Vehicle control method and device, processor and vehicle

Abstract

The invention discloses a vehicle control method and device, a processor and electronic equipment. The method comprises the steps that various driving signals generated in the driving process of a vehicle are monitored; in response to a plurality of driving signals including a target driving signal in a failure state, performing data prediction on the target driving signal through a plurality of data prediction paths to obtain a plurality of predicted driving data, the data prediction paths being used for representing paths for performing data prediction on the target driving signal, and the data prediction paths being used for representing paths for performing data prediction on the target driving signal; the predicted driving data is used for representing a prediction result of the driving data corresponding to the target driving signal; based on the multiple pieces of predicted driving data, a control strategy of the vehicle is determined, and the control strategy is used for representing a rule for controlling whether the vehicle is kept in a normal driving mode or not; and calling the control strategy to control the vehicle to run. The technical problem that the limitation of the power performance of the vehicle is high is solved.

Description

Technical Field

[0001] This invention relates to the field of vehicles, and more specifically, to a vehicle control method, apparatus, processor, and vehicle. Background Technology

[0002] Currently, the operating environment of vehicles is complex and variable. Sensors and their wiring in vehicles may malfunction due to vibration, humidity and heat and / or electromagnetic interference, which may cause loss or distortion of vehicle driving signals.

[0003] When a vehicle's driving signals are lost or distorted, the vehicle is often controlled to continue driving in a conservative limp mode, resulting in a high degree of limitation in the vehicle's power performance.

[0004] There is currently no effective solution to the technical problem of the limited power performance of the aforementioned vehicles. Summary of the Invention

[0005] This invention provides a vehicle control method, device, processor, and vehicle to at least address the technical problem of limited vehicle power performance.

[0006] According to one aspect of the present invention, a vehicle control method is provided, the method comprising: monitoring multiple driving signals generated by the vehicle during driving; responding to the multiple driving signals, including a target driving signal in a failed state, performing data prediction on the target driving signal through multiple data prediction paths to obtain multiple predicted driving data, wherein the data prediction path represents the path for performing data prediction on the target driving signal, and the predicted driving data represents the prediction result of the driving data corresponding to the target driving signal; determining a vehicle control strategy based on the multiple predicted driving data, wherein the control strategy represents the rule for controlling whether the vehicle maintains a normal driving mode; and invoking the control strategy to control the vehicle to drive.

[0007] Optionally, a vehicle control strategy is determined based on multiple predicted driving data, including: fusing multiple predicted driving data to obtain fused driving data; and determining a control strategy based on the fused driving data.

[0008] Optionally, multiple predicted driving data are fused to obtain fused driving data, including: determining the initial weights of each of the multiple predicted driving data to obtain multiple initial weights; normalizing the multiple initial weights to obtain multiple target weights; and using the multiple target weights to fuse the multiple predicted driving data to obtain fused driving data.

[0009] Optionally, multiple predicted driving data are fused using multiple target weights to obtain fused driving data, including: adjusting the predicted driving data corresponding to each target weight using each target weight among the multiple target weights; and fusing the multiple adjusted predicted driving data to obtain fused driving data.

[0010] Optionally, determining a control strategy based on the fused driving data includes: determining confidence data of the fused driving data, wherein the confidence data is used to represent the degree of confidence in the fused driving data for the vehicle; and determining a control strategy based on the confidence data.

[0011] Optionally, state data of multiple driving signals are determined to obtain multiple state data, wherein the state data is used to represent the degree of normality of the current state of the driving signal relative to the normal state; confidence data of the fused driving data is determined, including: determining the correspondence between each state data in the multiple state data and each target weight in the multiple target weights to obtain multiple correspondences; adjusting the corresponding state data according to the multiple correspondences using each target weight, and combining the multiple adjusted state data to obtain confidence data.

[0012] Optionally, determining a control strategy based on confidence data includes: determining a first control strategy in response to a confidence level corresponding to the confidence data being greater than a first preset confidence level, wherein the first control strategy represents a rule for controlling the vehicle to maintain a normal driving mode; determining a second control strategy in response to a confidence level corresponding to the confidence data being less than or equal to the first preset confidence level and greater than a second preset confidence level, wherein the first preset confidence level is greater than the second preset confidence level, and the second control strategy represents a rule for controlling the vehicle to switch from a normal driving mode to a performance degradation mode; and determining a third control strategy in response to a confidence level corresponding to the confidence data being less than the second preset confidence level, wherein the third control strategy represents a rule for controlling the vehicle to switch from a normal driving mode to a limp mode.

[0013] According to one aspect of the present invention, a vehicle control device is provided, the device comprising: a monitoring unit for monitoring various driving signals generated by the vehicle during driving; a prediction unit for, in response to the various driving signals, including a target driving signal in a failed state, performing data prediction on the target driving signal through multiple data prediction paths to obtain multiple predicted driving data, wherein the data prediction path represents the path for performing data prediction on the target driving signal, and the predicted driving data represents the prediction result of the driving data corresponding to the target driving signal; a first determining unit for determining a vehicle control strategy based on the multiple predicted driving data, wherein the control strategy represents the rules for controlling whether the vehicle maintains a normal driving mode; and a control unit for invoking the control strategy to control the vehicle's driving.

[0014] According to another aspect of the present invention, a processor is also provided. The processor is used to run a program, wherein the program, when run by the processor, executes the vehicle control method of the present invention.

[0015] According to another aspect of the embodiments of the present invention, a vehicle is also provided, comprising: a memory storing an executable program; and a processor for running the program, wherein the program executes the vehicle control method of various embodiments of the present invention during runtime.

[0016] According to another aspect of the present invention, a computer-readable storage medium is also provided. The computer-readable storage medium includes a stored program, wherein, when the program is executed, it controls the device where the computer-readable storage medium is located to perform the vehicle control method of the present invention.

[0017] According to another aspect of the present invention, a computer program product is also provided, the computer program product including a computer program, wherein the computer program, when executed by a processor, implements the vehicle control method of the present invention.

[0018] According to another aspect of the present invention, a computer program product is also provided, including a non-volatile computer-readable storage medium for storing a computer program, which, when executed by a processor, implements the vehicle control method of the present invention.

[0019] According to another aspect of the present invention, a computer program is also provided, which, when executed by a processor, implements the vehicle control method described in the above embodiments of the present invention.

[0020] In this embodiment of the invention, when controlling vehicle movement, various driving signals generated during vehicle movement are monitored; in response to the various driving signals, including a target driving signal in a failed state, multiple data prediction paths are used to predict the target driving signal, resulting in multiple predicted driving data; based on the multiple predicted driving data, a vehicle control strategy is determined; and the control strategy is invoked to control vehicle movement. Because this embodiment of the invention, when multiple driving signals are monitored, including a target driving signal in a failed state, can predict the target driving signal through multiple data prediction paths to obtain multiple predicted driving data, and based on the obtained multiple predicted driving data, a vehicle control strategy can be determined, and then the determined control strategy can be invoked to control vehicle movement, thereby achieving the goal of avoiding the vehicle continuing to drive in a conservative limp mode when the vehicle's driving signals are lost or distorted, thus solving the technical problem of high limitations in vehicle power performance, and achieving the technical effect of reducing the limitations of vehicle power performance. Attached Figure Description

[0021] The accompanying drawings, which are included to provide a further understanding of the invention and form part of this application, illustrate exemplary embodiments of the invention and, together with their description, serve to explain the invention and do not constitute an undue limitation thereof. In the drawings:

[0022] Figure 1 This is a flowchart of a vehicle control method according to an embodiment of the present invention;

[0023] Figure 2(a) is a flowchart of a method for handling drive system signal failure of a hybrid vehicle based on multi-source signals according to an embodiment of the present invention;

[0024] Figure 2(b) is a schematic diagram of a drive system for a hybrid vehicle according to an embodiment of the present invention;

[0025] Figure 2(c) is a schematic diagram of a signal health assessment system according to an embodiment of the present invention;

[0026] Figure 3 This is a schematic diagram of a vehicle control device according to an embodiment of the present invention;

[0027] Figure 4 This is a schematic diagram of a vehicle according to an embodiment of the present invention. Detailed Implementation

[0028] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. 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 should fall within the scope of protection of the present invention.

