Dynamic safety filtering control method and domain control architecture for extreme driving functions

Abstract

A dynamic safety filtering control method and domain control architecture for extreme driving functions. The method comprises: S10, obtaining environment state information and function state information; S20, determining whether a vehicle is approaching a real-time physical constraint boundary; if yes, entering step S50; and if not, entering step S30; S30, determining whether the vehicle has exceeded a dynamic safety boundary; if yes, entering step S50; and if not, entering step S40; S40, completely trusting in an extreme driving control output; S50, calculating a target yaw angle and a target vehicle speed of the vehicle; S60, in light of a real-time physical constraint boundary finite time, intervening in the target yaw angle and / or the target vehicle speed, wherein intervening in the target yaw angle ensures that the vehicle is at a safe distance from the physical constraint boundary, and intervening in the vehicle speed ensures the dynamic stability of the vehicle; and S70, according to the target vehicle speed and the target yaw angle, calculating a safety control quantity.

Description

Dynamic safety filtering control method and domain control architecture for extreme driving functions Technical Field

[0001] The present invention relates to a dynamic safety filtering control method, device and domain control architecture for extreme driving functions, and relates to the field of vehicle extreme driving. Background Art

[0002] According to the World Health Organization, over 90% of traffic accidents are caused by driver error. Among these, accidents resulting in significant casualties primarily occur under extreme driving conditions. Under these conditions, vehicles often experience high speeds and sharp turns, reducing control margins and increasing the risk of instability. With the advancement of autonomous driving and chassis intelligence, some manufacturers have developed features for extreme safety and driving experience in smart cars. Currently, approaches to extreme driving functions are broadly categorized into model-based and data-driven approaches. Model-based approaches typically design a cost function based on performance requirements and minimize it to find the optimal control strategy within the constraints. Data-driven approaches, on the other hand, collect large amounts of input-output data and directly generate control strategies through machine learning and deep learning.

[0003] Model-based or data-driven extreme driving functions prioritize optimizing preset performance while ignoring the potential safety risks inherent in the extreme driving function itself. During the implementation of extreme driving functions, sudden environmental changes, such as obstacles or road surface changes, may occur. In these situations, the preset performance indicators may not meet safety requirements in these sudden environmental changes, and the planned control cannot be adjusted in real time, potentially leading to accidents. Furthermore, extreme driving functions require high real-time and high-precision input signals. Sensor delays, oscillations, or errors can lead to misuse of the function, resulting in vehicle instability.

[0004] Current solutions for extreme driving functions primarily rely on the autonomous decision-making process of taking over vehicle control immediately after the extreme driving control module is triggered. However, these solutions lack safety considerations for the extreme driving function and fail to account for the impact of the vehicle's electrical and electronic architecture. This can lead to unexpected serious accidents due to factors such as sudden environmental changes and misuse of the function. Existing research on extreme driving ignores the potential safety risks inherent in the function itself and fails to address the prevention and control of potential hazards in the event of functional abnormalities, making it prone to unexpected safety incidents.

[0005] Summary of the Invention

[0006] The present invention aims to address at least one of the technical problems existing in the prior art. To address this issue, the present invention provides a dynamic safety filtering method, device, and control architecture for extreme driving functions. These methods are capable of monitoring and analyzing data-driven or model-based extreme driving functions, intervening in vehicle operation based on functional anomalies to ensure the vehicle remains within physical constraints.

[0007] In order to achieve the above-mentioned object of the invention, the technical solution of the present invention is:

[0008] In a first aspect, the present invention provides a dynamic safety filtering control method for extreme driving functions, comprising:

[0009] S10, obtaining environmental status information and vehicle function status information;

[0010] S20, determining whether the vehicle is close to the real-time physical constraint boundary, if yes, proceeding to step S50; if no, proceeding to step S30;

[0011] S30, determining whether the vehicle exceeds the dynamic safety boundary, if yes, proceeding to step S50; if no, proceeding to step S40;

[0012] S40, full trust in extreme driving control output;

[0013] S50, calculating a target yaw angle and a target vehicle speed;

[0014] S60: Intervene in a target yaw angle and / or target vehicle speed for a limited time based on the real-time physical constraint boundary, wherein intervening in the target yaw angle ensures that the vehicle maintains a safe distance from the physical constraint boundary, and intervening in the target vehicle speed ensures dynamic stability of the vehicle;

[0015] S70: Calculate a safety control variable according to the target vehicle speed and the target yaw angle.

[0016] Furthermore, determining whether the vehicle is close to a real-time physical constraint boundary includes:

[0017] The vehicle's proximity is determined based on the distance and relative speed between the vehicle and the physical constraint boundary. The real-time physical constraint boundary refers to a sudden road boundary or an unexpected obstacle in the road that the currently executed function cannot adapt to.

