Two-wheeled self-balancing vehicle scenario-based balance control and functional safety hierarchical degradation method, system and vehicle

Abstract

The application discloses a two-wheeled self-balancing vehicle scene-based balance control and function safety hierarchical degradation method and system and a vehicle, belongs to the technical field of two-wheeled intelligent vehicle automatic control and function safety, and comprises the following steps: identifying remote calling, autonomous parking, vehicle following and parking standby scenes; according to the identified scene type, matching and switching the corresponding balance control strategy, and configuring the scene-specific speed limit envelope and the steering limit envelope; the application adopts a scene-based hierarchical balance regulation and control architecture, a full-link fault health monitoring and a progressive hierarchical safety degradation mechanism, takes into account the use requirements of commercial shared vehicles in complex outdoor road conditions, multi-passenger and multi-cargo and cross-region regulation adaptation, simultaneously constructs a complete safety protection system from normal operation to fault bottom, and greatly improves the operation stability, environmental adaptability and whole-vehicle operation safety bottom line of the unmanned two-wheeled balancing vehicle.

Description

Technical Field

[0001] This invention belongs to the field of automatic control and functional safety technology for two-wheeled intelligent vehicles, specifically referring to the scenario-based balance control and functional safety classification and degradation method, system, and vehicle for two-wheeled self-balancing vehicles. Background Technology

[0002] With the development of intelligent and shared short-distance travel, two-wheeled self-balancing vehicles equipped with remote summoning, autonomous parking, and automatic following functions are gradually being commercialized. These products need to operate autonomously at low speeds and without human intervention for extended periods, while also meeting the relevant control requirements for road vehicle functional safety.

[0003] However, existing scenario-based balance control and functional safety classification and degradation systems for two-wheeled self-balancing vehicles still have certain shortcomings. Existing technologies rely on low-speed wheel correction and lack inertia stabilization, load pre-buffering, slope anti-saturation, and disturbance adaptive adjustment capabilities, making the vehicle body prone to swaying and imbalance. They can only detect local sensor faults and do not fully cover actuators and communication links. They lack multi-source fusion fault classification and directly cut off power after a fault, lacking graded buffering and degradation and corresponding minimum risk handling actions. Various control logics are independent and cannot cooperate in a closed loop. The fault threshold is fixed and cannot be adjusted, making it difficult to adapt to different vehicle models and local regulations. The anti-interference and fault protection performance is insufficient, and it cannot meet the high-safety operation requirements of commercial unmanned vehicles in multiple scenarios. Therefore, this paper proposes a scenario-based balance control and functional safety classification and degradation method, system, and vehicle for two-wheeled self-balancing vehicles. Summary of the Invention

[0004] The purpose of this invention is to provide a method, system, and vehicle for scenario-based balance control and functional safety classification and degradation of two-wheeled self-balancing vehicles, so as to solve the problems mentioned in the background art.

[0005] To achieve the above objectives, the present invention provides the following technical solution: a method, system and vehicle for scenario-based balance control and functional safety classification and degradation of two-wheeled self-balancing vehicles, including identifying unmanned low-speed operation scenarios such as remote call, autonomous parking, vehicle following, and parking standby; matching and switching the corresponding balance control strategy according to the identified scenario type, and configuring scenario-specific speed limit envelopes and steering limit envelopes.

[0006] Preferably, in the unmanned low-speed scenario, a rotational inertia-type balancing device is used to maintain the vehicle's balance, and a matching speed constraint envelope and steering constraint envelope are applied to the corresponding scenario, while a vehicle oscillation suppression control strategy is executed simultaneously.

[0007] Preferably, when detecting sudden load changes caused by passenger boarding / alighting or cargo loading / unloading, the ground support mechanism is pre-intervened in control in advance, and the balance control gain is feedforward corrected to suppress the vehicle body attitude disturbance generated during passenger boarding / alighting.

[0008] Preferably, when the vehicle is parked on a cross slope or ramp, periodic momentum unloading or support linkage is performed based on continuous disturbance estimation to prevent unilateral drift saturation of the frame corner.

