, or map validity, the candidate trigger action to be executed will be rolled back to a lower-level ADAS function and a prompt message will be output. The ADAS functions, from highest to lowest, are NOA function and ACC function.

6. The method according to claim 1, wherein, The method for determining the driving scenario includes: In response to the existence of a user-preset driving scenario or a map-preset driving scenario, the driving scenario is determined according to the user-preset driving scenario or the map-preset driving scenario. If there is no user-preset driving scenario or map-preset driving scenario, the driving scenario is determined based on vehicle speed and start-stop conditions; The determination of driving scenarios based on vehicle speed and start-stop conditions includes: If the vehicle speed is greater than or equal to the fourth preset speed threshold, the driving scenario is determined to be high-speed mode. If the vehicle speed is less than the fifth preset speed threshold and there is a start-stop condition, the driving scenario is determined to be a congestion mode. If the driving scenario does not fall under highway mode or congestion mode, then the driving scenario is determined to be city mode.

7. The method according to claim 1, wherein, The method for updating the preset lever timing threshold parameter includes: The feature data of valid events are recorded independently under different driving scenarios to form a feature data set corresponding to each driving scenario. If the number of feature data in the feature data set is greater than the minimum sample size threshold, then the median absolute deviation method is used to detect outliers in the feature data set. Feature data that meets the outlier determination criteria are removed from the feature data set to obtain robust feature data after outlier removal. Based on the robust feature data, a robust statistic is calculated, and the preset lever time-series threshold parameter is updated using an exponentially weighted moving average method. The updated preset lever time-series threshold parameter is a weighted combination of the original preset lever time-series threshold parameter and the robust statistic. Specifically, a single-step change limit constraint is applied to the update of the preset lever timing threshold parameter, so that the absolute value of the parameter change before and after a single update does not exceed the preset maximum step size; a cooling cycle constraint is applied to the update of the preset lever timing threshold parameter, so that the parameter update is performed at most once within the preset cooling time.

8. A system for ADAS function via a shift switch, characterized in that, Capable of being used to implement the method of claim 1, comprising: The gesture recognition module is used to recognize the lever gesture based on the level signal of the de-shaking processed lever and the preset lever timing threshold parameter. The lever gesture includes at least one of short touch, double touch, long press and continuous tap. The action determination module is used to perform state analysis on the lever gesture using a finite state machine to determine candidate trigger actions, which are used to turn the ADAS function on or off. The verification execution module is used to determine the safety gating verification result of the candidate triggering action based on the driving scenario and execute the candidate triggering action. The driving scenario includes at least one of highway, city, and congestion. The preset lever timing threshold parameter is updated based on the feature data of each recorded lever gesture using an adaptive learning mechanism. The preset lever timing threshold parameter includes de-shaking duration, upper limit of double-click interval time, lower limit of double-click interval time, and long-press threshold. The feature data includes the gesture type identifier of the lever gesture, gesture time feature, vehicle status information at the time of triggering, driving scenario label, and safety gate verification result.

9. An electronic device, characterized in that, include: One or more processors; Memory, used to store one or more programs; When the one or more programs are executed by the one or more processors, the one or more processors implement the method as described in any one of claims 1 to 7.

10. A computer-readable medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the steps of the method as described in any one of claims 1 to 7.

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