Cruise function multiplexing scheduling method and system based on key behavior discrimination

Abstract

The application discloses a cruise function multiplexing scheduling method and system based on key behavior identification, and relates to the technical field of key behavior collection cruise control. The cruise function multiplexing scheduling method and system based on key behavior identification comprises the following steps: S1, collecting and preprocessing key state data and vehicle cruise control data, and constructing a standardized cruise state data set; S2, constructing a multi-source cruise state synchronous sampling reconstruction model, and generating a state parameter expression consistent with a trigger time; S3, analyzing cruise state continuity, and generating a cruise reset target speed; and S4, constructing a cruise multiplexing scheduling model, and outputting a cruise target sequence. The method solves the problem that, during a cruise parameter resetting process, due to CAN communication refresh cycle differences, state parameters cannot be collected and written at the same time at the key trigger time, reset parameters are misaligned in the time dimension, and speed mutation and state jump problems are caused during subsequent calling.

Description

Technical Field

[0001] This invention relates to the field of cruise control technology, specifically a cruise function multiplexing scheduling method and system based on key behavior discrimination. Background Technology

[0002] With the continuous development of intelligent driving assistance functions in the field of vehicle control, cruise control has been widely used in the driving control of various passenger vehicles to achieve stable vehicle speed maintenance and reduce driver load. In existing cruise control implementations, the activation, resumption, and reset of the cruise function are typically triggered by human-machine interaction devices such as steering wheel buttons and toggle switches. This is combined with real-time data collection and processing by the vehicle control unit on vehicle speed, target cruise speed, drive motor output status, braking status, and pedal intervention status. During cruise operation, relevant status parameters are generally broadcast periodically through the vehicle communication network and received and buffered by the control unit, forming a cruise operation data chain with time-series characteristics. This provides basic data support for subsequent cruise control strategy execution and state switching. To adapt to complex driving scenarios, some control strategies also introduce simple state-holding logic and reset mechanisms. After button triggering, historical cruise parameters are directly called to restore the target speed, thereby achieving rapid response and continuous operation of the cruise function.

[0003] For example, invention patent CN114488799B discloses a method for optimizing controller parameters of an adaptive cruise control system, belonging to the field of automation control technology. The purpose of this invention is to use the differential evolution algorithm to solve the problem of optimizing controller parameters for adaptive cruise control algorithms, enabling the controller to automatically adjust parameters to meet the performance requirements of different vehicles. The steps of this invention are: designing a speed-time model of the preceding vehicle in following scenarios at different speeds, determining the parameter value range of the weight matrix in the system, selecting different types of evaluation indicators according to different performance requirements, and iterating through the output to obtain the optimal parameters. This invention utilizes the unique advantages of the differential evolution algorithm in solving complex problems such as nonlinearity, large space, and global optimization to optimize the controller parameters of adaptive cruise control algorithms under different performance requirements, enabling the vehicle to better track the preceding vehicle while adapting to different driver habits, and ensuring the comfort of the vehicle's passengers.

[0004] For example, the invention patent CN113589690B discloses a robust fault-tolerant method and device for cooperative adaptive cruise control. It constructs a mathematical model of a vehicle system with nonlinear system parameter perturbations, multi-source unknown external interference, radar performance defects, and execution control performance defects. It designs a CACC-SOTIF robust non-fragile dynamic output feedback H∞ active fault-tolerant controller, constructing an augmented closed-loop system with interference estimation errors, radar performance defect estimation errors, execution control performance defect estimation errors, and unknown interference. It solves for the gain of the CACC-SOTIF robust non-fragile dynamic output feedback H∞ active fault-tolerant controller, making the augmented closed-loop system with interference estimation errors, radar performance defect estimation errors, execution control performance defect estimation errors, and unknown interference asymptotically stable and possessing H∞ performance. This method can address time-varying perturbations of vehicle system parameters, controller parameter perturbations, and multi-source external interference while ensuring the expected functional safety of cooperative adaptive cruise control.

[0005] However, existing cruise control methods largely rely on direct parameter invocation and simple overriding, failing to uniformly constrain the temporal consistency of various state parameters during button triggering. Due to differences in refresh cycles among signal channels during vehicle communication, vehicle speed, cruise target speed, and related state parameters are collected and written to the reset parameter area at different time slices, resulting in a misalignment of the parameter set in the time dimension. In this situation, when a cruise reset operation is performed, the invoked parameter combination cannot accurately reflect the vehicle's operating state at the same moment, easily leading to sudden changes in the cruise target, discontinuous drive response, and unstable state transitions. Furthermore, existing methods lack the ability to analyze the relationship between button triggering behavior and cruise state evolution, failing to uniformly model state changes before and after the trigger moment, making it difficult to achieve smooth transitions and dynamic scheduling in scenarios with multiple triggers or continuous operations.

[0006] To address the above issues, there is an urgent need for a cruise function reuse scheduling method and system based on button behavior judgment. Summary of the Invention

[0007] Technical problems to be solved

[0008] To address the shortcomings of existing technologies, this invention provides a cruise function multiplexing scheduling method and system based on key behavior discrimination. This solves the problem that during the cruise parameter reset process, due to the difference in CAN communication refresh cycles, the various state parameters cannot be collected and written in a consistent time at the moment of key triggering, resulting in misalignment of reset parameters in the time dimension and causing sudden speed changes and state jumps during subsequent calls.

[0009] Technical solution

[0010] To achieve the above objectives, the present invention provides the following technical solution: a cruise function reuse scheduling method and system based on button behavior discrimination, comprising: S1, collecting button status data and vehicle cruise control data during vehicle cruise control, preprocessing the button status data and vehicle cruise control data to construct a standardized cruise status dataset; S2, based on the standardized cruise status dataset, constructing a multi-source cruise status synchronous sampling and reconstruction model, performing time alignment and unified reconstruction of vehicle cruise control data around the button trigger time, and generating a state parameter expression consistent with the trigger time; S3, using the state parameter expression consistent with the trigger time as input, performing continuity analysis on the cruise status continuity based on the offset relationship between the trigger vehicle speed and historical cruise stored parameters, and generating a cruise reset target speed for smooth adjustment based on the continuity analysis results; S4, constructing a cruise reuse scheduling model in conjunction with the cruise reset target speed, performing segmented generation and dynamic reconstruction of the cruise target update process under behavior triggering, and outputting a cruise target sequence with dynamic response capability.

[0011] Further, the specific steps for collecting button status data and vehicle cruise control data during the vehicle's cruise control process are as follows: Collecting button status data includes continuously sampling the level change sequence of the cruise cancel button to obtain the button level status sequence and the corresponding sampling time sequence, which are then aligned and stored in chronological order. Simultaneously, edge detection is performed on the level changes during sampling. When the button level changes from a non-triggered state to a triggered state, it is recorded as the press start sequence; when the button level changes from a triggered state to a non-triggered state, it is recorded as the release time sequence. A trigger segment sequence is formed by the one-to-one correspondence between the press start sequence and the release time sequence. Each trigger segment is determined by a pair of press start and release times. Collecting vehicle cruise control data includes the vehicle's actual driving speed, cruise target speed, drive motor output status, historical cruise storage parameters, cruise enable status, first entry flag, reset entry flag, braking status, and accelerator pedal intervention status. The data is then sorted and stored according to the received time marker.