[0018] Furthermore, determining whether the vehicle exceeds the dynamic safety boundary includes:

[0019] The dynamic safety boundary refers to the preset boundaries of the vehicle's yaw rate and sideslip angle in the phase diagram. If the vehicle's current state point is not within the boundary, the vehicle is considered to have exceeded the dynamic safety boundary.

[0020] Furthermore, the calculation of the target yaw angle and target speed of the vehicle includes:

[0021] Establish a vehicle dynamics model to predict the next vehicle state based on the current vehicle state and the action of the extreme driving control module;

[0022] The vehicle speed and yaw angle are selected from the next vehicle state as the target tracking quantity, and the lateral deviation of the Frenet coordinate system is selected as the tracking reference quantity:

[0023] Where, are respectively the expected vehicle speed, the expected yaw rate, and the expected normal speed along the road, s t is the current vehicle state, a input is the input before filtering, s t+1 The state of the next moment.

[0024] Furthermore, the limited-time intervention of the target yaw angle and / or target vehicle speed in combination with the real-time physical constraint boundary includes:

[0025] Combined control barrier function and increasing time scaling function Φ T Intervene in the target yaw angle to limit the impact of the approaching physical constraint boundary on the extreme driving control module to a limited time T:

[0026] Determine the risk of vehicle instability in real time and reduce the target speed v d Get v safe ;

[0027] Where, is the expected yaw rate, l d is the expected normal speed along the road, l is the actual normal speed along the road, v is the total speed of the vehicle, ∈, b is a positive parameter, s p To control the maximum distance of safety filter intervention, β is the vehicle's sideslip angle, T is the preset finite time, t is the actual time after the filter is triggered, and n is the order.

[0028] In a second aspect, the present invention further provides a dynamic safety filtering control system for extreme driving functions, the system comprising:

[0029] An information acquisition module is configured to obtain environmental status information and functional status information;

[0030] A physical constraint boundary judgment module determines whether the vehicle is close to the real-time physical constraint boundary;

[0031] a dynamic safety boundary judgment module, configured to determine whether the vehicle exceeds the dynamic safety boundary;

[0032] The output module is configured to fully trust the extreme driving output if the vehicle is not close to the real-time physical constraint boundary;

[0033] a target quantity calculation module, configured to calculate a target yaw angle and a target vehicle speed;

[0034] A target variable intervention module is configured to intervene in the target variable for a limited time based on boundary information. The module intervenes in the target yaw angle to ensure that the vehicle maintains a safe distance from the physical constraint and in the vehicle speed to ensure vehicle dynamic stability. If the vehicle state exceeds the dynamic control safety boundary, the module simultaneously performs countersteering and deceleration to stabilize the vehicle.

[0035] The control signal output module is configured to calculate a safety control variable according to a target vehicle speed and a target yaw angle.

[0036] In a third aspect, the present invention provides an intelligent vehicle domain control structure for extreme driving functions, the domain control structure comprising an autonomous driving system and an intelligent chassis system; the autonomous driving system is provided with an extreme driving comprehensive decision-making module and a fusion perception module, and the intelligent chassis system is provided with the dynamic safety filtering control system, the extreme driving control module and the conventional chassis dynamics controller according to the second aspect of the present invention; wherein,

[0037] The extreme driving comprehensive decision-making module is configured to block the vehicle's original stability control function, analyze whether there are unexpected changes in the environment that are not considered by the executed function based on the current and historical information obtained by the fusion perception module, and feed back the function status to the dynamic safety filtering control system;

[0038] The extreme driving control module, after being triggered by the extreme driving comprehensive decision module, inputs the control amount into the dynamic safety filter control system for intervention and takes over the conventional chassis dynamics controller in a linear or inertial transition manner;

[0039] The dynamic safety filtering control system evaluates the behavior and status of the extreme driving function and determines the adaptability of the function to the current environment. If the vehicle is not approaching physical constraints, it will directly output through the extreme driving control module; otherwise, it will use dynamic adaptive filtering to filter out operations that may cause vehicle instability and violate physical constraints.

[0040] Furthermore, the automatic driving system also includes an automatic driving system decision planning and trajectory tracking module. When the fusion perception module feeds back that the vehicle begins to move away from the newly added physical constraint boundary, the extreme driving comprehensive decision module transmits the normal environment and functional status to the dynamic safety filtering control system, and directly trusts the output of the extreme driving control module until the extreme driving control module completes the extreme driving task with predetermined performance. The extreme driving comprehensive decision module returns the control authority to the automatic driving system decision planning and trajectory tracking module, and the conventional chassis dynamics controller controls the steering system, drive system and braking system to perform the target operation. At this time, the extreme driving function exits.

