Multifunctional integrated vehicle-mounted safety control method and system based on UWB

Abstract

The invention discloses a UWB-based multifunctional integrated vehicle-mounted safety control method and system, and the method comprises the following steps: collecting the dynamic environment data around and in a vehicle in real time based on a plurality of sensors, carrying out the path adjustment and driving mode optimization through generating a multi-level three-dimensional environment model, and carrying out the control of the driving mode. The system performs risk assessment and dynamic adjustment by using external data such as obstacles, road conditions, weather and the like collected by a sensor and combining a behavior mode of a driver and a health state of a passenger, and in a collision prediction and protection mechanism, the system gradually activates a protection system by analyzing factors such as vehicle speed and acceleration, so that the safety is guaranteed, and the safety is ensured. The system optimizes the energy distribution of a key system through an energy scheduling model, improves the energy efficiency, and preferentially guarantees the operation of a safety system in an emergency. According to the invention, a self-adaptive learning algorithm and a multi-scene optimized driving assistance function are integrated, and self-adaptive adjustment and safety control of the vehicle in different driving environments are realized.

Description

Technical Field

[0001] The present invention relates to the technical field of vehicle safety control, and in particular to a multifunctional integrated vehicle-mounted safety control method and system based on UWB. Background Art

[0002] With the rapid development of intelligent driving and autonomous driving technologies, vehicle-mounted safety control systems have gradually become an important technical means to improve vehicle safety and driving performance. In existing technologies, vehicles mainly rely on sensors such as cameras, radars, and ultrasound to collect environmental information around the vehicle, and adjust the driving path, speed, and braking in combination with driver operations. These technologies have the problems of limited perception range, insufficient accuracy, and poor adaptability to complex environments. Especially in dynamically changing driving environments, traditional sensor systems find it difficult to capture rapidly changing external information in real time, resulting in reduced efficiency and safety of driving assistance functions.

[0003] Although the existing on-board safety control system has improved vehicle safety and driving experience to a certain extent, it still has many technical defects. The current on-board safety control system mainly relies on traditional environmental perception devices such as cameras, radars and ultrasonic sensors. These devices can provide external environmental information within a certain range, but their perception capabilities are insufficient in complex and changeable driving environments. Although cameras can identify obstacles and vehicles, their performance will drop significantly at night or in bad weather conditions, resulting in perception blind spots. Although radar and ultrasonic sensors can better capture close-range obstacles, they perform poorly in long-distance detection, especially in high-speed driving scenarios. The response time of radar sensors is often not fast enough. Due to these limitations, the existing system cannot accurately perceive and predict potential dangers in complex driving environments, resulting in the vehicle's inability to take effective countermeasures in a timely manner, thereby increasing driving risks.

[0004] Existing vehicle safety systems also have certain limitations in risk assessment. Most systems can only perform risk analysis based on single-dimensional data and lack the ability to comprehensively assess multi-dimensional data. This single risk assessment method ignores the influence of multiple important factors, such as road complexity, dynamic changes in obstacles, and behavioral patterns of external targets. The risk assessment of existing systems is usually static and difficult to dynamically adjust according to changes in the external environment and vehicle status. This risk assessment method causes the system to be unable to make accurate risk predictions when facing a changing driving environment, increasing the possibility of accidents.

[0005] In terms of collision prediction, existing technologies usually rely on simple preset algorithms or rules, which makes the collision prediction accuracy low. Since the existing system only relies on the vehicle's speed and the distance of the obstacle to predict the potential collision risk, it cannot consider the impact of multi-dimensional factors, which makes the system prone to misjudgment or missed judgment in complex driving environments. The existing system has a long reaction time when a potential collision occurs, especially in high-speed or emergency situations. It is difficult for the system to make a quick judgment and activate protective measures, which increases the probability of a collision and reduces the safety of the vehicle.

[0006] The energy management systems of existing vehicles also have many shortcomings. Current energy management usually relies on fixed energy allocation strategies and cannot be flexibly adjusted according to the real-time needs of the vehicle and environmental changes. The existing system cannot dynamically adjust the vehicle's energy distribution when the driver's operation and the external environment change, resulting in low energy utilization efficiency. In emergency situations, the existing energy management system also lacks response capabilities and cannot prioritize energy support for key safety systems such as braking and steering, which seriously affects the vehicle's safety and emergency response capabilities. Fixed energy allocation strategies are not only difficult to adapt to complex driving scenarios, but also easily cause energy waste and cannot meet energy-saving requirements.