[0029] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this invention are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of the invention described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover a non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0030] According to an embodiment of the present invention, a vehicle control method is provided. It should be noted that the steps shown in the flowchart in the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions. Furthermore, although a logical order is shown in the flowchart, in some cases, the steps shown or described may be executed in a different order than that shown here.

[0031] Figure 1 This is a flowchart of a vehicle control method according to an embodiment of the present invention, such as... Figure 1 As shown, the method may include the following steps:

[0032] Step S101: Monitor various driving signals generated by the vehicle during driving.

[0033] In the technical solution provided in step S101 of the present invention, the aforementioned multiple driving signals may include: the rotational speed signal of the drive motor, the rotational speed signal of the gearbox input shaft, the rotational speed signal of the gearbox output shaft, the gearbox gear position signal, and the wheel rotational speed signal, etc. For example, the rotational speed signal of the drive motor can be represented by Nm, the rotational speed signal of the gearbox input shaft can be represented by Ni, the rotational speed signal of the gearbox output shaft can be represented by No, the gearbox gear position signal can be represented by Gear, and the wheel rotational speed signal, etc., can be represented by Nw. This is only an example and is not specifically limited.

[0034] In this embodiment, various driving signals generated by the vehicle during operation are monitored. Optionally, this embodiment uses a drive motor speed sensor to monitor the drive motor speed signal generated by the vehicle during operation. The drive motor speed sensor can be installed on the drive motor shaft or inside the drive motor controller. A transmission input shaft speed sensor and a transmission output shaft speed sensor can also monitor the transmission input shaft speed signal generated by the vehicle during operation. These transmission input and output shaft speed sensors can be installed inside the transmission. A transmission gear position sensor can monitor the transmission gear position signal generated by the vehicle during operation. This transmission gear position sensor can be installed at the transmission gear control lever. Finally, wheel speed sensors can monitor the wheel speed signals generated by the vehicle during operation. These wheel speed sensors can be installed on the wheel hub or brake disc of each wheel.

[0035] Step S102: In response to multiple driving signals, including a target driving signal in a failed state, data prediction is performed on the target driving signal through multiple data prediction paths to obtain multiple predicted driving data. The data prediction path is used to represent the path for data prediction on the target driving signal, and the predicted driving data is used to represent the prediction result of the driving data corresponding to the target driving signal.

[0036] In the technical solution provided by step S102 of the present invention, the data prediction path can be used to represent the path for data prediction of the target driving signal. For example, if the target driving signal is the rotational speed signal of the drive motor, the data prediction path can be used to represent the path for data prediction of the rotational speed signal of the drive motor; if the target driving signal is the gear position signal of the gearbox, the data prediction path can be used to represent the path for data prediction of the gear position signal of the gearbox. This is only an example and is not specifically limited.

[0037] In this embodiment, the predicted driving data can be used to represent the prediction result of driving data corresponding to the target driving signal. For example, if the target driving signal is the rotational speed signal of the drive motor, the predicted driving data can be used to represent the prediction result of the rotational speed corresponding to the rotational speed signal of the drive motor; if the target driving signal is the rotational speed signal of the gearbox output shaft, the predicted driving data can be used to represent the prediction result of the rotational speed corresponding to the rotational speed signal of the gearbox output shaft. This is only an example and is not specifically limited.

[0038] In this embodiment, after monitoring various driving signals generated by the vehicle during operation, and in response to the presence of a target driving signal that is in a failed state, data prediction is performed on the target driving signal through multiple data prediction paths to obtain multiple predicted driving data. Optionally, based on the monitoring of various driving signals, this embodiment determines whether the various driving signals include a driving signal in a failed state. If it is determined that the various driving signals include a driving signal in a failed state, then the driving signal in a failed state is identified as the target driving signal. Then, data prediction is performed on the target driving signal through multiple data prediction paths to obtain multiple predicted driving data, thereby achieving the purpose of obtaining prediction results for driving data corresponding to the target driving signal.

[0039] Optionally, if the target driving signal is the speed signal of the drive motor, the above data prediction path can be used to represent the path for data prediction of the speed signal of the drive motor, and the above data prediction path can include: a first path (which can also be represented by path 1), which is a path for data prediction of the speed signal of the drive motor based on the speed signal of the input shaft of the gearbox; a second path (which can also be represented by path 2), which is a path for data prediction of the speed signal of the drive motor based on the speed signal of the output shaft of the gearbox and the gear position signal of the gearbox; and a third path (which can also be represented by path 3), which is a path for data prediction of the speed signal of the drive motor based on the speed signal of the wheel, the gear position signal of the gearbox, and the reduction ratio signal (Final) of the wheel.

[0040] Optionally, if the above data prediction path includes a first path, a second path, and a third path, data prediction of the target driving signal can be performed through the first path, the second path, and the third path to obtain three predicted driving data. This is only an example and is not specifically limited.

[0041] Step S103: Based on multiple predicted driving data, determine the vehicle control strategy, wherein the control strategy is used to represent the rules for controlling whether the vehicle maintains a normal driving mode.

[0042] In the technical solution provided by step S103 of the present invention, the control strategy can be used to represent rules for controlling whether the vehicle maintains a normal driving mode. The control strategy can include: a first control strategy, a second control strategy, and a third control strategy. The first control strategy can be used to represent rules for controlling the vehicle to maintain a normal driving mode, the second control strategy can be used to represent rules for controlling the vehicle to switch from a normal driving mode to a performance degradation mode, and the third control strategy can be used to represent rules for controlling the vehicle to switch from a normal driving mode to a limp mode.

[0043] In this embodiment, in response to multiple driving signals, including a target driving signal in a failed state, data prediction is performed on the target driving signal through multiple data prediction paths to obtain multiple predicted driving data. Based on this multiple predicted driving data, a vehicle control strategy is determined. Optionally, this embodiment fuses the multiple predicted driving data to obtain fused driving data. Based on this fused driving data, a vehicle control strategy can be determined, thereby achieving the purpose of determining a rule for controlling whether the vehicle maintains a normal driving mode.

[0044] Optionally, based on obtaining multiple predicted driving data, an average value of the multiple predicted driving data is determined. Then, according to the differences between each of the multiple predicted driving data and the average value, a target driving data is determined from the multiple predicted driving data. The difference between the target driving data and the average value is less than the difference between the predicted driving data (excluding the target driving data) and the average value. A confidence value for the target driving data is then determined. Based on the confidence value of the target driving data, a vehicle control strategy can be determined, thereby achieving the purpose of determining a rule for controlling whether the vehicle maintains a normal driving mode.

[0045] Step S104: Invoke the control strategy to control the vehicle's movement.