[0041] Furthermore, the extreme driving control module includes sequential decision-making and optimal control problems for optimizing preset extreme driving performance, optimizing preset performance indicators in the form of reward functions or cost functions, and solving front wheel angle and driving and braking targets driven by data or models.

[0042] Furthermore, the condition for triggering the extreme driving control module by the extreme driving comprehensive decision-making module is: the extreme driving comprehensive decision-making module monitors the vehicle's operating status and dynamic status in real time during normal driving to see whether they are close to the safety boundary, and determines whether the function currently performed by the vehicle involves the adhesion limit. If it involves the adhesion limit, the extreme driving control module is triggered.

[0043] The present invention adopts the above technical solution, which has the following characteristics:

[0044] 1. The present invention focuses on the potential safety risks of extreme driving functions themselves. It can monitor and analyze data-driven or model-solved extreme driving functions, and intervene in vehicle operations based on functional abnormalities to ensure that the vehicle is within the physical constraints, thereby avoiding excessive sacrifice of preset performance to the greatest extent.

[0045] 2. The emergence and development of the extreme driving function of the present invention has broadened the dynamic control boundaries of the vehicle, greatly improved driving safety and driving experience, and at the same time improved the reliability and adaptability of the function itself.

[0046] 3. Based on the concept of functional safety, the present invention aims to operate at a preset performance degradation level while ensuring safety when an abnormality occurs in the extreme driving function or when facing unknown dangers, thereby ensuring operational safety and having broad application prospects.

[0047] 4. The dynamic safety filtering method proposed in the present invention limits the intervention of functions to a limited time, thereby reducing the impact on the overall driving performance.

[0048] In summary, the present invention can be widely applied to vehicle extreme driving. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] Various other advantages and benefits will become apparent to those skilled in the art upon reading the detailed description of the preferred embodiment below. The accompanying drawings are for illustration purposes only and are not to be considered as limiting the present invention. Throughout the drawings, the same reference numerals are used to denote the same components. In the drawings:

[0050] FIG1 is a flow chart of a dynamic safety filtering control method for extreme driving functions provided by an embodiment of the present invention.

[0051] FIG2 is a schematic diagram of an intelligent vehicle domain control architecture adapted to extreme driving functions provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0052] It should be understood that the terms used herein are for the purpose of describing specific example embodiments only and are not intended to be limiting. Unless the context clearly indicates otherwise, the singular forms "one", "an" and "said" as used herein may also be meant to include plural forms. The terms "comprise", "include", "contain" and "have" are inclusive and therefore specify the presence of stated features, steps, operations, elements and / or parts, but do not exclude the presence or addition of one or more other features, steps, operations, elements, parts, and / or combinations thereof. The method steps, processes, and operations described herein are not to be construed as necessarily requiring them to be performed in the specific order described or illustrated, unless the order of execution is clearly indicated. It should also be understood that additional or alternative steps may be used.

[0053] Although the terms first, second, third, etc. can be used in the text to describe multiple elements, components, regions, layers and / or sections, these elements, components, regions, layers and / or sections should not be limited by these terms. These terms can only be used to distinguish an element, component, region, layer or section from another region, layer or section. Unless the context clearly indicates otherwise, terms such as "first", "second" and other numerical terms do not imply order or sequence when used in the text. Therefore, the first element, component, region, layer or section discussed below can be referred to as the second element, component, region, layer or section without departing from the teaching of the example embodiments.

[0054] For ease of description, spatially relative terms may be used herein to describe the relationship of one element or feature relative to another element or feature as shown in the figures, such as "inside," "outside," "inner side," "outer side," "lower," "upper," etc. Such spatially relative terms are intended to encompass different orientations of the device in use or operation in addition to the orientation depicted in the figures.

[0055] Existing model-based or data-driven extreme driving functions focus on optimizing the performance of the function's presets, but neglect the safety risks inherent in the extreme driving function itself, or the prevention and control of potential hazards in the event of functional anomalies. This can easily lead to unexpected safety accidents. The present invention provides a dynamic safety filtering method and device for extreme driving functions, as well as an intelligent vehicle domain control architecture. These methods can monitor and analyze data-driven or model-based extreme driving functions, and intervene in vehicle operations based on functional anomalies to ensure the vehicle remains within its physical constraints.

[0056] The dynamic safety filtering method, system, and intelligent vehicle domain control architecture for extreme driving functions proposed in this invention are designed for data-driven or model-based extreme driving functions and have the following characteristics:

[0057] (1) Behavior monitoring and analysis: By combining the cloud control system, autonomous driving system, and intelligent chassis system, behavior monitoring and analysis of extreme driving functions are performed to understand the system's working status and driving behavior in real time;

[0058] (2) Dynamic safety filtering: Adaptive hybrid control is combined with classic switching control to intervene in the output and behavior of the extreme driving module in the form of dynamic safety filtering, which can solve the functional safety challenges caused by factors such as sudden changes in the environment and misuse of functions.