[0007] In terms of passenger health monitoring, although some high-end vehicles have introduced health monitoring functions, most of these functions are independent of other vehicle systems and fail to be effectively linked with the driving control system. Although the system can monitor passengers' heart rate, breathing rate and other physiological data, these monitoring data can usually only be used for warnings and cannot directly affect the vehicle's driving mode. The existing health monitoring system lacks linkage with the driving status and external environment, and cannot adjust the driving mode in time according to the passenger's health status. Especially when the passenger's health is abnormal, the system cannot respond quickly and adjust the vehicle speed or switch to automatic driving mode, resulting in potential safety risks.

[0008] Therefore, how to provide a multifunctional integrated vehicle safety control method and system based on UWB is a problem that technical personnel in this field need to solve urgently. Summary of the invention

[0009] One purpose of the present invention is to propose a multifunctional integrated vehicle-mounted safety control method and system based on UWB. The present invention generates a multi-level three-dimensional environmental model by configuring a UWB sensor network and combining an adaptive learning algorithm, and performs real-time monitoring and analysis of the dynamic environment, external targets and passenger health status during vehicle driving. The method describes in detail the operating steps of risk prediction, path adjustment, collision protection and energy scheduling based on a multi-factor weight model, and has the advantages of high real-time perception accuracy, accurate risk prediction, energy efficiency optimization and strong passenger safety protection.

[0010] The multifunctional integrated vehicle safety control method and system based on UWB according to an embodiment of the present invention comprises the following steps:

[0011] S1. Through the UWB sensor network configured around the vehicle, multi-level dynamic environment data around the vehicle and vehicle status information are collected in real time. The sensor automatically adjusts the perception range, sensitivity and frequency according to the dynamic complexity of the external environment and the current driving status of the vehicle, and generates a multi-level three-dimensional environment model through an adaptive learning algorithm;

[0012] S2. Based on the generated multi-level three-dimensional environmental model, a multi-factor weight model of time priority, space priority, speed priority and dynamic behavior prediction is introduced to predict and evaluate potential risk events. The model dynamically adjusts the risk weight parameters according to changes in the environment and vehicle status, and automatically generates a priority list containing real-time risk events;

[0013] S3. Based on the generated priority list, the system obtains the dynamic path and behavior intention data of the external target through UWB communication with the external device. The system adjusts the vehicle's path in combination with the behavior prediction results of the external target, and integrates it with the driver's behavior pattern database to intelligently adjust the driving path, speed and driving mode;

[0014] S4. The system proactively adjusts the vehicle's driving mode based on real-time monitoring of passenger health data, combined with route adjustments and external environmental changes. When passenger health data is abnormal, the system prioritizes switching to low-speed and autonomous driving modes based on environmental complexity and the results of dynamic vehicle route adjustments.

[0015] S5. Combine the generated 3D environment model with the route adjustment data to predict energy demand based on the current environment, driving status and future driving route. Through the adaptive energy scheduling model, the energy distribution of the vehicle is optimized in real time. The system pre-plans the energy consumption priority in different scenarios based on external factors, and gives priority to ensuring the energy supply of key equipment in an emergency.

[0016] S6. Based on the generated three-dimensional environment model and path adjustment information, the system uses a multi-dimensional collision prediction model to predict complex collision scenarios. The system predicts potential collision types by analyzing the relative position, speed, acceleration and deceleration trends, and direction changes between the vehicle and obstacles and external targets. According to the severity and probability of collision prediction, the system gradually activates a phased adaptive multi-level protection mechanism;

[0017] S7. The system analyzes the driver's current state and external environment based on the driver's behavior pattern library and the behavior pattern fusion mechanism. When the driver approaches the vehicle, the system automatically matches his behavior pattern and adjusts the settings in the vehicle. It also optimizes the vehicle startup process and provides a seamless entry experience based on the vehicle's current energy state and the driver's behavior pattern.

[0018] S8. Through the multi-scenario adaptive learning algorithm, the driving assistance functions in each scenario are optimized and adjusted with multiple objectives. The acceleration, steering and braking responses are optimized based on the external environment changes, the driver's operating mode and the vehicle performance. In similar environments, the system further optimizes the driving strategy based on the previous optimization results to achieve vehicle adaptive multi-scenario learning.