[0046] In the technical solution provided by step S104 of the present invention, after determining the vehicle control strategy based on multiple predicted driving data, the control strategy is invoked to control the vehicle's driving. Optionally, in this embodiment, based on the determined vehicle control strategy, if the control strategy is a first control strategy, the vehicle is controlled to maintain normal driving mode; if the control strategy is a second control strategy, the vehicle is controlled to switch from normal driving mode to performance degradation mode, and the vehicle is controlled to maintain performance degradation mode; if the control strategy is a third control strategy, the vehicle is controlled to switch from normal driving mode to limp mode, and the vehicle is controlled to maintain limp mode.

[0047] In steps S101 to S104 of this application, when controlling vehicle movement, multiple driving signals generated during vehicle movement are monitored; in response to multiple driving signals, including a target driving signal in a failed state, multiple data prediction paths are used to predict the target driving signal to obtain multiple predicted driving data; based on the multiple predicted driving data, a vehicle control strategy is determined; and the control strategy is invoked to control vehicle movement. Because this embodiment of the invention, when multiple driving signals are monitored, including a target driving signal in a failed state, multiple data prediction paths are used to predict the target driving signal to obtain multiple predicted driving data. Based on the obtained multiple predicted driving data, a vehicle control strategy can be determined, and then the determined control strategy can be invoked to control vehicle movement. This achieves the goal of avoiding the vehicle continuing to drive in a conservative limp mode when the vehicle's driving signal is lost or distorted, thereby solving the technical problem of high limitations in vehicle power performance and achieving the technical effect of reducing the limitations of vehicle power performance.

[0048] The method described in this embodiment will be further described below.

[0049] As an optional embodiment, step S103, determining the vehicle control strategy based on multiple predicted driving data, includes: fusing the multiple predicted driving data to obtain fused driving data; and determining the control strategy based on the fused driving data.

[0050] In this embodiment, the aforementioned fused driving data can be represented by Nm_fusion.

[0051] In this embodiment, in response to multiple driving signals, including a target driving signal in a failed state, data prediction is performed on the target driving signal through multiple data prediction paths to obtain multiple predicted driving data. Then, the multiple predicted driving data are fused to obtain fused driving data. Optionally, this embodiment adjusts the multiple predicted driving data based on the obtained data, and then fuses the adjusted predicted driving data to obtain fused driving data.

[0052] In this embodiment, after fusing multiple predicted driving data to obtain fused driving data, a control strategy is determined based on the fused driving data. Optionally, based on the obtained fused driving data, this embodiment can determine whether the vehicle's control strategy is a first control strategy, a second control strategy, or a third control strategy, thereby achieving the goal of determining whether the vehicle maintains a normal driving mode, and thus realizing the technical effect of improving the accuracy of vehicle control.

[0053] The following section further describes the steps of fusing multiple predicted driving data to obtain fused driving data in this embodiment.

[0054] As an optional implementation method, multiple predicted driving data are fused to obtain fused driving data, including: determining the initial weights of each of the multiple predicted driving data to obtain multiple initial weights; normalizing the multiple initial weights to obtain multiple target weights; and using the multiple target weights to fuse the multiple predicted driving data to obtain fused driving data.

[0055] In this embodiment, the initial weights can be represented by Wk. For example, if the data prediction path includes a first path, a second path, and a third path, then the multiple initial weights include a first initial weight W1 corresponding to the first path, a second initial weight W2 corresponding to the second path, and a third initial weight W3 corresponding to the third path. This is only an example and is not a specific limitation.

[0056] In this embodiment, the target weight can be represented by Wk', where Wk' = Wk / (W1 + W2 + W3). For example, if the multiple initial weights include: a first initial weight W1 corresponding to the first path, a second initial weight W2 corresponding to the second path, and a third initial weight W3 corresponding to the third path, then the multiple target weights can include: a first target weight W1', a second target weight W2', and a third target weight W3'. This is only an example and is not specifically limited.

[0057] In this embodiment, in response to multiple driving signals, including a target driving signal in a failed state, data prediction is performed on the target driving signal through multiple data prediction paths to obtain multiple predicted driving data. Then, the initial weights of the multiple predicted driving data are determined to obtain multiple initial weights. The multiple initial weights are then normalized to obtain multiple target weights.

[0058] Optionally, in this embodiment, based on the data prediction of the target driving signal through the first path, the second path, and the third path to obtain three predicted driving data, a first initial weight W1, a second initial weight W2, and a third initial weight W3 are determined for each of the three predicted driving data, resulting in three initial weights. Normalizing these three initial weights yields multiple target weights; that is, normalizing the first initial weight W1, the second initial weight W2, and the third initial weight W3 yields a first target weight W1', a second target weight W2', and a third target weight W3'. This is merely an example and not a specific limitation.

[0059] In this embodiment, after normalizing multiple initial weights to obtain multiple target weights, the multiple predicted driving data are fused using these target weights to obtain fused driving data. Optionally, this embodiment, based on obtaining three target weights, uses a first target weight W1', a second target weight W2', and a third target weight W3' to fuse three predicted driving data to obtain fused driving data, thereby achieving the purpose of fusing predicted driving data.

[0060] The following section further describes the steps of fusing multiple predicted driving data using multiple target weights to obtain fused driving data in this embodiment.

[0061] As an optional implementation method, multiple predicted driving data are fused using multiple target weights to obtain fused driving data, including: adjusting the predicted driving data corresponding to each target weight using each target weight among the multiple target weights; and fusing the multiple adjusted predicted driving data to obtain fused driving data.

[0062] In this embodiment, if the target driving signal is the rotational speed signal of the drive motor, the aforementioned predicted driving data can be used to represent the predicted rotational speed corresponding to the rotational speed signal of the drive motor. The multiple predicted driving data may include: first predicted driving data obtained by predicting the target driving signal through a first path, second predicted driving data obtained by predicting the target driving signal through a second path, and third predicted driving data obtained by predicting the target driving signal through a third path. For example, the first predicted driving data can be represented by Nm_est1, the second predicted driving data by Nm_est2, and the third predicted driving data by Nm_est3. This is merely an example and not a specific limitation.

[0063] In this embodiment, after normalizing multiple initial weights to obtain multiple target weights, the predicted driving data corresponding to each target weight is adjusted using each target weight. Optionally, based on obtaining multiple target weights, if the multiple target weights include a first target weight W1', a second target weight W2', and a third target weight W3', then the first target weight W1' is used to adjust the first predicted driving data corresponding to the first target weight W1', the second target weight W2' is used to adjust the second predicted driving data corresponding to the second target weight W2', and the third target weight W3' is used to adjust the third predicted driving data corresponding to the third target weight W3'.

[0064] In this embodiment, after adjusting the predicted driving data corresponding to each of the multiple target weights, the adjusted predicted driving data are fused to obtain fused driving data. Optionally, this embodiment adjusts the first, second, and third predicted driving data, and then fuses the adjusted first, second, and third predicted driving data to obtain fused driving data, thereby achieving the purpose of fusing predicted driving data.

[0065] Optionally, formula (1) can be used to fuse the adjusted first predicted driving data, the adjusted second predicted driving data, and the adjusted third predicted driving data to obtain fused driving data:

[0066] Nm_fusion=W1'*Nm_est1+W2'*Nm_est2+W3'*Nm_est3 (1)

[0067] The steps for determining the control strategy based on fused driving data in this embodiment will be further described below.

[0068] As an optional implementation method, determining a control strategy based on fused driving data includes: determining confidence data of the fused driving data, wherein the confidence data is used to represent the degree of confidence of the fused driving data for the vehicle; and determining a control strategy based on the confidence data.