[0009] Preferably, it estimates external disturbances caused by crosswinds and road impacts in real time and offsets the impact of disturbances through feedforward compensation; it identifies different load states such as single-passenger, two-person, and cargo-carrying in real time online and adaptively corrects balance control parameters and scene strategy switching thresholds.

[0010] Preferably, health monitoring and multi-sensor fusion confidence assessment are performed on sensors, actuators, and communications to determine the fault level.

[0011] Preferably, a step-by-step functional safety degradation process is executed based on the determined fault level, with the degradation order as follows: limited balance performance - forced speed and steering constraints - forced safe stopping of the entire vehicle - complete landing and locking of the ground support mechanism.

[0012] Preferably, each functional degradation level is pre-configured with a corresponding minimum risk maneuver (MRM) strategy, which includes two types of safety actions: slowing down by pulling over and parking with stable support.

[0013] Preferably, the function degradation decision logic is executed in conjunction with the vehicle control transfer strategy and the ramp saturation momentum unloading strategy to form a closed-loop control logic for vehicle scenario balance plus fault degradation.

[0014] Preferably, it includes a scene recognition module, a balance control module, a health monitoring module, a graded degradation decision-making module, a minimum risk maneuver execution module, and a ground support execution module; The modules within the system work together to achieve the control method; The fault degradation thresholds within the graded degradation system are calibrated and configured based on vehicle parameters and target market regulations and standards.

[0015] Compared with the prior art, the beneficial effects of the present invention are: 1. This invention combines a scenario-based hierarchical balance control architecture with full-link fault health monitoring and a progressive graded safety degradation mechanism. It integrates multiple technical means such as rotational inertia stability compensation, predictive load disturbance suppression, long-term slope anti-saturation control, unified handling of multi-source hardware communication faults, and multi-module collaborative closed-loop management. It takes into account the usage needs of commercial shared vehicles in complex outdoor road conditions, multi-passenger and multi-cargo carrying, and cross-regional regulatory adaptation. At the same time, it builds a complete safety protection system from normal operation to fault protection, which greatly improves the operational stability, environmental adaptability and overall vehicle safety baseline of unmanned two-wheeled balance vehicles. 2. This invention accurately distinguishes various unmanned low-speed operation scenarios and matches each scenario with exclusive balance control logic and independent motion constraint range. At the same time, it designs predictive feedforward adjustment and long-term anti-saturation control logic for load changes, slope static offset, external airflow and road impact, which can actively predict load fluctuations and intervene in advance to support and buffer, continuously offset the continuous disturbances brought by the slope and external environment. It can adaptively adjust the balance control parameters without relying on driver manual intervention, adapting to diverse commercial operation environments such as open outdoor, frequent passenger and cargo carrying, long and short slope parking, etc., maintaining the vehicle's stable posture throughout the process, and effectively expanding the range of working conditions in which the vehicle can operate stably. 3. This invention continuously monitors the health status of the vehicle's sensing devices, drive actuators, and internal and external communication transmission channels. It uses multi-source signal cross-verification to assess the reliability of hardware operation and accurately distinguishes different levels of fault severity. Based on a graded and progressive degradation process, it gradually constrains the vehicle's movement capability. At each degradation stage, the basic vehicle balance capability is preserved, and the stability control is not directly terminated. At the same time, the minimum risk handling action is matched for each fault state. For minor faults, the vehicle is guided to a safe area for parking, while for severe faults, the vehicle is directly supported by mechanical bottom-line support. 4. This invention deeply integrates fault degradation judgment logic, vehicle control authority switching strategy, and slope momentum unloading control mechanism, and interacts in real time with operating conditions, load, fault, and control command information to construct an integrated closed-loop control system covering all operating conditions, including normal driving, load disturbance, slope parking, and hardware / software communication failures. Various control logics cooperate with each other without conflict, improving the overall vehicle control coordination. The fault degradation judgment criteria within the system can be flexibly calibrated according to the vehicle hardware structure parameters and the functional safety regulations corresponding to the deployment area, making it adaptable to two-wheeled self-balancing vehicles with different weights, wheelbases, and balancing mechanism specifications. At the same time, the entire control scheme can be integrated into the vehicle control system, mounted on the vehicle hardware, or stored as a program medium for batch reuse. Attached Figure Description