[0012] Further, the specific steps for preprocessing the button status data and vehicle cruise control data to construct a standardized cruise status dataset are as follows: The button level state sequence and sampling time sequence are reconstructed using a unified time axis; the trigger segment sequence is divided with the press start sequence and release time sequence as boundaries, and the number of trigger segments is counted; the interval between any pair of adjacent press start and release time is defined as a single trigger segment; the time interval between adjacent trigger segments from the release time to the next press start is defined as the trigger interval sequence; the segment duration sequence is calculated using the time difference between the press start sequence and the release time sequence; the continuous trigger interval sequence is calculated using the time difference between the release time sequence and the next press start sequence; the segment duration sequences are summed, and the maximum segment duration is extracted; the ratio of the maximum segment duration to the sum of the segment duration sequences is calculated to obtain the single segment duration ratio; the difference between adjacent trigger intervals in the continuous trigger interval sequence is calculated sequentially to obtain the interval difference between each pair of adjacent trigger intervals; and the difference is calculated based on the median and absolute deviation of all interval differences. The statistical results are used to construct a baseline for interval fluctuation. When the absolute value of the interval difference between adjacent trigger intervals is less than the baseline for interval fluctuation, the two trigger segments corresponding to the trigger interval are grouped into the same trigger segment group according to the time sequence. When the absolute value of the interval difference between adjacent trigger intervals is greater than the baseline for interval fluctuation, the trigger segments are divided into segments based on the position between the trigger intervals as the grouping boundary, forming a continuous trigger segment group. Based on the number of trigger segments, the proportion of single segment duration, and the sequence of adjacent trigger interval differences, a behavioral intent identifier is generated by combining and mapping. A behavioral chain number is generated according to the temporal continuity relationship of the continuous trigger segment group. The maximum value in the segment duration length sequence is output as the main segment duration length. The continuous trigger interval sequence and the sequence of adjacent trigger interval differences are combined and output as a continuous trigger interval expression. The trigger time anchor point is calculated based on the time center position of the press start sequence, release time sequence, and segment duration length sequence. The button status data and vehicle cruise control data that have completed the standardization process are normalized using the minimum and maximum value normalization algorithm to construct a standardized cruise status dataset.

[0013] Furthermore, the specific steps for constructing a multi-source cruise state synchronous sampling and reconstruction model based on a standardized cruise state dataset, and for time alignment and unified reconstruction of vehicle cruise control data around the button trigger moment, are as follows: Constructing a multi-source cruise state synchronous sampling and reconstruction model; establishing symmetrical sampling windows around the trigger time anchor point; extracting time segments falling within the sampling window range from the vehicle's actual driving speed, cruise target speed, drive motor output state, historical cruise stored parameters, cruise enable state, first entry flag, reset entry flag, braking state, and accelerator pedal intervention state to form candidate sample sequences for the corresponding input quantities; after the candidate sample sequences are formed, nearest neighbor search is used. The algorithm calculates the time distance between each candidate sample time and the trigger time anchor point, extracts the candidate sample with the smallest time distance from each candidate sample sequence as the anchor point matching sample, and retains the adjacent candidate samples before and after the anchor point matching sample as time correction references; the actual vehicle speed and the current cruise target speed are reconstructed using a linear interpolation algorithm to reconstruct the value corresponding to the trigger time anchor point; the drive motor output state, cruise enable state, first entry flag, reset entry flag, braking state and accelerator pedal intervention state are filled back into the state corresponding to the trigger time anchor point using a zero-order hold method; and the historical cruise storage parameters are extracted using a nearest neighbor hold method to extract the most recent written value before the trigger time anchor point.

[0014] Further, the specific steps for generating state parameter expressions consistent with the trigger time are as follows: after completing time alignment, the reconstructed actual vehicle speed is output as the trigger speed, the reconstructed cruise target speed is output as the trigger target cruise speed, the reconstructed drive motor output state is output as the trigger drive motor output state, the reconstructed historical cruise storage parameters are output as the trigger historical cruise storage parameters, the reconstructed cruise enable state is output as the trigger cruise enable state, the reconstructed first entry flag is output as the trigger first entry flag, the reconstructed reset entry flag is output as the trigger reset entry flag, the reconstructed braking state is output as the trigger braking state, and the reconstructed accelerator pedal intervention state is output as the trigger accelerator pedal intervention state. The time difference between the sampling time of each input quantity and the trigger time anchor point is calculated as the time distance, and the numerical difference between the reconstructed value and the original sampled value is calculated as the interpolation correction amount. The time distance of each input quantity and the interpolation correction amount are combined according to the corresponding relationship to form a synchronous residual sequence of a multi-dimensional vector structure.

[0015] Furthermore, the specific steps for analyzing the continuity of cruise state based on the offset relationship between the trigger vehicle speed and the historical cruise storage parameters, using the state parameter expression consistent with the trigger time as input, are as follows: subtract the trigger vehicle speed from the target cruise speed at the trigger time to obtain the target offset; subtract the trigger vehicle speed from the historical cruise storage parameters to obtain the historical offset; multiply the target offset by the historical offset to obtain the directional coupling amount; take the absolute value of the target offset to obtain the target amplitude; take the absolute value of the historical offset to obtain the historical amplitude; add the target amplitude and the historical amplitude to obtain the normalization constraint amount; divide the directional coupling amount by the normalization constraint amount to obtain the offset adjustment amount; when the normalization constraint amount is zero, replace the normalization constraint amount with the minimum positive value; add the trigger vehicle speed and the offset adjustment amount to obtain the cruise reset target speed.

[0016] Furthermore, the specific steps for generating the cruise reset target speed for smooth adjustment based on the continuity analysis results are as follows: the fixed time step used for control execution is defined as the control cycle, which is determined by the vehicle longitudinal control task scheduling cycle. After the cruise reset target speed is calculated, the difference between the current actual vehicle speed and the cruise reset target speed is calculated to obtain the speed offset. The speed offset is divided into M sub-offset sequences within the continuous control cycle. In each control cycle, the cruise target speed is updated to the superposition result of the cruise target speed and the corresponding sub-offset in the previous control cycle. The updated cruise target speed is written into the cruise target register area inside the VCU and simultaneously sent to the longitudinal control execution unit to adjust the drive torque output.

[0017] Furthermore, the specific steps for constructing a cruise reuse scheduling model based on the cruise reset target speed, and for segmenting and dynamically reconstructing the cruise target update process under behavior triggering, are as follows: A cruise reuse scheduling model is constructed. At the behavior mapping layer, the model performs a structured mapping of behavior intent identifiers, establishing a correspondence between behavior triggering and cruise scheduling using behavior chain numbers and trigger time anchors as indexes, and forming a behavior-driven scheduling identifier sequence in chronological order. At the sequence construction layer, a cruise target change expression is constructed based on the speed offset. The synchronization residual sequence is mapped to adjustment weights in the cruise target change expression according to the input quantity correspondence. Within each control cycle, the sub-offset sequence is split and constrained based on the adjustment weights, transforming the cruise target change expression into a cruise target sequence unfolded according to the control cycle, ensuring that the cruise target sequence maintains consistency with the trigger time anchor in the time dimension.

[0018] Further, the specific steps for outputting a cruise target sequence with dynamic response capability are as follows: At the consistency constraint layer, the cruise target sequence is constrained and adjusted based on the synchronization residual sequence. The time offset information of each input quantity in the synchronization residual sequence is normalized and mapped according to the relative proportion of the time offset to generate the adjustment weight of the cruise target sequence, ensuring that the change amplitude of the cruise target sequence between different time slices remains consistent with the time alignment accuracy, forming a cruise target change trajectory with temporal continuity. At the dynamic response layer, a sequence update triggering mechanism is constructed based on the changing trend of the difference between the vehicle's actual driving speed and the target cruise speed at the trigger. When the changing trend of the difference reverses, the reconstruction process of the cruise target sequence is triggered, and the current vehicle actual driving speed is used as the new starting condition, combined with the cruise reset target speed to regenerate the cruise target sequence. At the output layer, the cruise target sequence is output as the cruise control input, and scheduling identification information associated with the behavior chain number and trigger time anchor point is output, identifying the source of the current cruise target sequence and the corresponding behavior triggering relationship.

[0019] The second aspect of this invention provides a cruise function multiplexing scheduling system based on button behavior discrimination, comprising: a cruise state data construction module, used to collect button state data and vehicle cruise control data during vehicle cruise control, preprocess the button state data and vehicle cruise control data, and construct a standardized cruise state dataset; a cruise state synchronization reconstruction module, used to construct a multi-source cruise state synchronization sampling reconstruction model based on the standardized cruise state dataset, perform time alignment and unified reconstruction of vehicle cruise control data around the button trigger time, and generate a state parameter expression consistent with the trigger time; a cruise reset target generation module, used to take the state parameter expression consistent with the trigger time as input, perform continuity analysis on the cruise state continuity from the offset relationship between the trigger vehicle speed and historical cruise stored parameters, and generate a cruise reset target speed for smooth adjustment based on the continuity analysis results; and a cruise multiplexing scheduling module, used to construct a cruise multiplexing scheduling model in combination with the cruise reset target speed, perform segmented generation and dynamic reconstruction of the cruise target update process under behavior triggering, and output a cruise target sequence with dynamic response capability.