[0069] In this embodiment, the confidence data described above can be used to represent the degree of confidence in the fused driving data for the vehicle. For example, the confidence data can be represented by C_fusion.

[0070] In this embodiment, after fusing multiple predicted driving data to obtain fused driving data, the confidence level of the fused driving data is determined. Optionally, based on the obtained fused driving data, this embodiment calculates the confidence level of the fused driving data to obtain the confidence level of the fused driving data, thereby achieving the purpose of determining the confidence level of the fused driving data for the vehicle.

[0071] In this embodiment, after determining the confidence data of the fused driving data, a control strategy is determined based on the confidence data. Optionally, this embodiment, based on the determined confidence data of the fused driving data, determines the confidence level corresponding to the confidence data, compares the confidence level corresponding to the confidence data with a preset confidence level, and obtains the relationship between the confidence level corresponding to the confidence data and the preset confidence level; based on the relationship between the confidence level corresponding to the confidence data and the preset confidence level, the vehicle control strategy can be determined, thereby achieving the purpose of determining the rules for controlling whether the vehicle maintains a normal driving mode.

[0072] The steps for determining the confidence data of the fused driving data in this embodiment will be further described below.

[0073] As an optional embodiment, state data of multiple driving signals are determined to obtain multiple state data, wherein the state data is used to represent the degree of normality of the current state of the driving signal relative to the normal state; the confidence data of the fused driving data is determined, including: determining the correspondence between each state data in the multiple state data and each target weight in the multiple target weights to obtain multiple correspondences; adjusting the corresponding state data according to the multiple correspondences using each target weight; and combining the multiple adjusted state data to obtain confidence data.

[0074] In this embodiment, the aforementioned state data can be used to represent the degree of normality of the current state of the driving signal relative to the normal state. The multiple state data can be represented by various health state coefficients (C_n, C_i, C_o, C_g, C_w) for the driving signal. C_n can be the health state coefficient of the drive motor's rotational speed signal, C_i can be the health state coefficient of the gearbox input shaft's rotational speed signal, C_o can be the health state coefficient of the gearbox output shaft's rotational speed signal, C_g can be the health state coefficient of the gearbox's gear position signal, and C_w can be the health state coefficient of the wheel's rotational speed signal. For example, the aforementioned normal state can also be called a healthy state; that is, the aforementioned state data can be used to represent the degree of health of the current state of the driving signal relative to the healthy state.

[0075] In this embodiment, the state data of various driving signals are determined to obtain multiple state data. Optionally, this embodiment can obtain multiple state data by performing a health status assessment on the state data of various driving signals, thereby achieving the purpose of determining the degree of normality of the current state of the driving signal relative to the normal state.

[0076] It should be noted that the above health status assessment is related to physical range compliance, continuity of change rate, signal consistency, and signal quality. Taking the rotational speed signal of the drive motor as an example, the calculation steps of C_n are as follows:

[0077] Step 1: Conduct a physical range compliance check (Range Check) on the rotational speed signal of the drive motor. That is, according to the physical characteristics of the drive motor, the rotational speed of the drive motor must be within a reasonable range. For example, the reasonable range is [0, N_max] rpm. Here, define the range compliance factor F_range. If Nm is completely within [0, N_max], then F_range = 1.0; if Nm exceeds the range, then F_range = 0. It should be noted that for further smoothing, a buffer interval (such as [N_max, N_max * 1.1]) can be set, within which F_range linearly decays from 1.0 to 0.

[0078] Step 2: Conduct a continuity of change rate check (Plausibility Check) on the rotational speed signal of the drive motor. That is, the change in the rotational speed of the drive motor is limited by inertia and maximum torque, and the change rate (acceleration / deceleration) of the drive motor has a physical upper limit. Here, calculate the current signal change rate: dNm = |Nm(t) - Nm(t - 1)| / ΔT, and define the change rate compliance factor F_plaus. If dNm < dN_max (the maximum allowable change rate), then F_plaus = 1.0; if dNm >= dN_max, then F_plaus = 0. Similarly, a smoothing decay interval can be set.

[0079] Step 3: Conduct a signal consistency check (Cross-Check) on the rotational speed signal of the drive motor. That is, using the system redundancy relationship, compare the rotational speed signal of the drive motor with other reliable signals in real time. Here, if the rotational speed Ni of the input shaft of the transmission is diagnosed as highly healthy (for example, C_i > 0.9), then calculate the consistency error: Err_cross = |Nm - Ni|. In theory, the two should be equal. In addition, define the consistency factor F_cross. If Err_cross < Err_threshold (a very small allowable error), then F_cross = 1.0. As Err_cross increases, F_cross exponentially decays from 1.0 to 0.

[0080] Step four involves performing a signal quality check on the drive motor's speed signal. This involves analyzing the characteristics of the drive motor's speed signal itself, applicable to both analog and digital signals. Specifically, this includes calculating the variance or standard deviation of the signal within a time window; excessive fluctuations in the variance or standard deviation indicate interference with the drive motor's speed signal; detecting frozen values ​​in the drive motor's speed signal: if the drive motor's speed signal remains unchanged for multiple consecutive cycles, it may mean the corresponding sensor is "stuck"; and defining a signal quality factor F_quality, which is mapped from 1.0 (stable and variable) to 0 (frozen or drastically fluctuating) based on the above analysis results.

[0081] Step 5, calculate C_n using the following formula (2):

[0082] C_n=F_range*F_plaus*F_cross*F_quality (2)

[0083] When any of the above factors drops to 0, C_n is 0, that is, the current state of the drive motor speed signal is 0 relative to the health state.

[0084] In this embodiment, the state data of the above-mentioned multiple driving signals may include: first state data C_i corresponding to the first target weight W1', second state data C_o and C_g corresponding to the second target weight W2', and third state data C_w corresponding to the third target weight W3'.

[0085] In this embodiment, after determining the state data of various driving signals and obtaining multiple state data, the correspondence between each state data and each target weight in the multiple target weights is determined, resulting in multiple correspondences. According to the multiple correspondences, the corresponding state data is adjusted using each target weight, and the multiple adjusted state data are combined to obtain confidence data.

[0086] Optionally, this embodiment, based on obtaining multiple state data, determines the correspondence between each state data and each target weight in the multiple target weights, thus obtaining multiple correspondences. According to these multiple correspondences, the first state data corresponding to the first target weight W1' is adjusted using the first target weight W1', the second state data corresponding to the second target weight W2' is adjusted using the second target weight W2', and the third state data corresponding to the third target weight W3' is adjusted using the third target weight W3'. The adjusted first state data, adjusted second state data, and adjusted third state data are then combined to obtain confidence data.

[0087] Optionally, using formula (3), the adjusted first-state data, the adjusted second-state data, and the adjusted third-state data can be combined to obtain confidence data:

[0088] C_fusion=W1'*C_i+W2'*(C_o*C_g)+W3'*C_w (3)

[0089] The steps for determining the control strategy based on confidence data in this embodiment will be further described below.

[0090] As an optional embodiment, determining a control strategy based on confidence data includes: determining a first control strategy in response to a confidence level corresponding to the confidence data being greater than a first preset confidence level, wherein the first control strategy represents a rule for controlling the vehicle to maintain a normal driving mode; determining a second control strategy in response to a confidence level corresponding to the confidence data being less than or equal to the first preset confidence level and greater than a second preset confidence level, wherein the first preset confidence level is greater than the second preset confidence level, and the second control strategy represents a rule for controlling the vehicle to switch from a normal driving mode to a performance degradation mode; and determining a third control strategy in response to a confidence level corresponding to the confidence data being less than the second preset confidence level, wherein the third control strategy represents a rule for controlling the vehicle to switch from a normal driving mode to a limp mode.