[0016] Figure 1 This invention relates to a scenario-based balance control and functional safety classification and degradation method, system, and vehicle operation flow for two-wheeled self-balancing vehicles. Figure 1 ; Figure 2 This invention relates to a scenario-based balance control and functional safety classification and degradation method, system, and vehicle operation flow for two-wheeled self-balancing vehicles. Figure 2 ; Figure 3 This invention relates to a scenario-based balance control and functional safety classification and degradation method, system, and vehicle operation flow for two-wheeled self-balancing vehicles. Figure 3 ; Figure 4This invention relates to a scenario-based balance control and functional safety classification and degradation method, system, and vehicle operation flow for two-wheeled self-balancing vehicles. Figure 4 . Detailed Implementation

[0017] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0018] Example: Please see Figures 1-4 As shown, the present invention provides a technical solution including identifying unmanned low-speed operation scenarios such as remote call, autonomous parking, vehicle following, and parking standby; matching and switching the corresponding balance control strategy according to the identified scenario type, and configuring scenario-specific speed limit envelopes and steering restriction envelopes.

[0019] In this embodiment, in the unmanned low-speed scenario, a rotational inertia-type balancing device is preferentially used to maintain the vehicle balance, and a matching speed constraint envelope and steering constraint envelope are applied to the corresponding scenario, while the vehicle oscillation suppression control strategy is executed synchronously.

[0020] The rotating inertia balancing device is an independent torque compensation mechanism. It does not rely on the wheel drive motor to output balancing torque, but relies on a high-speed rotating flywheel to generate a reverse anti-tipping torque. It independently divides the inertia output range for four subdivided unmanned low-speed scenarios: remote-controlled short-distance vehicle relocation, autonomous parking and precise parking, long-distance automatic following, and long-term parking standby. It sets the upper limit of flywheel speed and torque output response rate for different operating conditions. In each control cycle, it continuously collects vehicle tilt angle and angular velocity signals to verify the oscillation suppression effect. Once a suppression failure trend is detected, the inertia compensation intensity is increased in advance.

[0021] In this embodiment, when the load change caused by passenger boarding / alighting or cargo loading / unloading is detected, the ground support mechanism is pre-intervention control is triggered in advance, and the balance control gain is feedforward corrected to suppress the vehicle body attitude disturbance generated at the moment of passenger boarding / alighting.

[0022] The ground support mechanism features a two-tiered progressive protection logic. The first tier involves the support legs slightly extending to pre-ground to provide cushioning support. The second tier involves the support legs fully landing and locking in place. The system relies on multi-dimensional signals from image recognition, vehicle torque fluctuations, and seat pressure sensors to predict the timing of load changes during passenger boarding / alighting and cargo loading / unloading, anticipating the magnitude of load mutations several control cycles in advance. It dynamically adjusts the balance gain feedforward correction weights for different load fluctuation scenarios, such as single-person boarding / alighting, two-person transfers, and bulk cargo loading / unloading, pre-adjusting control parameters before load mutations occur to reduce the instantaneous tilt and vibration of the vehicle. This is particularly suitable for commercial scenarios involving high-frequency passenger turnover and short-distance cargo transport in shared electric motorcycles. Through predictive feedforward and support pre-intervention technologies, it reduces the impact of load mutations on the balance stability of unmanned scenarios from the source of disturbance.

[0023] In this embodiment, when the vehicle is parked on a cross slope or ramp, it performs periodic momentum unloading or support linkage based on continuous disturbance estimation to prevent unilateral drift saturation of the frame corner.