[0020] Beneficial effects

[0021] The present invention has the following beneficial effects:

[0022] (1) The cruise function reuse scheduling method and system based on key behavior discrimination realizes the stable expression of key behavior temporal structure and completes the structured mapping of behavior intention by constructing a trigger segment division and continuous trigger interval parsing mechanism.

[0023] (2) The cruise function multiplexing scheduling method and system based on key behavior discrimination establishes a multi-source state synchronous reconstruction process around the trigger time anchor point, so that cruise control data under different refresh cycles can be aligned and consistently expressed on a unified time reference.

[0024] (3) The cruise function multiplexing scheduling method and system based on key behavior discrimination, by performing coupled constraint calculation on the offset relationship between the triggered vehicle speed and the historical cruise storage parameters, forms a cruise reset adjustment result with directional consistency and amplitude constraint characteristics.

[0025] (4) The cruise function multiplexing scheduling method and system based on key behavior discrimination, by constructing a dynamic reconstruction mechanism based on the trend of difference change, adjusts the cruise target sequence in real time when the state change direction changes, thereby maintaining the stability and continuity of the control process.

[0026] Of course, any product implementing this invention does not necessarily need to achieve all of the advantages described above at the same time. Attached Figure Description

[0027] Figure 1 This is a flowchart of the cruise function multiplexing scheduling method based on key behavior discrimination according to the present invention;

[0028] Figure 2 This is a structural diagram of the cruise function multiplexing and scheduling system based on key behavior discrimination according to the present invention;

[0029] Figure 3 This is a comparison chart of the cruise reset target speeds involved in this invention;

[0030] Figure 4 This is the time consistency constraint adjustment mapping diagram involved in the present invention. Detailed Implementation

[0031] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0032] Please see Figures 1-4 This invention provides a technical solution: a cruise function multiplexing scheduling method and system based on key press behavior judgment, such as... Figure 1The flowchart shown is a cruise function reuse scheduling method based on button behavior discrimination provided in this application example, including: S1, collecting button status data and vehicle cruise control data during vehicle cruise control, preprocessing the button status data and vehicle cruise control data to construct a standardized cruise status dataset; S2, based on the standardized cruise status dataset, constructing a multi-source cruise status synchronous sampling and reconstruction model, performing time alignment and unified reconstruction of vehicle cruise control data around the button trigger time, generating a state parameter expression consistent with the trigger time; S3, using the state parameter expression consistent with the trigger time as input, performing continuity analysis on the cruise status continuity from the offset relationship between the trigger vehicle speed and historical cruise stored parameters, and generating a cruise reset target speed for smooth adjustment based on the continuity analysis results; S4, combining the cruise reset target speed to construct a cruise reuse scheduling model, performing segmented generation and dynamic reconstruction of the cruise target update process under behavior triggering, and outputting a cruise target sequence with dynamic response capability.

[0033] Specifically, the steps for collecting button status data and vehicle cruise control data during vehicle cruise control are as follows: Button status data is collected by continuously sampling the level change sequence through the cruise cancel button switch signal interface in the steering wheel button input circuit. The sampling period is limited to 5ms to 20ms to ensure effective capture of short-term pressing behavior. Simultaneously, a debouncing process with a time threshold of 20ms to 50ms is introduced during sampling to filter out high-frequency level jitter occurring within this time threshold, resulting in a stable button level status sequence and corresponding sampling time sequence, which are then aligned and stored in chronological order. Edge detection is performed on level changes during sampling. When the button level changes from a non-triggered state to a triggered state, it is recorded as the press start sequence; when the button level changes from a triggered state to a non-triggered state, it is recorded as the release time sequence. A trigger segment sequence is formed by a one-to-one correspondence between the press start sequence and the release time sequence. Each trigger segment is determined by a pair of press start and release times. Each sampling time is marked with a timestamp generated by a unified clock source within the vehicle controller to ensure consistency in subsequent time alignment.

[0034] The system collects vehicle cruise control data, reading timestamped data of each control signal through the vehicle control unit's data cache interface. The actual vehicle speed is derived from wheel speed sensors or vehicle speed fusion estimation results; the cruise target speed is derived from the cruise control function's internal setting register; the drive motor output status is derived from the drive motor control unit's torque request or output feedback signal; historical cruise storage parameters are derived from the cruise function's historical write cache; the cruise enable status, first entry flag, and reset entry flag are derived from the cruise control logic status register; the braking status is derived from the brake pedal switch signal; and the accelerator pedal intervention status is derived from the accelerator pedal position sensor signal. Each signal is updated according to its original refresh cycle, with the vehicle speed signal refresh cycle ranging from 10ms to 20ms, the drive motor output status refresh cycle ranging from 10ms to 50ms, and the cruise control-related status signal refresh cycle ranging from 20ms to 100ms. All data is uniformly marked and sorted using the controller's internal timestamp as the time base to ensure time consistency of multi-source data in subsequent processing.

[0035] In this implementation scheme, the level changes of the cruise cancel button are continuously sampled and debouncing with time constraints. Combined with edge detection, a stable trigger segment sequence is formed, and multi-source cruise control data with a unified timestamp is acquired simultaneously. This constructs an input data foundation with a clear time reference and traceable data source, providing reproducible data support for subsequent time alignment reconstruction and continuity analysis.

[0036] Specifically, the preprocessing of button status data and vehicle cruise control data to construct a standardized cruise status dataset involves the following steps: reconstructing the button level state sequence and sampling time sequence using a unified time axis; dividing the trigger segment sequence with the press start sequence and release time sequence as boundaries and counting the number of trigger segments; defining the interval between any pair of adjacent press start and release time as a single trigger segment; and defining the time interval between adjacent trigger segments from the release time to the next press start as a trigger interval sequence.

[0037] The segment duration sequence is calculated by the time difference between the press start sequence and the release time sequence. The continuous trigger interval sequence is calculated by the time difference between the release time sequence and the next press start sequence. The segment duration sequence and the continuous trigger interval sequence are dimensionless to provide a unified comparison benchmark for time data under different driving cycles and sampling scales. The segment duration sequences are summed and the maximum segment duration is extracted. The ratio of the maximum segment duration to the sum of the segment duration sequences is calculated to obtain the single segment duration ratio. The single segment duration ratio is used to characterize the dominance of a single press in the overall triggering process. When the ratio is in the range of 0.6 to 0.85, it represents a long press. When the ratio is in the range of 0.3 to 0.6, it represents multiple consecutive presses. When the ratio is less than 0.3, it represents a discrete short press.

[0038] Adjacent difference operation is performed on the continuous trigger interval sequence to obtain the adjacent trigger interval difference sequence. The adjacent trigger interval difference sequence is statistically processed within the sliding time window. The interval fluctuation benchmark is constructed by combining the median and the absolute deviation. The interval fluctuation benchmark is used to characterize the stable range of interval fluctuation formed by the driver during continuous operation. Its value is adaptively updated with the driver's operating rhythm to reduce the impact of abnormal operation or noise jitter on the grouping results.

[0039] The difference between two adjacent trigger intervals in the continuous trigger interval sequence is calculated sequentially to obtain the interval difference between each pair of adjacent trigger intervals. When the absolute value of the interval difference between adjacent trigger intervals is less than the interval fluctuation benchmark, the two trigger segments corresponding to the trigger interval are merged into the same trigger segment group according to the time sequence. When the absolute value of the interval difference between adjacent trigger intervals is greater than the interval fluctuation benchmark, the trigger segments are divided into segments based on the position between the trigger intervals as the grouping boundary to form a continuous trigger segment group.

[0040] The behavior intent identifier is generated by combining and mapping the number of trigger segments, the proportion of single segment duration, and the difference sequence of adjacent trigger intervals. The behavior chain number is generated according to the temporal continuity of the continuous trigger segment group. The maximum value in the segment duration length sequence is output as the main segment duration length. The continuous trigger interval sequence and the difference sequence of adjacent trigger intervals are combined and output as the continuous trigger interval expression. The trigger time anchor point is calculated based on the time center position of the press start sequence, the release time sequence, and the segment duration length sequence.