[0091] In this embodiment, the aforementioned preset confidence level may include a first preset confidence level and a second preset confidence level. The first preset confidence level is greater than the second preset confidence level. For example, the first preset confidence level may be 0.8, and the second preset confidence level may be 0.5. These values ​​are merely illustrative and not intended to be specific.

[0092] In this embodiment, the first control strategy described above can be used to represent rules for controlling the vehicle to maintain a normal driving mode.

[0093] In this embodiment, after determining the confidence data of the fused driving data, in response to the confidence level corresponding to the confidence data being greater than a first preset confidence level, the control strategy is determined as the first control strategy. Optionally, based on determining the confidence data of the fused driving data, this embodiment compares the confidence level corresponding to the confidence data with the first preset confidence level and the second preset confidence level to obtain the relationship between the confidence level corresponding to the confidence data and the first preset confidence level and the second preset confidence level. If the relationship indicates that the confidence level corresponding to the confidence data is greater than the first preset confidence level, then the control strategy is determined as the first control strategy, thereby achieving the purpose of determining the rules for controlling the vehicle to maintain a normal driving mode.

[0094] In this embodiment, the second control strategy described above can be used to represent the rules for controlling the vehicle to switch from a normal driving mode to a performance degradation mode.

[0095] In this embodiment, after determining the confidence data of the fused driving data, in response to the confidence level corresponding to the confidence data being less than or equal to a first preset confidence level and greater than a second preset confidence level, the control strategy is determined as a second control strategy. Optionally, based on determining the confidence data of the fused driving data, this embodiment compares the confidence level corresponding to the confidence data with the first preset confidence level and the second preset confidence level to obtain the relationship between the confidence level corresponding to the confidence data and the first preset confidence level and the second preset confidence level. If the relationship indicates that the confidence level corresponding to the confidence data is less than or equal to the first preset confidence level and greater than the second preset confidence level, then the control strategy is determined as the second control strategy, thereby achieving the purpose of determining the rules for controlling the vehicle to switch from normal driving mode to performance degradation mode.

[0096] In this embodiment, the third control strategy described above can be used to represent the rules for controlling the vehicle to switch from normal driving mode to limp mode.

[0097] In this embodiment, after determining the confidence data of the fused driving data, in response to the confidence level corresponding to the confidence data being less than the second preset confidence level, the control strategy is determined to be the third control strategy. Optionally, based on determining the confidence data of the fused driving data, this embodiment compares the confidence level corresponding to the confidence data with the first preset confidence level and the second preset confidence level to obtain the relationship between the confidence level corresponding to the confidence data and the first preset confidence level and the second preset confidence level. If the relationship indicates that the confidence level corresponding to the confidence data is less than the second preset confidence level, then the control strategy is determined to be the third control strategy, thereby achieving the purpose of determining the rule for controlling the vehicle to switch from normal driving mode to limp mode.

[0098] In this embodiment of the invention, when controlling vehicle movement, various driving signals generated during vehicle movement are monitored; in response to the various driving signals, including a target driving signal in a failed state, multiple data prediction paths are used to predict the target driving signal, resulting in multiple predicted driving data; based on the multiple predicted driving data, a vehicle control strategy is determined; and the control strategy is invoked to control vehicle movement. Because this embodiment of the invention, when multiple driving signals are monitored, including a target driving signal in a failed state, can predict the target driving signal through multiple data prediction paths to obtain multiple predicted driving data, and based on the obtained multiple predicted driving data, a vehicle control strategy can be determined, and then the determined control strategy can be invoked to control vehicle movement, thereby achieving the goal of avoiding the vehicle continuing to drive in a conservative limp mode when the vehicle's driving signals are lost or distorted, thus solving the technical problem of high limitations in vehicle power performance, and achieving the technical effect of reducing the limitations of vehicle power performance.

[0099] The technical solutions of the embodiments of the present invention will be illustrated below with reference to preferred embodiments.

[0100] Currently, the operating environment of vehicles is complex and variable. Sensors and their wiring in vehicles may malfunction due to vibration, humidity and heat and / or electromagnetic interference, which may cause loss or distortion of vehicle driving signals.

[0101] When a vehicle's driving signals are lost or distorted, the vehicle is often controlled to continue driving in a conservative limp mode, resulting in a high degree of limitation in the vehicle's power performance.

[0102] To address the aforementioned technical problems, this invention proposes a vehicle control method. When multiple driving signals are detected, including a target driving signal in a failed state, multiple data prediction paths are used to predict the target driving signal, resulting in multiple predicted driving data. Based on this predicted driving data, a vehicle control strategy can be determined. Then, the determined control strategy is invoked to control the vehicle's movement. This achieves the goal of preventing the vehicle from continuing to drive in a conservative limp mode when the driving signal is lost or distorted, thus solving the technical problem of limited vehicle power performance and achieving the technical effect of reducing the limitations of vehicle power performance.

[0103] In this embodiment, by executing the drive system signal failure handling method for a hybrid vehicle based on multi-source signals, the vehicle's driving can be controlled by invoking the vehicle's control strategy. For example, Figure 2(a) is a flowchart of a drive system signal failure handling method for a hybrid vehicle based on multi-source signals according to an embodiment of the present invention. As shown in Figure 2(a), the method may include the following steps:

[0104] Step S201: Real-time acquisition of various driving signals from the vehicle drive system.

[0105] In the technical solution provided by step S201 of the present invention, the above-mentioned multiple driving signals may include: the rotational speed signal of the drive motor, the rotational speed signal of the gearbox input shaft, the rotational speed signal of the gearbox output shaft, the gearbox gear signal, and the rotational speed signal of the wheels, etc.

[0106] For example, the aforementioned drive system can be as shown in Figure 2(b). For instance, Figure 2(b) is a schematic diagram of a drive system for a hybrid vehicle according to an embodiment of the present invention. The drive system 200 may include: an engine 201, a clutch 202, a drive motor 203, a gearbox 204, and wheels 205. The engine 201 and the drive motor 203 can be connected via the clutch 202, and the drive motor 203 and the wheels 205 can be connected via the gearbox 204.

[0107] In this embodiment, for each driving signal, its health status coefficient (C_n, C_i, C_o, C_g, C_w) is calculated. This coefficient is a continuous value between [0, 1], where 1 represents absolute health and 0 represents complete failure. The health status assessment of the various driving signals mentioned above is related to physical range compliance, rate of change continuity, signal consistency, and signal quality. Taking the speed signal of the drive motor as an example, the health status of the drive motor's speed signal can be assessed through the signal health assessment system. For example, Figure 2(c) is a schematic diagram of a signal health assessment system according to an embodiment of the present invention. As shown in Figure 2(c), the signal health assessment system 220 may include: a physical range compliance check module 221, a rate of change continuity check module 222, a signal consistency check module 223, and a signal quality check module 224.

[0108] In this embodiment, the above physical range compliance checking module 221 can be used to perform physical range compliance checking on the rotational speed signal of the drive motor. That is, according to the physical characteristics of the drive motor, the rotational speed of the drive motor must be within a reasonable range. For example, the reasonable range is [0, N_max] rpm. Here, a range compliance factor F_range is defined. If Nm is completely within [0, N_max], then F_range = 1.0; if Nm exceeds the range, then F_range = 0. It should be noted that for further smoothing, a buffer interval (such as [N_max, N_max * 1.1]) can be set, within which F_range linearly decays from 1.0 to 0.