[0024] The disturbance estimation unit integrates data from a three-axis tilt sensor, wheel speed acquisition signals, and slope detection module outputs to calculate the static offset torque on the slope in real time, continuously outputting constant disturbance observation values. The periodic momentum unloading control strategy and the prior vehicle balance integral saturation unloading logic communicate and cooperate in both directions. When the integral term of the balance controller is detected to be close to the saturation threshold, the periodic torque release operation is automatically triggered. The support linkage logic intelligently selects single-sided support landing to assist in deflection suppression or double-sided support to extend synchronously to stabilize the vehicle body according to the left and right tilt direction of the slope. The two controls can be activated individually or in combination. For long-term static unmanned conditions such as parking on long slopes in underground garages, waiting at fixed points on gentle slopes in parks, and temporary parking on roadside cross slopes, the system continuously offsets the slope offset torque, avoiding the problem of integral saturation and balance control failure caused by long-term unidirectional correction of the controller, and achieving stable attitude control of unmanned parking on multi-slope roads for a long time.

[0025] In this embodiment, external disturbances caused by crosswinds and road impacts are estimated in real time, and the impact of disturbances is offset by feedforward compensation; different load states such as single-passenger, two-person, and cargo-carrying are identified online in real time, and the balance control parameters and scene strategy switching thresholds are adaptively corrected.

[0026] The system categorizes external disturbances into two main types: persistent environmental disturbances and instantaneous road impact disturbances. A steady-state disturbance observation feedforward model is established for persistent crosswinds, while a pulse-type disturbance compensation algorithm is built for instantaneous impacts from speed bumps and potholes. These two types of disturbance compensation logics operate in parallel without interference. Load status recognition relies on multi-source information collected from the motor's steady-state output torque, vehicle static tilt angle, and seat pressure to accurately distinguish between various load conditions, including single-person riding, two-person riding, light-load cargo, and heavy-load cargo. When a load status switch occurs, the system synchronously updates core balance control parameters such as balance stiffness, response speed, and damping coefficient across all unmanned scenarios. It also dynamically corrects the threshold for switching between different scenarios, preventing issues like balance overshoot and slow response caused by sharing a fixed set of control parameters for light and heavy loads. In outdoor open-air operations, complex bumpy roads, and multi-person / cargo commercial scenarios, the system continuously adapts to changes in the external environment and vehicle load, ensuring that the scenario-based balance control strategy remains within its optimal operating range under all operating conditions.

[0027] In this embodiment, health monitoring and multi-sensor fusion confidence assessment are performed on sensors, actuators, and communications to determine the fault level.

[0028] Health monitoring covers all sensing, driving, and communication link hardware of the entire vehicle, including vehicle attitude sensors, wheel speed detection sensors, slope sensing devices, rotating inertia balancing device drive actuators, ground support mechanism motors, vehicle on-board control bus, remote summoning wireless communication link, and vehicle-to-cloud interactive transmission channel. The multi-sensor fusion confidence assessment adopts a dual judgment mechanism of multi-source signal cross-verification and timing consistency comparison. It makes tiered judgments on various abnormal fault phenomena such as complete signal loss, signal zero-point offset, excessive signal noise, actuator response delay, communication packet loss, and link interruption. It classifies multiple standardized fault levels according to the severity of the fault's impact on balance control and autonomous driving safety. The fault level judgment results are synchronously sent to the graded degrading decision unit in real time.

[0029] In this embodiment, a step-by-step functional safety degradation process is executed according to the determined fault level. The degradation order is as follows: limited balance performance - forced speed and steering constraint - forced safe stop of the whole vehicle - complete landing and bottom locking of the ground support mechanism.