[0041] The button status data and vehicle cruise control data that have undergone standardization are normalized using a minimum-maximum value normalization algorithm, which transforms each input quantity into a dimensionless expression, thereby constructing a standardized cruise status dataset.

[0042] In this implementation scheme, by performing structured decomposition and dimensionless normalization of key triggering behavior on a unified time axis, the continuous features of the segments and the fluctuation features of the trigger interval are extracted. Based on the stability of the interval, the trigger segments are grouped and the behavior chain is constructed, thereby forming a standardized input expression with time consistency and behavior distinguishability, providing a stable and quantifiable data foundation for subsequent time alignment reconstruction and scheduling decisions.

[0043] Specifically, based on a standardized cruise state dataset, a multi-source cruise state synchronous sampling and reconstruction model is constructed. The specific steps for time alignment and unified reconstruction of vehicle cruise control data around the button trigger moment are as follows: Construct a multi-source cruise state synchronous sampling and reconstruction model, establish a symmetrical sampling window around the trigger time anchor point, wherein the sampling window length is set to Tw, and it covers a time range of Tw / 2 both forward and backward with the trigger time anchor point as the center. The sampling step size is consistent with the original refresh cycle of each input quantity. Time segments falling within the sampling window range are extracted from the actual vehicle speed, cruise target speed, drive motor output state, historical cruise storage parameters, cruise enable state, first entry flag, reset entry flag, braking state, and accelerator pedal intervention state to form candidate sample sequences of the corresponding input quantities.

[0044] After the candidate sample sequence is formed, the nearest neighbor search algorithm is used to calculate the time distance between each candidate sample time and the trigger time anchor point. The candidate sample with the smallest time distance is extracted from each candidate sample sequence as the anchor point matching sample, and the adjacent candidate samples before and after the anchor point matching sample are retained as time correction references. The nearest neighbor search is used to ensure that the reconstruction result is closest to the trigger time anchor point in the time dimension, thereby reducing the deviation caused by time misalignment.

[0045] The actual vehicle speed and the current cruise target speed are reconstructed using a linear interpolation algorithm to reconstruct the values ​​corresponding to the trigger time anchor point. Since the actual vehicle speed and the cruise target speed are continuously changing quantities, linear interpolation can create a smooth transition between adjacent sampling points, avoiding abrupt changes caused by discrete sampling. The zero-order hold method is used to fill in the state corresponding to the trigger time anchor point for the drive motor output state, cruise enable state, first entry flag, reset entry flag, braking state, and accelerator pedal intervention state. Since each data is a discrete state quantity, its value remains unchanged within the sampling period. The zero-order hold method can maintain the consistency of the state logic. The most recent written value before the trigger time anchor point is extracted from the historical cruise storage parameters using the nearest neighbor hold method. The historical cruise storage parameters have event-driven update characteristics. Using the most recent written value can ensure the temporal validity of the parameter source.

[0046] After the reconstruction of multi-source data is completed, a synchronous residual sequence is formed based on the time distance and numerical correction of each input quantity participating in the reconstruction. The synchronous residual sequence is then used as a time consistency constraint in the subsequent cruise target generation process, so that the reconstruction results maintain coordination and consistency under a unified time reference among different input quantities.

[0047] In this implementation scheme, a time window with a clear length constraint is constructed around the trigger time anchor point to reconstruct multi-source cruise control data under a unified time reference. Differentiated reconstruction strategies are adopted for continuous changes, discrete state quantities, and historical stored parameters, thereby eliminating time misalignment of multi-source data while ensuring numerical continuity and state consistency. At the same time, a synchronous residual sequence reflecting time alignment error and reconstruction deviation is formed, providing a consistency constraint basis for the subsequent cruise target adjustment process.

[0048] Specifically, the steps for generating state parameter expressions consistent with the trigger time are as follows: After time alignment, the reconstructed actual vehicle speed is output as the trigger speed; the reconstructed cruise target speed is output as the trigger target cruise speed; the reconstructed drive motor output state is output as the trigger drive motor output state; the reconstructed historical cruise storage parameters are output as the trigger historical cruise storage parameters; the reconstructed cruise enable state is output as the trigger cruise enable state; the reconstructed first entry flag is output as the trigger first entry flag; the reconstructed reset entry flag is output as the trigger reset entry flag; the reconstructed braking state is output as the trigger braking state; and the reconstructed accelerator pedal intervention state is output as the trigger accelerator pedal intervention state. After all input quantities complete unified time reference mapping, the time difference between the original sampling time of each input quantity participating in reconstruction and the trigger time anchor point is calculated. The corresponding time distance is obtained, and the difference between the reconstructed value and the original sampled value used for reconstruction is calculated to obtain the interpolation correction. The time distance is used to characterize the offset of each input quantity on the time axis, and the interpolation correction is used to characterize the magnitude adjustment generated during the numerical reconstruction process. Subsequently, a one-to-one correspondence is established according to the input quantity dimension, and the time distance and interpolation correction of each input quantity are paired and combined to form a multi-dimensional vector structure composed of time offset information and numerical correction information. The vector structure is arranged according to the unified input quantity order to construct a synchronous residual sequence. During the construction process, the components of each dimension are subjected to dimensionless normalization to make the time distance and interpolation correction comparable on the same scale. The synchronous residual sequence is subjected to stability constraint processing through sliding window statistics to suppress local fluctuations caused by single abnormal sampling, thereby obtaining a unified residual expression for characterizing the temporal consistency and numerical consistency of multi-source data.

[0049] In this implementation scheme, by structured output of each input quantity under a unified time reference and pairing and fusing time offset information with numerical correction information, a synchronous residual sequence that can simultaneously reflect time alignment error and reconstruction amplitude deviation is constructed. This provides a quantifiable consistency constraint basis for the subsequent cruise target adjustment process, ensuring that multi-source data have a unified time scale and stable numerical basis when participating in scheduling calculations.

[0050] Specifically, using state parameters consistent with the trigger time as input, the continuity analysis of cruise state continuity is performed based on the offset relationship between the trigger vehicle speed and the historical cruise stored parameters. The specific steps are as follows:

[0051] The target amplitude is obtained by taking the absolute value of the target offset; the historical amplitude is obtained by taking the absolute value of the historical offset; the target amplitude and the historical amplitude are added together to obtain the normalization constraint. The absolute value of the target offset is used to extract the change intensity of the current cruise control intention relative to the trigger speed, and the absolute value of the historical offset is used to extract the change intensity of the historical cruise reference relative to the trigger speed. The two are added together to construct the amplitude normalization scale, so that the subsequent adjustment results are constrained by the overall change amplitude, thereby avoiding the adjustment imbalance caused by the excessive amplitude from a single source. Amplitude normalization reduces the impact of speed scale differences under different driving scenarios, and improves the stability and comparability of the results.

[0052] The offset adjustment is obtained by dividing the directional coupling amount by the normalization constraint amount. When the normalization constraint amount is zero, it is replaced with the minimum positive value. The directional coupling amount is obtained by multiplying the target offset amount by the historical offset amount, which is used to characterize the consistency of the change direction of the two. When the two are in the same direction, the directional coupling amount is positive, indicating that the current control intention is consistent with the historical benchmark, thus outputting positive adjustment and enhancing the recovery trend. When the two are in opposite directions, the directional coupling amount is negative, indicating that the current control intention conflicts with the historical benchmark. Thus, the adjustment amplitude is suppressed by the normalization constraint or even reverse adjustment is generated to avoid erroneous recovery caused by directly resetting to the historical value or the target value. By dividing by the normalization constraint amount, the offset adjustment amount is limited to a range consistent with the overall amplitude, thus forming a restricted continuous adjustment result. When the denominator is zero, a minimum positive value is introduced to ensure calculation stability.

[0053] The cruise reset target speed is obtained by adding the trigger vehicle speed and the offset adjustment amount. The trigger vehicle speed is used as a reference to superimpose the offset adjustment amount to ensure that the reset result is gradually adjusted from the current actual operating state, rather than directly jumping to the target cruise speed or historical cruise stored parameters. This ensures a continuous transition process in scenarios such as brake exit recovery, hill driving, recovery after accelerator pedal intervention, and short-press and long-press triggering. Compared with directly using historical cruise stored parameters for reset, this method can reduce the probability of speed overshoot and false reset. Compared with directly using the current cruise target speed for reset, this method can shorten the recovery time and reduce the acceleration peak, thus exhibiting higher smoothness and robustness in different driving scenarios.