[0059] (3) Limited time intervention: By combining the control obstacle function with the incremental time scale function, the intervention of the dynamic safety filter on the main control module is limited to a limited time, so as to reduce the impact of the dynamic safety filter on the overall driving performance.

[0060] In summary, the present invention fully considers the impact on the overall driving performance while ensuring the safety and stability of the extreme driving function, and provides an effective safety protection solution for the extreme driving function.

[0061] Exemplary embodiments of the present invention will be described in more detail below with reference to the accompanying drawings. Although exemplary embodiments of the present invention are shown in the accompanying drawings, it should be understood that the present invention can be implemented in various forms and should not be limited by the embodiments described herein. Rather, these embodiments are provided to enable a more thorough understanding of the present invention and to fully convey the scope of the present invention to those skilled in the art.

[0062] Example 1: As shown in FIG1 , the dynamic safety filtering control method for extreme driving functions provided in this embodiment includes:

[0063] S10, Environmental status and extreme driving function status analysis

[0064] In this embodiment, environmental status information such as pedestrian approach information, obstacle information, road adhesion coefficient, vehicle position and posture information, and extreme driving function status information are obtained, including but not limited to the currently executed automatic driving function, active safety function and stability function, and it is analyzed whether there are unexpected changes in the environment that are not taken into account by the functions executed by extreme driving.

[0065] S20, determining whether the vehicle is close to the real-time physical constraint boundary, if yes, proceeding to step S50; if no, proceeding to step S30;

[0066] In this embodiment, the physical constraint boundary refers to a sudden change in the road boundary or an unexpected obstacle in the road to which the currently executed function cannot adapt.

[0067] In this embodiment, the extreme driving fusion perception module and the V2X collaborative environmental perception system determine physical constraints in real time and judge the degree of vehicle proximity based on factors such as the distance between the vehicle and the physical constraint boundary and the relative movement speed.

[0068] S30, determining whether the vehicle exceeds the dynamic safety boundary, if yes, proceeding to step S50; if no, proceeding to step S40;

[0069] In this embodiment, the dynamic safety boundary refers to the preset boundaries of the vehicle's yaw rate and center of mass sideslip angle in the power system phase diagram. If the point of the vehicle's current state is not within the boundary, the vehicle is considered to have exceeded the dynamic safety boundary.

[0070] S40, full trust in the extreme driving control output.

[0071] S50, calculating the target speed, yaw angle, and lateral displacement of the vehicle;

[0072] In this embodiment, the target speed, yaw angle, and lateral displacement of the vehicle are calculated, including:

[0073] Establish a high-precision vehicle dynamics model to predict the next vehicle state based on the current vehicle state and the action of the extreme driving control module;

[0074] The vehicle speed and yaw angle are selected from the next vehicle state as the target tracking quantity, and the lateral deviation of the Frenet coordinate system is selected as the tracking reference quantity:

[0075] Where, are respectively the expected vehicle speed, the expected yaw rate, and the expected normal speed along the road, s t is the current vehicle state, a input is the input before filtering, s t+1 The state of the next moment.

[0076] It should be noted that the vehicle dynamics model is applicable to but not limited to 2-15 degree of freedom vehicle dynamics models, vehicle models built into commercial software such as Carsim, etc., and can be selected according to actual needs without limitation here.

[0077] S60, combining boundary information with limited time intervention target amount;

[0078] In this embodiment, dynamic safety interventions are performed on the target tracking parameters, including interventions on the target yaw angle to ensure the vehicle maintains a safe distance from the physical constraint boundary and interventions on vehicle speed to ensure dynamic stability. If the vehicle state exceeds the dynamic control safety boundary, countersteering and speed reduction are simultaneously implemented to stabilize the vehicle.

[0079] First, combined with the control barrier function and increasing time scaling function Φ T The target yaw angle is intervened to limit the impact of the approaching physical constraint boundary on the extreme driving control module to a limited time T, thereby minimizing the negative impact on the overall driving performance.

[0080] Where, is the expected yaw rate, l d is the expected normal speed along the road, l is the actual normal speed along the road, v is the total speed of the vehicle, ∈, b is a positive parameter, s p To control the maximum distance of safety filter intervention, β is the vehicle's sideslip angle, T is the preset finite time, t is the actual time after the filter is triggered, and n is the order.

[0081] Secondly, for the risk of dynamic instability caused by factors such as sudden changes in road surface, the vehicle state instability risk is determined in real time, and the target speed v is reduced. d Get v safe And limiting measures such as limiting the front wheel turning angle.

[0082] Finally, the safety control quantity a is calculated through feedback control based on the target quantity and the current state. safe :

[0083] Where a st The deceleration and steering inputs applied to stabilize the vehicle can usually be obtained by looking up the table; It is the input obtained by tracking the corrected target quantity.