[0030] Each level of downgrade retains the basic vehicle balance maintenance capability, only gradually reducing the vehicle's motion performance until the final stage, where it completely relies on ground support to lock off the dependence on balance control. The downgrade process is equipped with a smooth transition buffer mechanism, and the control parameters are adjusted progressively to avoid dangerous conditions such as sudden torque changes or violent vehicle shaking. The overall fault downgrade handling technology route is benchmarked against the safety management ideas of Ninebot, Muwei Commercial Unmanned Parking, and Automatic Summoning models. At the same time, pedestrian safety protection logic is added for the shared electric motorcycle operation scenario with high pedestrian traffic, taking into account the vehicle's own balance safety and the protection needs of pedestrians and other vehicles on the surrounding roads, forming a full-chain fault safety protection system from performance constraints to mechanical backup.

[0031] In this embodiment, each level of functional degradation is pre-configured with a corresponding minimum risk maneuver (MRM) strategy. The minimum risk maneuver strategy includes two types of safety actions: slowing down by pulling over and parking with stable support.

[0032] The system independently matches a dedicated Minimum Risk Maneuvering Logic (MRM) for each fault level. For minor local sensor anomalies, a slow-down strategy is prioritized, automatically planning a path to a safe, open area at the edge of the road. For moderate communication failures, actuator performance degradation, and severe core hardware damage, a stationary stabilization and parking strategy is directly triggered, preventing the vehicle from moving autonomously. All MRM strategies are executed with scenario-specific speed limits and steering envelope constraints overlaid throughout the process, and the rotational inertia balancing device is continuously activated to maintain the vehicle's basic attitude, ensuring no loss of balance during fault handling. MRM actions and the graded degradation process are triggered synchronously, forming an integrated safety handling chain of fault identification, grade determination, degradation control, and minimum risk maneuvering. This strictly adheres to the functional safety design principle of minimum risk operation under fault conditions for unmanned low-speed vehicles, minimizing the risk of collisions and tipping over caused by loss of vehicle control during fault conditions.

[0033] In this embodiment, the function degradation decision logic is executed in conjunction with the vehicle control transfer strategy and the ramp saturation momentum unloading strategy to form a closed-loop control logic for vehicle scenario balance plus fault degradation.

[0034] The vehicle control transfer strategy corresponds to the primary and secondary controller switching architecture. While triggering a fault-based degradation decision, the control of the primary control unit is seamlessly transferred to the backup redundant control unit simultaneously, ensuring uninterrupted control commands and continuous operation of the balance logic. The slope saturation momentum unloading strategy intervenes in parallel to continuously offset the slope static offset torque and avoid the slope integral saturation from exacerbating vehicle tilt during the fault degradation process. The seven core units of scene recognition, adaptive balance control, full-link health monitoring, fault level determination, graded degradation decision, MRM maneuver execution, and ground support drive interact with data in real time to build a complete closed-loop safety control system covering all working conditions, including normal unmanned driving, load sudden disturbances, long-term parking on slopes, and various hardware faults of sensors / actuators / communications. The system deeply couples the two core technologies of scenario-based balance control and fault functional safety degradation to achieve integrated management and control of stable control under normal working conditions and layered safety handling under fault conditions.

[0035] This embodiment includes a scene recognition module, a balance control module, a health monitoring module, a graded degradation decision-making module, a minimum risk maneuver execution module, and a ground support execution module; The modules within the system work together to achieve the control method; The fault degradation thresholds within the graded degradation system are calibrated and configured based on vehicle parameters and target market regulations and standards.