[0054] For example, in a specific implementation scenario where cruise control resumes after braking disengagement, the vehicle's original cruise target speed was 100 km / h, and the historical cruise storage parameter was 98 km / h. After braking, the vehicle's actual speed decreased to 70 km / h, triggering the resumption operation. When directly restoring to the historical cruise storage parameter, the vehicle's target speed jumps instantaneously from 70 km / h to 98 km / h, resulting in a peak acceleration exceeding the set comfort threshold and causing a noticeable impact. When directly restoring to the current cruise target speed, the target speed jumps from 70 km / h to 100 km / h, further increasing the peak acceleration and posing a risk of overshoot. Using this method, the target offset is 30, and the historical offset... With a value of 28 and a positive directional coupling, a limited offset adjustment is obtained after normalization constraints. The cruise reset target speed is controlled within the range of 80km / h to 85km / h, and gradually approaches the target speed in subsequent control cycles, making the speed recovery process show a continuous increasing trend. In this scenario, the peak acceleration is reduced by more than 30%, the recovery time remains within an acceptable range, and there is no situation of erroneous reset to the wrong speed. In the scenarios of hill driving and recovery after accelerator pedal intervention, when the target offset is inconsistent with the historical offset direction, the directional coupling is negative, and the offset adjustment is suppressed, thereby avoiding premature erroneous reset before the vehicle speed has stabilized and reducing the probability of erroneous reset.

[0055] ;

[0056] In the formula, It represents the trigger speed corresponding to the trigger time anchor point, which is used to characterize the actual operating state of the vehicle at the moment of user operation and is the benchmark quantity for constructing the continuity constraint of reset parameters; It represents the target cruise speed at the time of triggering corresponding to the trigger time anchor point. It is used to characterize the desired speed pointed to by the current cruise control command and is a reference quantity reflecting the current control trend direction. This represents the historical cruise storage parameters triggered by the most recent valid write. It is used to characterize the cruise reset baseline after the previous round of user confirmation and serves as a reference for maintaining cross-cycle control consistency.

[0057] In this implementation plan, the trigger speed of trigger segment A is set to 20, the target cruise speed at trigger time is set to 24, and the historical cruise storage parameter at trigger time is set to 22.

[0058] The trigger speed for trigger segment B is set to 26, the target cruise speed at trigger time is set to 23, and the historical cruise storage parameter at trigger time is set to 28.

[0059] The trigger speed of trigger segment C is set to 30, the target cruise speed at trigger time is set to 33, and the historical cruise storage parameter at trigger time is set to 35.

[0060] The trigger speed of trigger segment D is set to 28, the target cruise speed at trigger time is set to 31, and the historical cruise storage parameter at trigger time is set to 26.

[0061] The trigger speed for trigger segment E is set to 34, the target cruise speed at trigger time is set to 36, and the historical cruise storage parameter at trigger time is set to 33.

[0062] The trigger speed for trigger segment F is set to 22, the target cruise speed at trigger is set to 25, and the historical cruise storage parameter at trigger is set to 27. The cruise reset target speed for each trigger segment is calculated, as shown in Table 1, the cruise reset target speed data table.

[0063] Table 1 Cruise Reset Target Speed ​​Data Table

[0064] Trigger Fragment A 20 24 22 21.33 Trigger Fragment B 26 23 28 24.80 Trigger Fragment C 30 33 35 31.88 Trigger Fragment D 28 31 26 26.80 Trigger Fragment E 34 36 33 33.33 Trigger Fragment F 22 25 27 23.88

[0065] like Figure 3 As shown, this is a comparison chart of cruise reset target speeds provided in this application example. (See Table 1 and...) Figure 3As can be seen, the cruise reset target speed data table uses trigger segments as a sequence to synchronously record the relationship between the trigger vehicle speed, the target cruise speed at trigger, and the historical cruise storage parameters at trigger. Based on this, it provides the calculated result of the cruise reset target speed, which reflects the fusion result between multiple speed references at the moment of button trigger. From the numerical distribution, it can be seen that the input parameters in trigger segment E are generally in the higher range, and the corresponding cruise reset target speed reaches 33.33, which is the highest level in the entire sequence. This indicates that under the combined effect of high vehicle speed and high target cruise speed, the reset result tends to maintain the continuity of the current operating trend. The input parameters in trigger segment A are generally lower, and the corresponding cruise reset target speed is 21.33, which is the lowest level in the entire sequence. This indicates that the reset result is closer to the basic operating state in low-speed trigger scenarios, thus avoiding sudden changes. In trigger segment C, the historical cruise storage parameter reaches 35, the highest value among all segments, and the corresponding reset result of 31.88 clearly converges towards the historical benchmark, demonstrating the guiding effect of the historical reference on the reset result. In trigger segment D, the historical cruise storage parameter is 26, lower than the current target cruise speed of 31, and the corresponding reset result of 26.80 clearly converges towards the lower benchmark, indicating that the constraint of cross-cycle consistency during the reset process plays a role in suppressing the expansion of offset. From the cruise reset target speed comparison chart, it can be seen that the three basic curves represent the current operating state, the current control intention, and the historical reference benchmark, respectively. The cruise reset target speed curve always lies between the three and maintains a smooth change without sudden jumps or violent oscillations, demonstrating the continuity constraint characteristics of the reset process. Between trigger segments B and C, the three basic curves show a clear separation, and the corresponding reset curve exhibits a transitional convergence shape, indicating that the reset result plays a coordinating role when the differences between multiple references are large. At trigger segment E, the three curves are relatively concentrated, and the corresponding reset curve closely matches the three, indicating that when the state consistency is high, the reset process tends to directly continue the current control trend. Overall, the line graph reflects that the cruise reset target speed has an adaptive fusion capability under different triggering conditions. It can absorb information from the current operating status and control target, and avoid cross-cycle discontinuity through historical benchmark constraints, thereby achieving stable and continuous cruise target reconstruction.

[0066] Specifically, the steps for generating the cruise reset target speed for smooth adjustment based on the continuity analysis results are as follows: The fixed time step used for control execution is defined as the control cycle, which is determined by the vehicle longitudinal control task scheduling cycle and kept consistent with the overall vehicle control task cycle. After the cruise reset target speed is calculated, the difference between the current vehicle's actual speed and the cruise reset target speed is calculated to obtain the speed offset. The number of segments M is determined based on the proportional relationship between the absolute magnitude of the speed offset and the maximum allowable speed change gradient within the control cycle, where M is the smallest positive integer that satisfies the condition that the speed change within a single control cycle does not exceed the set change gradient constraint, thus ensuring the smoothness and executability of the adjustment process. The speed offset is divided into M segments of sub-offset sequence at equal intervals and applied segment by segment within consecutive control cycles. Within each control cycle, the cruise target speed is updated to the superposition result of the cruise target speed in the previous control cycle and the current sub-offset. Simultaneously, during the update process, the target speed change in adjacent control cycles is subject to continuity constraints to avoid abrupt changes.

[0067] The updated cruise target speed is written into the cruise target register area inside the VCU and simultaneously sent to the longitudinal control execution unit. Through coordinated adjustment of the drive torque output, the actual vehicle speed gradually converges along the updated cruise target speed trajectory, thereby completing the smooth transition control during the cruise reset process.

[0068] In this implementation scheme, the difference between the cruise reset target speed and the current vehicle speed is divided into segments according to the control cycle, and the number of segments M is determined according to the change gradient constraint. This allows the speed adjustment process to be applied smoothly in a progressive manner on a cycle-by-cycle basis. At the same time, the updated cruise target speed is written into the control register in real time and driven longitudinal execution, thereby ensuring that the speed change during the cruise reset process is continuous and controllable and avoiding sudden shocks.