[0109] In this embodiment, the above rate of change continuity checking module 222 can be used to perform rate of change continuity checking on the rotational speed signal of the drive motor. That is, the change in the rotational speed of the drive motor is limited by inertia and maximum torque, and the rate of change (acceleration / deceleration) of the drive motor has a physical upper limit. Here, calculate the current signal rate of change: dNm = |Nm(t) - Nm(t - 1)| / ΔT, and define a rate of change compliance factor F_plaus. If dNm < dN_max (the maximum allowable rate of change), then F_plaus = 1.0; if dNm >= dN_max, then F_plaus = 0. Similarly, a smoothing attenuation interval can be set.

[0110] In this embodiment, the above signal consistency checking module 223 can be used to perform signal consistency checking on the rotational speed signal of the drive motor. That is, using the system redundancy relationship, the rotational speed signal of the drive motor is compared with other reliable signals in real time. Here, if the rotational speed Ni of the transmission input shaft is diagnosed as being highly healthy (for example, C_i > 0.9), then calculate the consistency error: Err_cross = |Nm - Ni|. In theory, the two should be equal. In addition, define a consistency factor F_cross. If Err_cross < Err_threshold (a very small allowable error), then F_cross = 1.0. As Err_cross increases, F_cross exponentially decays from 1.0 to 0.

[0111] In this embodiment, the signal quality check module 224 can be used to perform a signal quality check on the speed signal of the drive motor, that is, to analyze the characteristics of the speed signal itself, applicable to analog or digital signals. Specifically, it calculates the variance or standard deviation of the signal within a time window; if the fluctuation of the variance or standard deviation is too large, it indicates that the speed signal of the drive motor is being interfered with; it performs a freeze-value detection on the speed signal of the drive motor: if the speed signal of the drive motor does not change within several consecutive cycles, it may mean that the corresponding sensor is "stuck"; and it defines a signal quality factor F_quality, which is mapped from 1.0 (stable and changing) to 0 (frozen or drastically fluctuating) based on the above analysis results.

[0112] Step 5: Calculate C_n using the above formula (2). Wherein, when any of the above factors decreases to 0, then C_n is 0, that is, the current state of the drive motor speed signal is 0 relative to the healthy state.

[0113] After real-time acquisition of various driving signals from the vehicle drive system, step S202 is executed to determine whether the various driving signals include driving signals that are in a failed state.

[0114] If it is determined that the driving signals are not in a failed state, then step S203 is executed to output the driving signals.

[0115] If it is determined that multiple driving signals, including driving signals in a failed state, then steps S204, S205 and S206 are executed to obtain the first initial weight W1 corresponding to the first path, the second initial weight W2 corresponding to the second path, and the third initial weight W3 corresponding to the third path.

[0116] In this embodiment, the first predicted driving data Nm_est1 = Ni is obtained by predicting the target driving signal through the first path. The second predicted driving data Nm_est2 = No*i_Gear is obtained by predicting the target driving signal through the second path. The third predicted driving data Nm_est3 = Nw*i_final*i_Gear is obtained by predicting the target driving signal through the third path. The weight (Wk) of each estimated path is proportional to the product of the real-time health status coefficients of all source signals it depends on. For example, the weight corresponding to path 1 is equal to the health status of Ni, i.e., W1 = C_i; the weight W2 of path 2 is proportional to (C_o*C_g), for example, the weight W2 of path 2 is equal to the product of the health status of No and Gear, i.e., W2 = (C_o*C_g); the weight corresponding to path 3 is equal to the health status of Nw, i.e., W3 = C_w.

[0117] After obtaining the first initial weight W1, the second initial weight W2, and the third initial weight W3, step S207 is executed to normalize the first initial weight W1, the second initial weight W2, and the third initial weight W3, and to calculate the fused driving data.

[0118] In the technical solution provided by step S207 of the present invention, all weights are normalized (Wk'=Wk / ΣWk), and the final fused driving data (Nm_fusion) is the weighted sum of the estimated value of each path and its normalized weight: Nm_fusion=Σ(Wk'*Nm_est_k).

[0119] After calculating the fused driving data, step S208 is executed to determine the confidence level of the fused driving data for the vehicle.

[0120] In the technical solution provided in step S208 of the present invention, the confidence level (C_fusion) of the fused driving data is calculated. This confidence level is a weighted sum of the normalized weights of each estimated path and the health values ​​of the source signals they depend on, for example: C_fusion = W1'*C_i + W2'*(C_o*C_g) + W3'*C_w. This fused driving data (also referred to as the fused estimate) and its confidence level are then output to the Vehicle Domain Control Unit (VDCU).

[0121] After determining the confidence level of the fused driving data for the vehicle, step S209 is executed, and a fault-tolerant control strategy is implemented based on the confidence level.

[0122] In the technical solution provided by step S209 of the present invention, based on the confidence level (or simply confidence level), the graded fault-tolerant control vehicle controller receives the fused driving data and its confidence level, and executes differentiated fault-tolerant control strategies. Specifically, if C_fusion > 0.8, the confidence level is high, the fused driving data is normal data, and the vehicle control strategy is determined as the first control strategy, i.e., the vehicle maintains full-function operation; if 0.5 < C_fusion ≤ 0.8, the confidence level is medium, and the vehicle control strategy is determined as the second control strategy, triggering a performance degradation mode, moderately limiting torque or maximum speed, and alerting the driver; if C_fusion ≤ 0.5, the confidence level is low, and the vehicle control strategy is determined as the third control strategy, triggering a traditional limp-home mode, greatly limiting power output, and requesting immediate repair.

[0123] In this embodiment of the invention, when controlling vehicle movement, various driving signals generated during vehicle movement are monitored; in response to the various driving signals, including a target driving signal in a failed state, multiple data prediction paths are used to predict the target driving signal, resulting in multiple predicted driving data; based on the multiple predicted driving data, a vehicle control strategy is determined; and the control strategy is invoked to control vehicle movement. Because this embodiment of the invention, when multiple driving signals are monitored, including a target driving signal in a failed state, can predict the target driving signal through multiple data prediction paths to obtain multiple predicted driving data, and based on the obtained multiple predicted driving data, a vehicle control strategy can be determined, and then the determined control strategy can be invoked to control vehicle movement, thereby achieving the goal of avoiding the vehicle continuing to drive in a conservative limp mode when the vehicle's driving signals are lost or distorted, thus solving the technical problem of high limitations in vehicle power performance, and achieving the technical effect of reducing the limitations of vehicle power performance.

[0124] According to an embodiment of the present invention, a vehicle control device is also provided. It should be noted that this vehicle control device can be used to execute a vehicle control method according to one of the embodiments.

[0125] Figure 3 This is a schematic diagram of a vehicle control device according to an embodiment of the present invention. Figure 3 As shown, the vehicle control device 300 may include: a monitoring unit 301, a prediction unit 302, a first determination unit 303, and a control unit 304.

[0126] The monitoring unit 301 is used to monitor various driving signals generated by the vehicle during driving.

[0127] The prediction unit 302 is used to respond to multiple driving signals, including a target driving signal in a failed state, by performing data prediction on the target driving signal through multiple data prediction paths to obtain multiple predicted driving data. The data prediction path is used to represent the path for performing data prediction on the target driving signal, and the predicted driving data is used to represent the prediction result of the driving data corresponding to the target driving signal.