[0019] Optionally, the S1 specifically includes:

[0020] S11, by configuring multiple UWB sensors around the vehicle, collecting multi-level dynamic environmental data around the vehicle and in-vehicle status information in real time, the environmental data including the spatial position of obstacles, relative speed, terrain changes, weather conditions and traffic flow density, and classifying the data into static environmental data and dynamic environmental data;

[0021] S12. Based on the collected static and dynamic environmental data, conduct real-time assessment of environmental complexity and adjust the sampling frequency and accuracy of sensors in real time to adapt to complex changes in the environment:

[0022]

[0023] Among them, C e represents the complexity of the environment, T represents the traffic flow density, W represents the impact of weather conditions, V represents the current speed of the vehicle, represents the relative movement change of the obstacle, α and β are adjustment factors, and ∈ is the stability term;

[0024] S13. Based on the evaluated environmental complexity results, the system dynamically adjusts the data weights of each sensor through an adaptive learning algorithm:

[0025]

[0026] in, is the weight of sensor data, δ is the adaptive learning rate, is the result of weight adjustment according to the complexity of the environment, γ represents the weight factor for obstacle changes, and η is the balance coefficient;

[0027] S14. After adjusting the sensor weights, the system generates a multi-level three-dimensional environment model based on the weighted sensor data. The model includes the location of dynamic obstacles, the slope change of the terrain, the traffic flow density and its change trend. The three-dimensional environment model is continuously updated according to the real-time data and the changes in the sensor weights:

[0028]

[0029] Among them, M 3D is the three-dimensional environment model, P i (t) represents the data packet collected by the sensor at time t, W s (t) is the sensor weight at that moment.

[0030] Optionally, the S2 specifically includes:

[0031] S21, the system obtains factor information related to time, space, and speed based on the generated multi-level three-dimensional environmental model;

[0032] S22. Introduce a multi-factor weight model of time priority, space priority, and speed priority to perform weighted calculations on time, space, and speed factors:

[0033]

[0034] Among them, R w is the risk weight, T f is the time factor, S f is the space factor, V f is the speed factor, α, β, γ are weight coefficients, and Δt is the time change;

[0035] S23. Based on the weight model, the system introduces dynamic behavior prediction factors to predict the behavior path of external targets. It uses the movement information of external targets, combined with historical data and real-time data, to predict their future behavior:

[0036]

[0037] Among them, P d represents the dynamic behavior prediction value, O i (t) is the moving trajectory of the external target at time t, V t is the current speed of the vehicle, S i is the distance between the target and the vehicle;

[0038] S24, the system predicts the value P based on time, space, speed factors and dynamic behavior d , generate a priority list of risk events, sort through the priority list, high priority events are processed first, and low priority events enter the waiting queue.

[0039] Optionally, the S3 specifically includes:

[0040] S31, the system communicates with the external device through UWB according to the generated priority list to obtain the dynamic path and behavior intention data of the external target, wherein the external target data includes the relative position, speed, direction and behavior prediction of the target;

[0041] S32, route adjustment combined with external behavior prediction The system performs route adjustment based on the dynamic behavior prediction value of the external target and the driving state of the vehicle:

[0042]

[0043] Among them, P adj is the path adjustment amount, V t is the current speed of the vehicle, S o is the relative distance of the external target, P d is the dynamic behavior prediction value, λ1 is the adjustment coefficient, T r is the response time, μ1 is the prediction weight, ∈ is the stability term;

[0044] S33, the system combines the behavior prediction of external targets with the driver's historical behavior pattern to optimize the vehicle's path, speed and driving mode, and intelligently adjusts the vehicle based on the driver's behavior pattern and changes in the current external environment;

[0045] S34, adjust P based on path adj And intelligent adjustment, dynamically adjust the vehicle's driving speed and driving mode, and the speed and path adjustments are carried out in conjunction, so that the vehicle can maintain driving safety and stability in a complex external environment. By real-time analysis of external target data and the driver's behavior patterns, the optimal driving mode and speed adjustment plan are selected.

[0046] Optionally, the S4 specifically includes:

[0047] S41. Monitor the health status of passengers in real time through sensors in the vehicle, including heart rate, breathing rate, body temperature and posture changes, conduct real-time analysis based on the health status data, and identify any abnormalities;

[0048] S42, combining the acquired health status data with the current driving route and external environmental conditions. If the passenger's health status is abnormal, the system decides whether to adjust the driving mode by comprehensively considering the current driving route and weather conditions;

[0049] S43, the system adjusts the amount P of the route based on the passenger's health data adj , determine whether the vehicle's speed and path need to be adjusted:

[0050]

[0051] Among them, H adj is the adjustment amount based on health status, H s Indicates the change of passenger health status, P adj is the path adjustment amount, θ is the adjustment coefficient, T h is the time interval for health data collection, ∈ h is a stable term;

[0052] S44. When the system detects an abnormal health condition of a passenger, it will automatically switch to a low-speed mode or an automatic driving mode based on the complexity of the road and weather conditions. The switching of the driving mode will be based on the urgency of the passenger's health condition and the risk assessment results of the external environment.