[0069] Specifically, the cruise reuse scheduling model is constructed by combining the cruise reset target speed. The specific steps for segmented generation and dynamic reconstruction of the cruise target update process under behavior triggering are as follows: Constructing a cruise reuse scheduling model, the cruise reuse scheduling model performs structured mapping of behavior intention identifiers at the behavior mapping layer. First, taking continuous trigger segment groups as basic units, each trigger segment group is sorted according to the time sequence of the trigger time anchor points, and assigned an incremental number in sequence to form a behavior chain number. On this basis, the behavior chain number and the corresponding trigger time anchor point are combined and encoded to construct an index pair set, and the index pairs are arranged according to the time sequence to form a behavior-driven scheduling identifier sequence. The behavior-driven scheduling identifier sequence is used to uniquely identify the cruise scheduling process corresponding to each behavior trigger and maintain a consistent association relationship across cycles.

[0070] In the sequence construction layer, a cruise target change expression is constructed based on velocity offset. First, an initial change trajectory is established using velocity offset as the total adjustment quantity, and it is discretized and expanded according to the control cycle to form a sub-offset sequence. Then, the synchronization residual sequence is expanded according to the input dimension, and the time distance and interpolation correction amount corresponding to each input quantity are normalized. The contribution ratio of each input quantity to the adjustment process is calculated based on their relative magnitude, and this contribution ratio is mapped to the adjustment weight distribution in the sub-offset sequence, so that the sub-offsets in different control cycles are non-uniformly distributed according to the time alignment deviation and the numerical correction magnitude. In each control cycle, the sub-offsets are corrected according to the adjustment weight, so that the adjustment process corresponding to the input quantity with larger time offset is delayed or weakened, while the adjustment process corresponding to the input quantity with higher time alignment is executed first, thereby realizing the coupling constraint between the adjustment process and time consistency. Finally, the corrected sub-offset sequences are accumulated in the order of the control cycle to generate the cruise target sequence, so that the cruise target sequence is aligned with the trigger time anchor point in the time dimension and exhibits smooth transition characteristics in continuous control cycles.

[0071] In this implementation example, by establishing a sortable index relationship between behavior trigger information and time anchor points, and expanding the speed offset into an adjustment sequence constrained by synchronization residuals within the control period, the cruise target update process is transformed from uniform amplitude adjustment to distributed adjustment based on time consistency differences, thereby ensuring that the cruise target sequence meets both time alignment constraints and change continuity requirements during the generation process.

[0072] Specifically, the steps for outputting a cruise target sequence with dynamic response capability are as follows: At the consistency constraint layer, the cruise target sequence is constrained and adjusted based on the synchronization residual sequence. First, the time distance corresponding to each input quantity in the synchronization residual sequence is normalized, and the relative proportion of the time distance is used as the basis for allocating adjustment weights. This adjustment weight is applied to the sub-offset sequence expanded according to the control cycle, so that the speed change within each control cycle and the time alignment degree of the corresponding input quantity form a one-to-one correspondence. During this process, the proportion of sub-offsets within the corresponding control cycle is reduced for input quantities with larger time distances, and the proportion of sub-offsets within the corresponding control cycle is increased for input quantities with smaller time distances. This allows the cruise target sequence to prioritize responses to data sources with higher time alignment during generation, and suppresses data with large time misalignments. This ensures that the change amplitude of the cruise target sequence between different time slices remains consistent with the time alignment accuracy, forming a continuous and smooth cruise target change trajectory. Simultaneously, it avoids the instantaneous adjustment superposition phenomenon caused by time misalignment of multi-source data, thereby suppressing speed abrupt shocks.

[0073] In the dynamic response layer, a sequence update triggering mechanism is constructed based on the trend of the difference between the actual vehicle speed and the target cruise speed at the trigger. Specifically, the change in the sign and magnitude of the difference within consecutive control cycles is judged. When the difference shows a sign reversal within two consecutive control cycles and the reversal magnitude exceeds the sub-offset magnitude within the current control cycle, the state is judged as a reverse offset in the adjustment direction, thereby triggering the reconstruction process of the cruise target sequence. During the reconstruction process, the current actual vehicle speed is used as the new starting reference, and the cumulative adjustment results corresponding to the executed sub-offsets are retained. At the same time, the cruise reset target speed is used as the convergence endpoint to recalculate the remaining sub-offset sequence, so that the subsequent adjustment process is redistributed under the new directional constraints. This avoids over-adjustment caused by the continued execution of the original adjustment path and reduces the cumulative adjustment error under false triggering conditions.

[0074] The output layer outputs the cruise target sequence as the cruise control input and outputs scheduling identification information associated with the behavior chain number and trigger time anchor point. The scheduling identification information is used to mark the behavior trigger source and time position corresponding to the current cruise target sequence, so that the cruise control process has traceability and distinguishability, thereby ensuring the independence and consistency between scheduling processes when multiple behavior triggers occur in an overlapping manner.

[0075] like Figure 4The diagram shown is a time consistency constraint adjustment mapping diagram provided in this application example. Using a unified time axis as the horizontal reference and structured response as the vertical expression, it constructs a complete evolutionary process from time misalignment distribution to constraint mapping adjustment and then to dynamic reconstruction. This visually presents the inherent mechanism by which cruise parameters converge from an inconsistent state to a consistent control trajectory before and after button triggering. In the upper blue curve of the diagram, the continuously fluctuating waveform reflects the time misalignment distribution structure formed by multi-source cruise control data near the triggering time. Different peak regions correspond to the degree of offset of different signal channels at the sampling time. The left region is marked as the misalignment concentration area, indicating that multiple parameters exhibit significant asynchronous acquisition within this time period, resulting in a discrete and dispersed state expression in the time dimension. This part directly corresponds to the distribution pattern of the synchronization residual sequence on the time axis. In the middle region of the diagram, the transition from the blue curve to the orange curve reflects the time consistency constraint mapping process. The orange curve represents the adjustment trajectory generated after the introduction of the constraint mechanism. This trajectory no longer simply follows the original state change but forms a smooth and continuous evolutionary trend after the time offset information participates in the adjustment. The constraint mapping transition zone in the diagram corresponds to this transformation process. Essentially, it transforms the degree of time misalignment into adjustment intensity, causing different time slices to have differentiated effects on the adjustment results, thereby achieving the reconstruction from a discrete state to a continuous control trajectory. A light-colored band exists in the lower vertical position, labeled as the continuous adjustment maintenance band. This area represents the allowable range for maintaining stable output during adjustment. When the adjustment trajectory falls within this area, it indicates that the cruise target changes are continuous and without abrupt changes. This structure reflects the stability constraint capability of the adjustment result in the time dimension and is also the core basis for maintaining smoothness in subsequent control execution. In the right region, a vertical dashed line marks the trend reversal trigger reconstruction position. This position corresponds to the time point when the direction of difference change reverses during adjustment. When the trajectory reaches this position, the original adjustment path no longer meets the continuity requirement, thus triggering a new scheduling generation process. As shown in the diagram, after this trigger point, the orange curve re-fits the stable band region, labeled as "reconstructed and fitted to the stable band," demonstrating that the scheduling process has dynamic correction capabilities, allowing the output trajectory to return to the stable range after deviation. Furthermore, the vertical dashed lines in the diagram connecting corresponding points of the blue and orange curves represent the mapping relationship between temporal misalignment information and the adjustment result. These connections reveal the impact paths of different time slices on the adjustment trajectory, demonstrating that the adjustment process is not a uniform proportional correction, but rather a non-uniform response distribution based on the degree of time offset. Overall, the temporal consistency constraint adjustment mapping diagram fully expresses three key processes: first, the temporal misalignment distribution structure formed by multi-source data near the trigger moment; second, the temporal misalignment information being mapped into adjustment constraints and participating in trajectory generation; and third, dynamic reconstruction triggered by trend changes during the adjustment process, ultimately returning to the stable range, thereby achieving a smooth evolution of the cruise target on a continuous time axis.The time consistency constraint adjustment mapping diagram reveals the mechanism of time consistency constraints in the scheduling generation process from a structural perspective, which can directly support the technical features of synchronization residuals participating in adjustment and dynamic reconfiguration triggering in the relevant claims.

[0076] In this implementation scheme, by converting time offset information into adjustment weights to participate in the generation of cruise target sequence, and triggering reconstruction update based on current vehicle speed when the direction of difference change reverses, the cruise target adjustment process can adaptively allocate with time alignment accuracy and correct in time when the adjustment direction is abnormal. This avoids speed abrupt changes and false triggering accumulation caused by the superposition of misalignment of multi-source data, and ensures the continuity and stability of the cruise control process.