[0036] Working Principle: The system distinguishes various unmanned low-speed operation conditions based on onboard perception information. Significant differences exist in vehicle driving requirements and stability control boundaries across different scenarios. The system switches to appropriate balance control logic based on the identified scenario category, while independently defining speed and steering constraints for each scenario. This ensures precise matching between the balance control strategy and the scenario's operational requirements, preventing stability control deviations caused by adapting a unified control logic to multiple unmanned conditions. For various unmanned low-speed operation scenarios, the system abandons the traditional method of relying solely on wheel drive for balance correction. Instead, it uses an independent rotational inertia mechanism as the core compensation unit for vehicle stability, leveraging inertial torque to counteract the vehicle's tilting tendency. Combined with the speed and steering constraints defined for the scenario, the system simultaneously performs vehicle oscillation suppression and control, continuously reducing the vehicle's reciprocating swaying tendency under low-dynamic conditions such as low-speed maneuvering, automatic following, and static parking, ensuring stable vehicle posture throughout unmanned operation. The system continuously senses the vehicle's load during operation. The system monitors changes in load conditions, capturing sudden load shifts caused by passenger boarding / alighting and cargo loading / unloading. Before severe load fluctuations occur, the ground support mechanism initiates pre-buffering actions, simultaneously updating the balance controller gain parameters via the feedforward path to compensate for imbalance torques caused by load shifts. Through a combination of predictive support intervention and pre-correction of control parameters, the system counteracts vehicle tilt disturbances caused by instantaneous load impacts. When the vehicle is parked on slopes or cross slopes for extended periods, the system continuously collects vehicle attitude information to calculate constant offset disturbances caused by the slope. The system suppresses integral saturation of the balance controller through two parallel control paths: periodically releasing accumulated control momentum to offset unidirectional offset torque, and applying lateral support force in conjunction with the ground support mechanism to balance the slope tilt trend. These two control methods can operate independently or in tandem, continuously eliminating unilateral angle drift caused by prolonged parking on slopes, ensuring the balance control unit remains within its effective adjustment range and does not lose its adjustment capability due to continuous unidirectional correction. The system continuously collects external environmental interference signals during vehicle operation, distinguishes between continuous airflow interference and instantaneous road impact interference, and sets feedforward compensation logic accordingly. It outputs compensation torque in real time to counteract the impact of external disturbances on vehicle balance. Simultaneously, based on the vehicle's operating status, it identifies the overall load on the vehicle body online, dynamically updates the core balance control parameters according to different passenger and cargo carrying conditions, and synchronously adjusts the judgment conditions for switching between various scenarios. This allows the balance control logic to autonomously adapt to the load and external environment, maintaining optimal stable control throughout the entire process. The system continuously monitors the operating status of the vehicle's sensing devices, drive actuators, and internal and external communication links, collecting data from multiple hardware sources. The output signal undergoes cross-comparison and verification, and the reliability of the hardware is calculated based on the fusion of multi-source sensor information. According to various fault manifestations such as signal anomalies, execution response anomalies, and communication link interruptions, the fault levels are classified according to the degree of impact on vehicle balance control and autonomous driving safety, and standardized fault level judgment results are output. The system performs progressive functional safety control based on the identified fault severity level, gradually restricting vehicle motion performance until mechanical structure protection is implemented. The entire degradation process gradually reduces the vehicle's driving ability, while retaining basic vehicle balance control capabilities at each degradation stage, without directly cutting off stability control logic, only limiting vehicle speed. The system controls the vehicle's steering and maneuvering space until the fault reaches its highest danger level, at which point it relies on a ground support mechanism for landing and locking, completely eliminating dependence on electronic control balance to achieve mechanical backup protection and constructing a layered, buffered fault safety protection chain. Each fault degradation level is matched with a dedicated minimum-risk maneuvering logic, with two types of basic safety actions designed to reduce the risk of vehicle loss of control. For minor faults, the vehicle is controlled to move smoothly to a safe area on the road to decelerate and stop; for more serious faults, it directly triggers stationary support parking to prevent autonomous vehicle movement. During all safety maneuvers, the corresponding speed and steering constraint rules are simultaneously applied to continuously maintain vehicle stability. The system maintains a balanced state; the fault degradation judgment logic does not operate independently, but works in tandem with two sets of supporting vehicle control strategies; it synchronously completes the switching and migration of vehicle control authority, ensuring continuous and stable output of control commands during faults, and synchronously activates ramp momentum unloading control to offset ramp offset torque; multiple control units, including scenario balance control, hardware fault monitoring, fault level judgment, graded degradation handling, minimum risk maneuver execution, and support mechanism drive, interact with operating condition data in real time, forming a closed-loop safety control system covering all operating conditions, including normal driving, load disturbance, ramp parking, and software and hardware communication failures, achieving integrated operation of stable control under normal operating conditions and safe handling under fault conditions.