[0084] S70: Track the target quantity to obtain a safety control signal.

[0085] In this embodiment, the target vehicle speed and target yaw angle are tracked, and the control variable is calculated through feedback control such as PID control and sliding mode control, and the control signal is sent to the vehicle braking system, drive system, steering system and suspension system.

[0086] Example 2: The above-mentioned Example 1 provides a dynamic safety filtering control method for extreme driving functions. Correspondingly, this embodiment provides a dynamic safety filtering control system for extreme driving functions. The device provided in this embodiment can implement the dynamic safety filtering control method for extreme driving functions of Example 1. The device can be implemented by software, hardware, or a combination of software and hardware. For the convenience of description, this embodiment is described by dividing the functions into various units and describing them separately. Of course, the functions of each unit can be implemented in the same or multiple software and / or hardware during implementation. For example, the device may include integrated or separate functional modules or functional units to execute the corresponding steps in each method of Example 1. Since the device of this embodiment is basically similar to the method embodiment, the description process of this embodiment is relatively simple. For relevant points, please refer to the partial description of Example 1. The embodiment of the dynamic safety filtering control system for extreme driving functions provided by the present invention is only illustrative.

[0087] Specifically, the present invention further provides a dynamic safety filtering control system for extreme driving functions, the system comprising:

[0088] An information acquisition module is configured to obtain environmental status information and functional status information;

[0089] A physical constraint boundary judgment module determines whether the vehicle is close to the real-time physical constraint boundary;

[0090] a dynamic safety boundary judgment module, configured to determine whether the vehicle exceeds the dynamic safety boundary;

[0091] The output module is configured to fully trust the extreme driving output if the vehicle is not close to the real-time physical constraint boundary;

[0092] a target quantity calculation module, configured to calculate a target yaw angle and a target vehicle speed;

[0093] A target variable intervention module is configured to intervene in the target variable for a limited time based on boundary information. The module intervenes in the target yaw angle to ensure that the vehicle maintains a safe distance from the physical constraint and in the vehicle speed to ensure vehicle dynamic stability. If the vehicle state exceeds the dynamic control safety boundary, the module simultaneously performs countersteering and deceleration to stabilize the vehicle.

[0094] The control signal output module is configured to calculate a safety control variable according to a target vehicle speed and a target yaw angle.

[0095] In a preferred embodiment of the present invention, for cars that are not equipped with high-level autonomous driving, the dynamic safety filtering control system proposed by the present invention is also applicable to the analysis, monitoring and intervention of the driver's radical extreme operations.

[0096] Example 3: As shown in Figure 2, the intelligent vehicle domain control architecture for extreme driving functions provided in this embodiment includes an automatic driving system 1, a chassis domain controller 2 (intelligent chassis system) and a cloud control system 3.

[0097] The autonomous driving system 1 is equipped with an extreme driving comprehensive decision module 11, a fusion perception module 12 and an autonomous driving system decision planning and trajectory tracking module 13; the chassis domain controller 2 is equipped with an extreme driving control module 21, a dynamic safety filtering control system 22 and a conventional chassis dynamics controller 23; the cloud control system 3 is equipped with a V2X collaborative environment perception module 31 and a cloud computing platform 32;

[0098] The extreme driving comprehensive decision-making module 11 is configured to monitor the vehicle's operating and dynamic states in real time during normal driving to determine whether they are approaching safety boundaries. It determines whether the current vehicle function involves adhesion limits and, if so, triggers the extreme driving control module 21. It then coordinates the allocation of computing power among the cloud computing platform 32, the vehicle computing unit, and the chassis domain controller 2 based on the computing power required for the extreme driving function. During the execution of the extreme driving function, the extreme driving comprehensive decision-making module 11 disables the vehicle's existing stability control function and, based on current and historical information acquired by the fusion perception module 12, analyzes whether there are unexpected changes in the environment that were not considered by the executed function. This includes both the environment and the vehicle itself, as well as the behavior and status of the extreme driving control module. The module then provides feedback on the function's state (e.g., how the function adapts to the environment and itself, such as a sudden change in the environment or a partial or severe failure of the braking system when the function requires it) to the dynamic safety filtering control system 22 in the chassis domain controller 2.

[0099] The extreme driving control module 21, after being triggered by the extreme driving comprehensive decision module 11, inputs the control variable into the dynamic safety filter control system 22 for safety intervention, and takes over the conventional chassis dynamics controller 23 in a linear or inertial transition manner;

[0100] The dynamic safety filtering control system 22 evaluates the behavior and status of the extreme driving function and determines the adaptability of the function to the current environment. For example, if the vehicle is not close to the physical constraint boundary, it will be directly output through the extreme driving control module 212; otherwise, dynamic adaptive filtering will be used to filter out operations that may cause vehicle instability and violate physical constraints.