[0077] The second aspect of this invention provides a cruise function multiplexing and scheduling system based on button behavior discrimination, comprising: a cruise state data construction module, used to collect button state data and vehicle cruise control data during vehicle cruise control, wherein the button state data originates from the level signal of the steering wheel cruise function button input circuit, and continuous sampling is performed through a fixed sampling period, combined with debouncing processing and edge detection to extract the press start point and release time, thereby forming a trigger segment sequence; the vehicle cruise control data originates from the time-stamped data cache interface inside the vehicle control unit, including the vehicle's actual driving speed, cruise target speed, drive motor output status, historical cruise storage parameters, and various status flag signals, and after data acquisition, unified time axis reconstruction, segment division, interval feature extraction, and dimensionless normalization processing are performed on each input quantity to construct a standardized cruise state dataset with a unified time reference and scale consistency;

[0078] The cruise state synchronization reconstruction module is used to construct a multi-source cruise state synchronization sampling reconstruction model based on a standardized cruise state dataset. It establishes a sampling window with a clear length constraint around the button trigger time, extracts candidate sample sequences for each input quantity within the window, and determines the anchor reference sample by the minimum time distance matching. At the same time, it performs differentiated reconstruction by interpolation reconstruction, state preservation and recent value extraction for continuous change quantities, discrete state quantities and historical stored parameters, respectively, so that multi-source data are uniformly mapped to the same trigger time base. It further calculates time offset and numerical correction to form a synchronization residual sequence, thereby obtaining a state parameter expression consistent with the trigger time.

[0079] The cruise reset target generation module takes the state parameter expression consistent with the trigger time as input, calculates the offset relationship between the trigger vehicle speed, the target cruise speed at the trigger time, and the historical cruise stored parameters, constructs a joint expression of directional coupling and amplitude constraint, performs quantitative analysis of the cruise state continuity, and generates the cruise reset target speed based on the analysis results. At the same time, it combines the control cycle and changing gradient constraints to expand the speed offset in segments, so that the reset process can achieve a smooth transition in a cycle-by-cycle progressive manner.

[0080] The cruise reuse scheduling module is used to construct a cruise reuse scheduling model by combining the cruise reset target speed. It establishes an index relationship between behavior triggering and scheduling process through behavior chain number and trigger time anchor point, and constructs a cruise target change expression based on speed offset according to the control cycle. It transforms the synchronization residual sequence into adjustment weights to participate in sub-offset allocation, so that the cruise target update process and time alignment accuracy form a coupling constraint. At the same time, when the difference change direction is reversed, it triggers sequence reconstruction based on the current vehicle speed, thereby outputting a cruise target sequence with dynamic response capability and continuous and stable change.

[0081] In this implementation plan, the cruise status data construction module is responsible for the unified collection and structured processing of multi-source data related to button triggering behavior and cruise control. Through time axis reconstruction, trigger segment division and dimensionless normalization processing, it forms an input data foundation with a unified time reference and scale consistency, thereby ensuring the comparability and reproducibility of data in subsequent processing.

[0082] The role of the cruise state synchronization reconstruction module is to perform unified time alignment of multi-source data around the trigger time anchor point, and eliminate the time misalignment between various input quantities through a differentiated reconstruction strategy, so that data from different sources can form a consistent expression under the same time reference. At the same time, it generates synchronization residual information that reflects time offset and reconstruction deviation, providing a basis for consistency constraints for subsequent adjustment processes.

[0083] The cruise reset target generation module is used to perform quantitative analysis of cruise continuity based on the state parameter relationship at the trigger time, transform the offset relationship between the current operating state and the historical reference state into the target adjustment result, and transform the overall speed adjustment process into a smooth change on a cycle by using a segmented expansion method, thereby avoiding the risk of sudden changes during the reset process.

[0084] The cruise multiplexing scheduling module is responsible for establishing a connection between behavior trigger information and time anchor points, introducing time consistency constraints to perform distributed control of speed adjustment during the scheduling process, and performing dynamic reconstruction when the adjustment direction changes abnormally. This enables the cruise target update process to adaptively adjust with the actual operating state, thereby ensuring that the output trajectory has continuity, stability and response consistency.

[0085] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus.

[0086] The preferred embodiments of the present invention disclosed above are merely illustrative of the invention. These preferred embodiments do not exhaustively describe all details, nor do they limit the invention to the specific implementations described. Clearly, many modifications and variations can be made based on the content of this specification. This specification selects and specifically describes these embodiments to better explain the principles and practical applications of the invention, thereby enabling those skilled in the art to better understand and utilize the invention. The invention is limited only by the claims and their full scope and equivalents.

Claims

1. A cruise function multiplexing and scheduling method based on key press behavior judgment, characterized in that: include: S1: Collect button status data and vehicle cruise control data during the vehicle's cruise control process, preprocess the button status data and vehicle cruise control data, and construct a standardized cruise status dataset. S2, based on a standardized cruise state dataset, constructs a multi-source cruise state synchronous sampling and reconstruction model, performs time alignment and unified reconstruction of vehicle cruise control data around the button trigger time, and generates a state parameter expression consistent with the trigger time; S3 takes the state parameter expression consistent with the trigger time as input, performs a continuity analysis on the cruise state continuity from the offset relationship between the trigger vehicle speed and the historical cruise stored parameters, and generates a cruise reset target speed for smooth adjustment based on the continuity analysis results. The specific steps for analyzing the continuity of cruise status by taking the state parameters consistent with the trigger time as input and analyzing the offset relationship between the trigger vehicle speed and the historical cruise storage parameters are as follows: The target offset is obtained by subtracting the trigger vehicle speed from the target cruise speed at the time of triggering; the historical offset is obtained by subtracting the trigger vehicle speed from the historical cruise stored parameters; the directional coupling is obtained by multiplying the target offset and the historical offset. The target amplitude is obtained by taking the absolute value of the target offset; the historical amplitude is obtained by taking the absolute value of the historical offset; the normalization constraint is obtained by adding the target amplitude and the historical amplitude. Divide the directional coupling amount by the normalized constraint amount to obtain the offset adjustment amount. When the normalized constraint amount is zero, replace the normalized constraint amount with the minimum positive value. The cruise reset target speed is obtained by adding the trigger speed and the offset adjustment amount. S4 combines the cruise reset target speed to construct a cruise reuse scheduling model, performs segmented generation and dynamic reconstruction of the cruise target update process under behavior triggering, and outputs a cruise target sequence with dynamic response capability.

2. The cruise function multiplexing and scheduling method based on key behavior discrimination according to claim 1, characterized in that: The specific steps for collecting button status data and vehicle cruise control data during the vehicle's cruise control process are as follows: The system collects button status data, which includes continuous sampling of the level change sequence of the cruise cancel button to obtain the button level status sequence and the corresponding sampling time sequence. These are then aligned and stored in chronological order. Simultaneously, edge detection is performed on the level changes during the sampling process. When the button level changes from a non-triggered state to a triggered state, it is recorded as the press start sequence. When the button level changes from a triggered state to a non-triggered state, it is recorded as the release time sequence. The press start sequence and the release time sequence are matched one-to-one to form a trigger segment sequence. Each trigger segment is determined by a pair of press start and release times. The system collects vehicle cruise control data, including actual vehicle speed, cruise target speed, drive motor output status, historical cruise storage parameters, cruise enable status, first entry sign, reset entry sign, braking status, and accelerator pedal intervention status. The system also sorts and stores the data according to the time of receipt.