[0037] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their likenesses.

[0038] The present invention and its embodiments have been described above. This description is not restrictive, and the accompanying drawings are only one embodiment of the present invention; the actual structure is not limited thereto. In conclusion, if those skilled in the art are inspired by this description and design similar structures and embodiments without departing from the spirit of the invention, such designs should fall within the protection scope of the present invention.

Claims

1. A method, system, and vehicle for scenario-based balance control and functional safety classification and degradation of two-wheeled self-balancing vehicles, characterized in that: This includes recognizing scenarios such as remote summoning, autonomous parking, vehicle following, and parking standby; matching and switching the corresponding balance control strategy according to the recognized scenario type, and configuring scenario-specific speed limit envelopes and steering restriction envelopes.

2. The method, system, and vehicle for scenario-based balance control and functional safety classification and degradation of two-wheeled self-balancing vehicles according to claim 1, characterized in that: In the unmanned low-speed scenario, a rotational inertia-type balancing device is used first to maintain the vehicle's balance, and a matching speed constraint envelope and steering constraint envelope are applied to the corresponding scenario, while a vehicle oscillation suppression control strategy is executed simultaneously.

3. The method, system, and vehicle for scenario-based balance control and functional safety classification and degradation of two-wheeled self-balancing vehicles according to claim 1, characterized in that: When detecting sudden load changes caused by passenger boarding / alighting or cargo loading / unloading, the ground support mechanism is pre-intervened and the balance control gain is fed forward to suppress the vehicle body attitude disturbance caused by the instantaneous passenger boarding / alighting.

4. The method, system, and vehicle for scenario-based balance control and functional safety classification and degradation of two-wheeled self-balancing vehicles according to claim 1, characterized in that: When the vehicle is parked on a cross slope or ramp, it performs periodic momentum unloading or support linkage based on continuous disturbance estimation.

5. The method, system, and vehicle for scenario-based balance control and functional safety classification and degradation of two-wheeled self-balancing vehicles according to claim 1, characterized in that: Real-time estimation of external disturbances caused by crosswinds and road impacts, and offsetting the impact of disturbances through feedforward compensation; The system identifies load status online in real time and adaptively adjusts balance control parameters and scenario strategy switching thresholds.

6. The method, system, and vehicle for scenario-based balance control and functional safety classification and degradation of two-wheeled self-balancing vehicles according to claim 1, characterized in that: Health monitoring and confidence assessment of multiple sensors and actuators are performed on sensors, actuators, and communications to determine the fault level.

7. The method, system, and vehicle for scenario-based balance control and functional safety classification and degradation of two-wheeled self-balancing vehicles according to claim 1, characterized in that: Based on the determined fault level, a step-by-step functional safety degradation process is executed, with the degradation order as follows: limited balance performance - forced speed and steering constraints - forced safe stop of the entire vehicle - complete landing and locking of the ground support mechanism.

8. The method, system, and vehicle for scenario-based balance control and functional safety classification and degradation of two-wheeled self-balancing vehicles according to claim 1, characterized in that: Each level of functional downgrade is pre-configured with a corresponding minimum risk mechanism. The minimum risk maneuver strategy includes two types of safety actions: slowing down by pulling over and parking with stable support in place.

9. The method, system, and vehicle for scenario-based balance control and functional safety classification and degradation of two-wheeled self-balancing vehicles according to claim 1, characterized in that: The functional degradation decision logic is executed in conjunction with the vehicle control transfer strategy and the ramp saturation momentum unloading strategy to form a closed-loop control logic for vehicle scenario balance plus fault degradation.

10. The method, system, and vehicle for scenario-based balance control and functional safety classification and degradation of two-wheeled self-balancing vehicles according to claim 1, characterized in that: The fault degradation thresholds within the graded degradation system are calibrated and configured based on vehicle parameters and target market regulations and standards.

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