[0128] The first determining unit 303 is used to determine the vehicle's control strategy based on multiple predicted driving data, wherein the control strategy is used to represent the rules for controlling whether the vehicle maintains a normal driving mode.

[0129] Control unit 304 is used to invoke control strategies and control vehicle movement.

[0130] Optionally, the first determining unit 303 may include: a fusion module for fusing multiple predicted driving data to obtain fused driving data; and a first determining module for determining a control strategy based on the fused driving data.

[0131] Optionally, the fusion module may include: a first determining submodule, used to determine the initial weights of each of the multiple predicted driving data to obtain multiple initial weights; a processing submodule, used to normalize the multiple initial weights to obtain multiple target weights; and a fusion submodule, used to fuse the multiple predicted driving data using the multiple target weights to obtain fused driving data.

[0132] Optionally, the fusion submodule can perform the following steps to fuse multiple predicted driving data using multiple target weights to obtain fused driving data: adjusting the predicted driving data corresponding to each target weight using each of the multiple target weights; and fusing the multiple adjusted predicted driving data to obtain fused driving data.

[0133] Optionally, the first determining module may include: a second determining submodule, used to determine confidence data of the fused driving data, wherein the confidence data is used to represent the degree of confidence of the fused driving data for the vehicle; and a third determining submodule, used to determine a control strategy based on the confidence data.

[0134] Optionally, the vehicle control device 300 may include: a second determining unit, configured to determine state data of multiple driving signals respectively, to obtain multiple state data, wherein the state data is used to represent the degree of normality of the current state of the driving signal relative to the normal state; the second determining submodule may determine the confidence data of the fused driving data by performing the following steps: determining the correspondence between each state data in the multiple state data and each target weight in the multiple target weights respectively, to obtain multiple correspondences; adjusting the corresponding state data according to the multiple correspondences using each target weight, and combining the multiple adjusted state data to obtain confidence data.

[0135] Optionally, the third determining submodule can determine a control strategy based on confidence data by performing the following steps: In response to a confidence level corresponding to the confidence data being greater than a first preset confidence level, the control strategy is determined as a first control strategy, wherein the first control strategy represents a rule for controlling the vehicle to maintain a normal driving mode; in response to a confidence level corresponding to the confidence data being less than or equal to the first preset confidence level and greater than a second preset confidence level, the control strategy is determined as a second control strategy, wherein the first preset confidence level is greater than the second preset confidence level, and the second control strategy represents a rule for controlling the vehicle to switch from a normal driving mode to a performance degradation mode; in response to a confidence level corresponding to the confidence data being less than the second preset confidence level, the control strategy is determined as a third control strategy, wherein the third control strategy represents a rule for controlling the vehicle to switch from a normal driving mode to a limp mode.

[0136] In this embodiment, a vehicle control device is provided, which may include: a monitoring unit for monitoring various driving signals generated by the vehicle during driving; a prediction unit for responding to various driving signals, including a target driving signal in a failed state, by performing data prediction on the target driving signal through multiple data prediction paths to obtain multiple predicted driving data, wherein the data prediction path represents the path for performing data prediction on the target driving signal, and the predicted driving data represents the prediction result of the driving data corresponding to the target driving signal; a first determining unit for determining a vehicle control strategy based on the multiple predicted driving data, wherein the control strategy represents the rule for controlling whether the vehicle maintains a normal driving mode; and a control unit for invoking the control strategy to control the vehicle's driving, thereby achieving the purpose of avoiding the vehicle continuing to drive in a conservative limp mode when the vehicle's driving signal is lost or distorted, thus solving the technical problem of the high limitation of the vehicle's power performance, and thereby achieving the technical effect of reducing the limitation of the vehicle's power performance.

[0137] According to an embodiment of the present invention, a processor is also provided for running a program, wherein the program is executed by the processor to perform the vehicle control method in the embodiment.

[0138] According to an embodiment of the present invention, a vehicle is also provided. Figure 4 This is a schematic diagram of a vehicle according to an embodiment of the present invention, such as... Figure 4 As shown, the vehicle 400 may include a memory 410 and a processor 420, wherein the memory 410 is used to store an executable program; the processor 420 is used to run the program stored in the memory 410, and the program executes the vehicle control method of this application when it runs.

[0139] In this application, "multiple" refers to two or more.

[0140] In this application, unless otherwise expressly defined, the terms "installation," "connection," and "linking" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral connection; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; and they can refer to the internal connection between two components. Those skilled in the art can understand the specific meaning of the above terms in this application based on the specific circumstances.

[0141] The terms “first,” “second,” “third,” “fourth,” etc., in this application (if present) are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence.

[0142] In this application, the term "and / or" is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A existing alone, A and B existing simultaneously, or B existing alone. Additionally, in this application, the character " / " generally indicates that the preceding and following related objects have an "or" relationship.

[0143] Unless otherwise specified, all steps of this application may be performed sequentially or randomly. For example, the vehicle control method of this application may include steps S101 and S102, meaning that the vehicle control method of this application may include steps S101 and S102 performed sequentially, or it may include steps S102 and S101 performed sequentially.

[0144] For example, the vehicle control method of this application may also include step S103, which means that step S103 can be added to the method in any order. For example, the vehicle control method of this application may include steps S101, S102 and S103, or it may include steps S101, S103 and S102, or it may include steps S103, S101 and S102, etc. This is only an example and is not specifically limited.

[0145] According to another aspect of the present invention, a computer-readable storage medium is also provided. The computer-readable storage medium includes a stored program, wherein, when the program is executed, it controls the device containing the computer-readable storage medium to perform the vehicle control method described in the embodiment.

[0146] Computer-readable storage media, also known as computer storage media, may include data signals propagated in baseband or as part of a carrier wave, carrying readable program code. These propagated data signals may take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. Computer-readable storage media can transmit, propagate, or transfer programs for use by or in conjunction with an instruction execution system, apparatus, or device.

[0147] The program code contained in a computer-readable storage medium may be transmitted using any suitable medium, including but not limited to wireless, wired, optical fiber, radio frequency, or any suitable combination thereof.

[0148] According to an embodiment of the present invention, a computer program product is also provided, the computer program product including a computer program, wherein the computer program, when executed by a processor, implements the vehicle control method of the embodiment.

[0149] According to an embodiment of the present invention, a computer program product is also provided, including a non-volatile computer-readable storage medium for storing a computer program, which, when executed by a processor, implements the vehicle control method in the embodiment.

[0150] According to an embodiment of the present invention, a computer program is also provided, which, when executed by a processor, implements the vehicle control method of the embodiment.

[0151] Optionally, when the above-mentioned computer program is executed by the processor, the program code implements the following steps: monitoring various driving signals generated by the vehicle during driving; responding to the various driving signals, including a target driving signal in a failed state, performing data prediction on the target driving signal through multiple data prediction paths to obtain multiple predicted driving data, wherein the data prediction path is used to represent the path for performing data prediction on the target driving signal, and the predicted driving data is used to represent the prediction result of the driving data corresponding to the target driving signal; determining the vehicle control strategy based on the multiple predicted driving data, wherein the control strategy is used to represent the rules for controlling whether the vehicle maintains a normal driving mode; and invoking the control strategy to control the vehicle's driving.

[0152] Optionally, when the above computer program is executed by the processor, the program code implements the following steps: fusing multiple predicted driving data to obtain fused driving data; and determining a control strategy based on the fused driving data.