[0101] In a preferred embodiment of the present invention, the extreme driving control module 21 uses a data-driven reinforcement learning algorithm, and the output control variable includes the front wheel angle δf , throttle opening and brake pedal opening τ, τ>0 represents the throttle opening, when τ>0, take |τ| as the brake pedal opening, the vehicle state includes the horizontal and vertical coordinates x of the vehicle in the Frenet coordinate system t ,l, vehicle speed v s ,v l , yaw angle

[0102] Furthermore, the extreme driving control module 21 includes sequential decision-making and optimal control problems for optimizing preset extreme driving performance, optimizes preset performance indicators in the form of reward functions or cost functions, and solves control quantities such as front wheel angle, driving and braking targets driven by data or models.

[0103] Furthermore, the extreme driving control module 21 includes but is not limited to performance-driven optimal control such as linear / nonlinear model predictive control algorithms, and selection of different reinforcement learning algorithms and their corresponding improved algorithms according to actual conditions, which will not be elaborated here.

[0104] In a preferred embodiment of the present invention, the extreme driving function's exit mechanism is as follows: when the fusion perception module 11 reports that the vehicle is beginning to move away from a newly added physical constraint boundary (e.g., when the distance between the vehicle and the physical constraint boundary increases or the relative velocity becomes negative), the extreme driving integrated decision module 11 transmits the normal environmental and functional status to the dynamic safety filtering control system 22, directly trusting the output of the extreme driving control module 21. Once the extreme driving control module 21 completes the extreme driving task with the predetermined performance, the extreme driving integrated decision module 11 returns control authority to the automated driving system decision planning and trajectory tracking module 13, and the conventional chassis dynamics controller 23 controls the steering, drive, and braking systems to execute the target operation.

[0105] In a preferred embodiment of the present invention, the fusion perception module 11 obtains current and historical information through the sensor combination 4 supporting the extreme driving function, including lidar signals, combined inertial navigation signals, wheel speed signals, and vehicle dynamics control signals including feedback signals of the drive system, braking system, steering system, and suspension system.

[0106] The following describes in detail the workflow of the intelligent vehicle domain control architecture for extreme driving functions of this embodiment through specific examples.

[0107] This embodiment assumes that the environment changes suddenly and a pedestrian approaches the vehicle that is at its adhesion limit. However, the original extreme driving function cannot adapt to the change in the physical constraint boundary due to its limited generalization. a input =[δ f ,τ]

[0108] Where x,l are the coordinates of the vehicle along the tangential and normal directions of the road; δ f is the front wheel steering, τ is the throttle opening, v s ,v l are the vehicle's velocity components along the tangential and normal directions of the road, respectively.

[0109] When no probe pedestrian is detected, the extreme driving function operates normally. The extreme driving integrated decision module 11 selects the extreme driving function, and the chassis domain controller 2 takes over the conventional chassis dynamics controller 23 to execute the data-driven extreme driving control strategy. The dynamic safety filter control system 22 does not detect that the vehicle is approaching the physical constraint boundary, nor does it detect that the vehicle is at risk of instability beyond the dynamic safety boundary. Therefore, the extreme driving control output is fully trusted.

[0110] When the sensor assembly 4 supporting extreme driving and the V2X collaborative environmental perception module 31 fuse perception and calculation to obtain information about approaching pedestrians, they transmit obstacle information, road adhesion coefficient, vehicle position and posture to the extreme driving comprehensive decision module 11. The extreme driving comprehensive decision module 11 transmits information about sudden environmental changes and functional abnormalities to the dynamic safety filtering control system 22. The dynamic safety filtering control system 22 determines that the vehicle is approaching the real-time physical constraint boundary and calculates the vehicle's target speed, yaw angle, and lateral displacement. Combined with the boundary information, it intervenes in the target quantity for a limited time and tracks the target quantity to obtain a safety control signal, specifically:

[0111] First, the current vehicle state s t And input a before filtering input Substitute into the vehicle dynamics model and calculate the state s at the next moment t+1 Since the state selected in this embodiment is in the Frenet coordinate system, it should be converted to the Cartesian coordinate system for dynamic model calculation, and then converted into Frenet coordinates. The vehicle speed, yaw angle and lateral deviation are selected as the tracking target.

[0112] Secondly, the target quantity is intervened in combination with the newly added physical constraints.

[0113] Since this embodiment mainly involves the addition of new physical constraints (s n ,l n ), the target yaw angle should be To avoid the new physical constraints being less than the safe distance s m , this embodiment designs the following control obstacle function Δs 2 =(xx n ) 2 +(lln ) 2

[0114] Where k r is a positive parameter that adjusts the degree of intervention of the dynamic safety filter control system on the extreme driving control module, where (x n ,l n ) is the real-time coordinate of the probe pedestrian.