3. The cruise function multiplexing and scheduling method based on key behavior discrimination according to claim 1, characterized in that: The specific steps for preprocessing button status data and vehicle cruise control data to construct a standardized cruise status dataset are as follows: The button level state sequence and the sampling time sequence are reconstructed using a unified time axis. The trigger segment sequence is divided with the press start sequence and the release time sequence as the boundary and the number of trigger segments is counted. The interval between any pair of adjacent press start and release time is defined as a single trigger segment, and the time interval between adjacent trigger segments from the release time to the next press start is defined as the trigger interval sequence. The segment duration sequence is calculated by the time difference between the press start sequence and the release time sequence; the continuous trigger interval sequence is calculated by the time difference between the release time sequence and the next press start sequence; the segment duration sequences are summed and the maximum segment duration is extracted, and the ratio of the maximum segment duration to the sum of the segment duration sequences is calculated to obtain the single segment duration ratio; the difference between adjacent trigger intervals in the continuous trigger interval sequence is calculated sequentially to obtain the interval difference between each pair of adjacent trigger intervals, and an interval fluctuation benchmark is constructed based on the median and absolute deviation statistical results of all interval differences; when the absolute value of the interval difference between adjacent trigger intervals is less than the interval fluctuation benchmark, the two trigger segments corresponding to the trigger interval are merged into the same trigger segment group according to the time order; when the absolute value of the interval difference between adjacent trigger intervals is greater than the interval fluctuation benchmark, the trigger segments are divided into continuous trigger segment groups based on the position between the trigger intervals as the grouping boundary. The behavior intent identifier is generated by combining and mapping the number of trigger segments, the proportion of single segment duration, and the difference sequence of adjacent trigger intervals. The behavior chain number is generated according to the temporal continuity of the continuous trigger segment group. The maximum value in the segment duration length sequence is output as the main segment duration length. The continuous trigger interval sequence and the difference sequence of adjacent trigger intervals are combined and output as the continuous trigger interval expression. The trigger time anchor point is calculated based on the time center position of the press start sequence, the release time sequence, and the segment duration length sequence. The button status data and vehicle cruise control data that have undergone standardization are normalized using a minimum-maximum value normalization algorithm to construct a standardized cruise status dataset.

4. The cruise function multiplexing and scheduling method based on key behavior discrimination according to claim 1, characterized in that: The specific steps for constructing a multi-source cruise state synchronous sampling and reconstruction model based on a standardized cruise state dataset, and for performing time alignment and unified reconstruction of vehicle cruise control data around the button trigger moment, are as follows: A multi-source cruise state synchronous sampling and reconstruction model is constructed. A symmetrical sampling window is established around the trigger time anchor point. Time segments falling within the sampling window range are extracted from the actual vehicle speed, cruise target speed, drive motor output state, historical cruise stored parameters, cruise enable state, first entry flag, reset entry flag, braking state and accelerator pedal intervention state to form a candidate sample sequence of the corresponding input quantity. After the candidate sample sequence is formed, the nearest neighbor search algorithm is used to calculate the time distance between each candidate sample time and the trigger time anchor point. The candidate sample with the smallest time distance is extracted from each candidate sample sequence as the anchor point matching sample, and the adjacent candidate samples before and after the anchor point matching sample are retained as time correction references. The numerical values ​​corresponding to the trigger time anchor point are reconstructed using a linear interpolation algorithm between the actual vehicle speed and the current cruise target speed. The state corresponding to the trigger time anchor point is backfilled using a zero-order hold method for the drive motor output state, cruise enable state, first entry flag, reset entry flag, braking state, and accelerator pedal intervention state. The most recent written value before the trigger time anchor point is extracted from the historical cruise storage parameters using a nearest neighbor hold method.

5. The cruise function multiplexing and scheduling method based on key behavior discrimination according to claim 1, characterized in that: The specific steps for generating state parameters consistent with the triggering time are as follows: After time alignment is completed, the reconstructed actual vehicle speed is output as the trigger speed, the reconstructed cruise target speed is output as the trigger target cruise speed, the reconstructed drive motor output status is output as the trigger drive motor output status, the reconstructed historical cruise storage parameters are output as the trigger historical cruise storage parameters, the reconstructed cruise enable status is output as the trigger cruise enable status, the reconstructed first entry flag is output as the trigger first entry flag, the reconstructed reset entry flag is output as the trigger reset entry flag, the reconstructed braking status is output as the trigger braking status, and the reconstructed accelerator pedal intervention status is output as the trigger accelerator pedal intervention status. The time difference between the sampling time of each input quantity and the trigger time anchor point is calculated as the time distance, and the numerical difference between the reconstructed value and the original sampled value is calculated as the interpolation correction amount. The time distance of each input quantity and the interpolation correction amount are combined according to the corresponding relationship to form a synchronous residual sequence of a multi-dimensional vector structure.

6. The cruise function multiplexing and scheduling method based on key behavior discrimination according to claim 1, characterized in that: The specific steps for generating the cruise reset target speed for smooth adjustment based on the continuity analysis results are as follows: The fixed time step used for control execution is defined as the control cycle. The control cycle is determined by the vehicle longitudinal control task scheduling cycle. After the cruise reset target speed is calculated, the difference between the current actual vehicle speed and the cruise reset target speed is calculated to obtain the speed offset. The speed offset is divided into M sub-offset sequences in the continuous control cycle. In each control cycle, the cruise target speed is updated to the superposition result of the cruise target speed and the corresponding sub-offset in the previous control cycle. The updated cruise target speed is written into the cruise target register area inside the VCU and simultaneously sent to the longitudinal control execution unit to adjust the drive torque output.

7. The cruise function multiplexing and scheduling method based on key behavior discrimination according to claim 1, characterized in that: The specific steps for constructing a cruise reuse scheduling model by combining the cruise reset target speed, and performing segmented generation and dynamic reconstruction of the cruise target update process triggered by behavior, are as follows: A cruise reuse scheduling model is constructed. In the behavior mapping layer, the cruise reuse scheduling model performs structured mapping of behavior intention identifiers, establishes the correspondence between behavior triggering and cruise scheduling by using behavior chain number and trigger time anchor point as index, and forms a behavior-driven scheduling identifier sequence in time order. In the sequence construction layer, a cruise target change expression is constructed based on the velocity offset. The synchronization residual sequence is mapped to the adjustment weight in the cruise target change expression according to the correspondence of the input quantities. In each control cycle, the sub-offset sequence is split and constrained and corrected according to the adjustment weight. The cruise target change expression is transformed into a cruise target sequence unfolded according to the control cycle, so that the cruise target sequence is consistent with the trigger time anchor point in the time dimension.

8. The cruise function multiplexing and scheduling method based on key behavior discrimination according to claim 1, characterized in that: The specific steps for outputting a cruise target sequence with dynamic response capability are as follows: In the consistency constraint layer, the cruise target sequence is constrained and adjusted based on the synchronization residual sequence. The time offset information of each input quantity in the synchronization residual sequence is normalized and mapped according to the relative proportion of the time offset to generate the adjustment weight of the cruise target sequence. This ensures that the change amplitude of the cruise target sequence between different time slices is consistent with the time alignment accuracy, forming a cruise target change trajectory with time continuity. In the dynamic response layer, a sequence update triggering mechanism is constructed based on the trend of the difference between the actual vehicle speed and the target cruise speed at the time of triggering. When the trend of the difference changes in the opposite direction, the reconstruction process of the cruise target sequence is triggered, and the current actual vehicle speed is taken as the new starting condition, and the cruise target sequence is regenerated in combination with the cruise reset target speed. The output layer outputs the cruise target sequence as the cruise control input, and outputs scheduling identification information associated with the behavior chain number and trigger time anchor point to identify the source of the current cruise target sequence and the corresponding behavior triggering relationship.

9. A cruise function multiplexing scheduling system based on key press behavior discrimination, employing the cruise function multiplexing scheduling method based on key press behavior discrimination as described in any one of claims 1-8, characterized in that: include: The cruise status data construction module is used to collect button status data and vehicle cruise control data during the vehicle's cruise control process, preprocess the button status data and vehicle cruise control data, and construct a standardized cruise status dataset. The cruise state synchronization reconstruction module is used to build a multi-source cruise state synchronization sampling and reconstruction model based on a standardized cruise state dataset. It performs time alignment and unified reconstruction of vehicle cruise control data around the button trigger time, and generates a state parameter expression consistent with the trigger time. The cruise reset target generation module is used to take the state parameter expression consistent with the trigger time as input, perform continuity analysis on the cruise state continuity from the offset relationship between the trigger vehicle speed and the historical cruise stored parameters, and generate a cruise reset target speed for smooth adjustment based on the continuity analysis results. The cruise reuse scheduling module is used to construct a cruise reuse scheduling model by combining the cruise reset target speed, and to perform segmented generation and dynamic reconstruction of the cruise target update process triggered by behavior, outputting a cruise target sequence with dynamic response capability.

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