[0153] Optionally, when the above computer program is executed by the processor, the program code implements the following steps: determining the initial weights of each of the multiple predicted driving data to obtain multiple initial weights; normalizing the multiple initial weights to obtain multiple target weights; and using the multiple target weights to fuse the multiple predicted driving data to obtain fused driving data.

[0154] Optionally, when the above computer program is executed by the processor, the program code implements the following steps: adjusting the predicted driving data corresponding to each of the multiple target weights using each target weight; and fusing the multiple adjusted predicted driving data to obtain fused driving data.

[0155] Optionally, when the above computer program is executed by the processor, the program code implements the following steps: determining confidence data of the fused driving data, wherein the confidence data is used to represent the degree of confidence of the fused driving data for the vehicle; and determining a control strategy based on the confidence data.

[0156] Optionally, when the above computer program is executed by the processor, the program code implements the following steps: determining the state data of multiple driving signals to obtain multiple state data, wherein the state data is used to represent the degree of normality of the current state of the driving signal relative to the normal state; determining the correspondence between each state data in the multiple state data and each target weight in the multiple target weights to obtain multiple correspondences; adjusting the corresponding state data according to the multiple correspondences using each target weight, and combining the multiple adjusted state data to obtain confidence data.

[0157] Optionally, when the above-mentioned computer program is executed by the processor, the program code implements the following steps: in response to the confidence level corresponding to the confidence data being greater than a first preset confidence level, the control strategy is determined to be a first control strategy, wherein the first control strategy is used to represent a rule for controlling the vehicle to maintain a normal driving mode; in response to the confidence level corresponding to the confidence data being less than or equal to the first preset confidence level and greater than a second preset confidence level, the control strategy is determined to be a second control strategy, wherein the first preset confidence level is greater than the second preset confidence level, and the second control strategy is used to represent a rule for controlling the vehicle to switch from a normal driving mode to a performance degradation mode; in response to the confidence level corresponding to the confidence data being less than the second preset confidence level, the control strategy is determined to be a third control strategy, wherein the third control strategy is used to represent a rule for controlling the vehicle to switch from a normal driving mode to a limp mode.

[0158] The sequence numbers of the above embodiments of the present invention are for descriptive purposes only and do not represent the superiority or inferiority of the embodiments.

[0159] In the above embodiments of the present invention, the descriptions of each embodiment have different focuses. For parts not described in detail in a certain embodiment, please refer to the relevant descriptions of other embodiments.

[0160] In the several embodiments provided in this application, it should be understood that the disclosed technical content can be implemented in other ways. The device embodiments described above are merely illustrative; for example, the division of units can be a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the displayed or discussed mutual couplings, direct couplings, or communication connections may be through some interfaces; indirect couplings or communication connections between units or modules may be electrical or other forms.

[0161] The units described as separate components may or may not be physically separate. Similarly, the components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple units. Some or all of the units can be selected to achieve the purpose of this embodiment, depending on actual needs.

[0162] Furthermore, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0163] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this invention, in essence, or the part that contributes to related technologies, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of the various embodiments of this invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, read-only memory (ROM), random access memory (RAM), portable hard drives, magnetic disks, or optical disks.

[0164] The above are merely preferred embodiments of the present invention. It should be noted that those skilled in the art can make various improvements and modifications without departing from the principle of the present invention, and these improvements and modifications should also be considered within the scope of protection of the present invention.

Claims

1. A method for controlling a vehicle, characterized in that, include: Monitor various driving signals generated by the vehicle during operation; In response to the multiple driving signals, including a target driving signal in a failed state, data prediction is performed on the target driving signal through multiple data prediction paths to obtain multiple predicted driving data. The data prediction path is used to represent the path for data prediction on the target driving signal, and the predicted driving data is used to represent the prediction result of the driving data corresponding to the target driving signal. Based on the multiple predicted driving data, a control strategy for the vehicle is determined, wherein the control strategy is used to represent the rules for controlling whether the vehicle maintains a normal driving mode; The control strategy is invoked to control the vehicle's movement.

2. The method according to claim 1, characterized in that, Based on the multiple predicted driving data, a control strategy for the vehicle is determined, including: The multiple predicted driving data are fused to obtain fused driving data; The control strategy is determined based on the fused driving data.

3. The method according to claim 2, characterized in that, The multiple predicted driving data are fused to obtain fused driving data, including: Each of the multiple predicted driving data sets is assigned an initial weight, resulting in multiple initial weights. The initial weights are normalized to obtain multiple target weights. The multiple target weights are used to fuse the multiple predicted driving data to obtain the fused driving data.

4. The method according to claim 3, characterized in that, Using the multiple target weights, the multiple predicted driving data are fused to obtain the fused driving data, including: The predicted driving data corresponding to each of the plurality of target weights is adjusted using each target weight. The multiple adjusted predicted driving data are fused to obtain the fused driving data.

5. The method according to claim 2, characterized in that, Based on the fused driving data, the control strategy is determined, including: Determine confidence data for the fused driving data, wherein the confidence data is used to represent the degree of confidence of the fused driving data for the vehicle; The control strategy is determined based on the confidence data.

6. The method according to claim 5, characterized in that, The method further includes: The state data of the various driving signals are determined respectively to obtain multiple state data, wherein the state data is used to represent the degree of normality of the current state of the driving signal relative to the normal state; Determining the confidence data of the fused driving data includes: determining the correspondence between each state data in the plurality of state data and each target weight in the plurality of target weights, thereby obtaining a plurality of correspondences; adjusting the corresponding state data according to the plurality of correspondences using each target weight; and combining the plurality of adjusted state data to obtain the confidence data.

7. The method according to claim 5, characterized in that, Based on the confidence data, the control strategy is determined, including: In response to the confidence level corresponding to the confidence data being greater than a first preset confidence level, the control strategy is determined as a first control strategy, wherein the first control strategy is used to represent a rule for controlling the vehicle to maintain the normal driving mode; In response to the confidence level corresponding to the confidence data being less than or equal to the first preset confidence level and greater than the second preset confidence level, the control strategy is determined as the second control strategy, wherein the first preset confidence level is greater than the second preset confidence level, and the second control strategy is used to represent the rule for controlling the vehicle to switch from the normal driving mode to the performance degradation mode; In response to the confidence level corresponding to the confidence data being less than the second preset confidence level, the control strategy is determined as a third control strategy, wherein the third control strategy is used to represent the rule for controlling the vehicle to switch from the normal driving mode to the limp mode.

8. A vehicle control device, characterized in that, include: The monitoring unit is used to monitor various driving signals generated by the vehicle during operation; The prediction unit is configured to respond to the multiple driving signals, including a target driving signal in a failed state, by performing data prediction on the target driving signal through multiple data prediction paths to obtain multiple predicted driving data, wherein the data prediction path is used to represent the path for performing data prediction on the target driving signal, and the predicted driving data is used to represent the prediction result of the driving data corresponding to the target driving signal; The first determining unit is configured to determine the control strategy of the vehicle based on the plurality of predicted driving data, wherein the control strategy is used to represent the rules for controlling whether the vehicle maintains a normal driving mode; The control unit is used to invoke the control strategy to control the vehicle's movement.

9. A processor, characterized in that, The processor is used to run a program, wherein the program, when run by the processor, executes the vehicle control method according to any one of claims 1 to 7.

10. A vehicle, characterized in that, include: Memory, which stores executable programs; A processor for running the program, wherein the program, when running, executes the vehicle control method according to any one of claims 1 to 7.

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