[0115] In order to limit the impact of the new constraint on the extreme driving control module within the time T, this embodiment combines the increasing time scaling function Φ T , get the target yaw angle after intervention Since the increasing time scaling function Φ T The existence of the obstacle control function The impact time on the target tracking volume is limited to T.

[0116] Where,∈,b is a positive parameter, s p To control the maximum distance of safety filter intervention, β is the vehicle's side slip angle.

[0117] Since there are no factors that cause vehicle instability in this embodiment, the extreme driving comprehensive decision module 11 determines that the vehicle is within the dynamic safety boundary. The dynamic safety filtering control system 22 does not intervene in the target speed, that is, v t =v d , get the target tracking amount after intervention

[0118] Then, the safety control quantity a is calculated through feedback control based on the target quantity and the current state. safe =[δ fs ,τ s ]. The method selected in this embodiment is model-based sliding mode control.

[0119] Finally, the control quantity a safe The calculated control signals are converted into control signals and transmitted to the steering, drive, and braking systems via the vehicle's control-by-wire system to execute control operations. Furthermore, the intelligent vehicle in this embodiment can be equipped with an active suspension system that adjusts the suspension state based on the calculated control signals and the vehicle's vehicle state, improving handling stability during extreme driving.

[0120] Each embodiment in this specification is described in a progressive manner, and the same or similar parts between the embodiments can be referred to each other, and each embodiment focuses on the differences from other embodiments. In the description of this specification, the reference terms "a preferred embodiment", "further", "specifically", "in the present embodiment", etc. mean that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the embodiment of this specification. In this specification, the schematic representation of the above terms does not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described can be combined in a suitable manner in any one or more embodiments or examples. In addition, those skilled in the art can combine and combine the different embodiments or examples described in this specification and the features of different embodiments or examples, unless they are contradictory.

[0121] The present application is described with reference to the flow chart and / or block diagram of the method, equipment (device) and computer program product according to the embodiment of the present application. It should be understood that each flow process and / or box in the flow chart and / or block diagram and the combination of the flow process and / or box in the flow chart and / or block diagram can be realized by computer program instructions. These computer program instructions can be provided to the processor of a general-purpose computer, a special-purpose computer, an embedded processing machine or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device produce a device for realizing the function specified in one flow chart flow or multiple flows and / or one box or multiple boxes of the block diagram.

[0122] These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce a product including an instruction device that implements the functions specified in one or more processes in the flowchart and / or one or more boxes in the block diagram.

[0123] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, so that the instructions executed on the computer or other programmable device provide steps for implementing the functions specified in one or more processes in the flowchart and / or one or more boxes in the block diagram.

[0124] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the various embodiments of the present invention.

Claims

1. A dynamic safety filtering control method for extreme driving functions, characterized in that: include: S10, obtaining environmental status information and vehicle extreme driving function status information; S20, determining whether the vehicle is close to the real-time physical constraint boundary, if yes, proceeding to step S50; if no, proceeding to step S30; S30, determining whether the vehicle exceeds the dynamic safety boundary, if yes, proceeding to step S50; if no, proceeding to step S40; S40, full trust in the extreme driving control output; S50, calculating a target yaw angle and a target vehicle speed of the vehicle; S60, intervening in a target yaw angle and / or a target vehicle speed in combination with a boundary limited time, wherein intervening in the target yaw angle ensures that the vehicle is at a safe distance from the physical constraint boundary, and intervening in the target vehicle speed ensures dynamic stability of the vehicle; S70: Calculate a safety control amount according to the target vehicle speed and the target yaw angle.

2. The dynamic safety filtering control method for extreme driving functions according to claim 1 is characterized in that: The determining whether the vehicle is close to a real-time physical constraint boundary includes: The proximity of the vehicle is determined based on the distance between the vehicle and the physical constraint boundary and the relative movement speed. The real-time physical constraint boundary refers to the sudden road boundary or sudden obstacle in the road to which the current function cannot adapt.

3. The dynamic safety filtering control method for extreme driving functions according to claim 1 is characterized in that: The determining whether the vehicle exceeds the dynamic safety boundary includes: The dynamic safety boundary refers to the preset boundary of the vehicle's yaw rate and center of mass sideslip angle in the phase diagram. If the point of the vehicle's current state is not within the boundary, the vehicle is considered to have exceeded the dynamic safety boundary.

4. The dynamic safety filtering control method for extreme driving functions according to claim 1, characterized in that: The calculating of the target yaw angle and target vehicle speed of the vehicle includes: Establish a vehicle dynamics model to predict the next vehicle state based on the current state of the vehicle and the action of the extreme driving control module; The vehicle speed and yaw angle are selected from the next vehicle state as the target tracking quantity, and the lateral deviation of the Frenet coordinate system is selected as the tracking reference quantity: In the formula, are respectively the expected vehicle speed, the expected yaw rate, and the expected normal speed along the road, s t is the current vehicle state, a input is the input before filtering, s t+1 The state of the next moment.

5. The dynamic safety filtering control method for extreme driving functions according to claim 1, characterized in that: The method of intervening the target yaw angle and / or the target vehicle speed in a limited time in combination with the real-time physical constraint boundary includes: Combined control barrier function and the increasing time scaling function Φ T Intervene the target yaw angle to limit the impact of the approaching physical constraint boundary on the extreme driving control module to a limited time T: Determine the risk of vehicle instability in real time and reduce the target vehicle speed v d Get v safe ; In the formula, is the expected yaw rate, l d is the estimated normal speed along the road, l is the actual normal speed along the road, v is the total speed of the vehicle, ∈, b is a positive parameter, s p To control the maximum distance of safety filter intervention, β is the sideslip angle of the vehicle center of mass, T is the preset finite time, t is the actual time after the filter is triggered, and n is the order.

6. A dynamic safety filtering control system for extreme driving functions, characterized in that: The system includes: An information acquisition module, configured to obtain environmental status information and functional status information; A physical constraint boundary judgment module determines whether the vehicle is close to the real-time physical constraint boundary; a dynamic safety boundary determination module, configured to determine whether the vehicle exceeds the dynamic safety boundary; The output module is configured to fully trust the extreme driving output if the vehicle is not close to the real-time physical constraint boundary; A target quantity calculation module is configured to calculate a target yaw angle and a target vehicle speed of the vehicle; A target quantity intervention module is configured to intervene in the target quantity for a limited time in combination with the boundary information, wherein the target yaw angle is intervened to ensure that the vehicle has a safe distance from the physical constraint, and the vehicle speed is intervened to ensure the dynamic stability of the vehicle. If the vehicle state has exceeded the dynamic control safety boundary, the reverse steering and deceleration are simultaneously performed to stabilize the vehicle; The control signal output module is configured to calculate a safety control amount according to a target vehicle speed and a target yaw angle.

7. An intelligent vehicle domain control structure for extreme driving functions, characterized in that: The domain control structure includes an automatic driving system and an intelligent chassis system; the automatic driving system is provided with an extreme driving comprehensive decision-making module and a fusion perception module, and the intelligent chassis system is provided with a dynamic safety filtering control system as claimed in claim 6, an extreme driving control module and a conventional chassis dynamics controller; wherein, The extreme driving comprehensive decision module is configured to shield the original stability control function of the vehicle, analyze whether there are unexpected changes in the environment that are not considered by the executed function based on the current and historical information obtained by the fusion perception module, and feed back the function status to the dynamic safety filtering control system; The extreme driving control module, after being triggered by the extreme driving comprehensive decision module, inputs the control amount into the dynamic safety filter control system for intervention, and takes over the conventional chassis dynamics controller in a linear or inertial transition manner; The dynamic safety filtering control system evaluates the behavior and status of the extreme driving function and determines the adaptability of the function to the current environment. If the vehicle is not close to the physical constraints, it will be directly output through the extreme driving control module; otherwise, it will filter out operations that may cause vehicle instability and violate physical constraints through dynamic adaptive filtering.

8. The intelligent vehicle domain control structure according to claim 7, characterized in that: The automatic driving system also includes an automatic driving system decision planning and trajectory tracking module. When the fusion perception module feedbacks that the vehicle begins to move away from the newly added physical constraint boundary, the extreme driving comprehensive decision module transmits the normal environment and functional status to the dynamic safety filtering control system, and directly trusts the output of the extreme driving control module until the extreme driving control module completes the extreme driving task with predetermined performance. The extreme driving comprehensive decision module returns the control authority to the automatic driving system decision planning and trajectory tracking module, and the conventional chassis dynamics controller controls the steering system, drive system and braking system to perform the target operation, at which time the extreme driving function exits.

9. The intelligent vehicle domain control structure according to claim 7 or 8, characterized in that: The extreme driving control module includes sequential decision-making and optimal control problems for optimizing preset extreme driving performance, optimizing preset performance indicators in the form of reward functions or cost functions, and solving front wheel steering angles and driving and braking targets driven by data or models.

10. The intelligent vehicle domain control structure according to claim 7, characterized in that: The condition that the extreme driving control module is triggered by the extreme driving comprehensive decision-making module is that the extreme driving comprehensive decision-making module monitors the vehicle's operating state and dynamic state in real time during normal driving to see whether they are close to the safety boundary, and determines whether the function currently performed by the vehicle involves the adhesion limit. If it involves the adhesion limit, the extreme driving control module is triggered.

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