[0053] S45. By real-time monitoring of the health status of passengers and continuously updating the three-dimensional environmental model in combination with UWB communication of external devices, the system further optimizes the driving mode and path adjustment based on the updated environmental model, so as to match the health status of passengers with the driving environment and provide a real-time responsive driving solution.

[0054] Optionally, the S5 specifically includes:

[0055] S51, based on the current driving path, external environment and vehicle operating status, predict the energy demand in different future driving scenarios combined with real-time road conditions, driving speed and vehicle load, so that the energy demand matches the actual driving scenario, and the system adjusts the energy prediction value in real time according to the changing environmental conditions to optimize energy utilization;

[0056] S52, dynamically adjusting energy demand by real-time monitoring of external environmental data. After combining external factors with energy demand, the energy allocation scheme can be adjusted according to the complexity of different driving scenarios to maximize vehicle energy efficiency and driving performance;

[0057] S53. The system dynamically optimizes the energy distribution of the vehicle using an adaptive energy scheduling model based on the energy demand prediction results and external environmental conditions, adjusts the energy distribution of each key system in real time, and optimizes the energy distribution strategy based on the driver's driving habits and historical driving data through a built-in self-learning algorithm;

[0058] S54. When abnormal health conditions of passengers, bad weather or traffic accidents are detected, the system will prioritize energy allocation to key safety systems. In emergency situations, the system will adjust energy priorities according to the risk level of the emergency, so that key systems can maintain normal operation even when energy is insufficient.

[0059] S55. The system can further optimize energy demand based on real-time feedback from external data in extreme environments. In extremely low temperature environments, the system gives priority to providing energy to key systems and adjusts energy consumption based on real-time feedback to adapt to special scenarios of low temperature and high energy consumption.

[0060] S56. The system continuously monitors the vehicle's energy consumption data and external feedback information, and optimizes the energy scheduling strategy through a real-time adaptive learning algorithm. This feedback mechanism allows the system to respond quickly to changes in the external environment and dynamically adjust energy distribution, so that the vehicle can achieve energy efficiency optimization and ensure driving safety in complex and emergency environments.

[0061] Optionally, the S6 specifically includes:

[0062] S61. By collecting multi-dimensional information of the vehicle's speed, acceleration, relative position and direction change of external targets, the system builds a collision prediction model to calculate potential collision scenarios. The system updates the collision prediction in real time based on the multi-dimensional information:

[0063] C pred =f(V t ,a t ,O d ,Δθ);

[0064] Among them, C pred is the collision prediction value, V t represents the current speed of the vehicle, a t is the current acceleration, O d is the relative distance of the external obstacle, Δθ is the direction change between the vehicle and the obstacle;

[0065] S62. Based on the collision prediction results, the system calculates the potential collision risk level, and the system adjusts the risk level according to the current driving state of the vehicle and external environmental factors:

[0066]

[0067] Among them, R level is the collision risk level, W e is the environmental impact factor, E s is the vehicle status influencing factor, ∈ r is the stability coefficient;

[0068] S63: When the system detects a high-risk collision, it gradually activates the protection mechanism according to the risk level, including early warning and deceleration prompts, activating higher-level protection systems, and activating each protection measure in sequence to ensure the safety of the vehicle and passengers;

[0069] S64. After the protection mechanism is activated, the system continuously monitors the vehicle's speed, acceleration and dynamic information of external obstacles, and adjusts the protection measures according to the real-time collision prediction results, so that the protection plan is optimized as the external environment changes;

[0070] S65: The system works with the energy management system to prioritize energy allocation for key protection systems. If the vehicle is in a scenario of insufficient energy or high energy consumption, the system will automatically adjust the energy allocation plan to enable key protection systems to work normally.

[0071] S66. When the protection mechanism is activated, the system makes dynamic adjustments based on the driver's operation. The system optimizes the protection plan based on the driver's operation input, so that the driver's operation is properly responded to, thereby optimizing the execution effect of the protection mechanism.

[0072] Optionally, the S8 specifically includes:

[0073] S81. Collect acceleration, braking, and steering response data of the vehicle and external environment data according to different driving environments, and construct response models under different driving scenarios to reflect the different effects of each scenario on vehicle behavior;

[0074] S82. Through adaptive learning algorithms, the vehicle's acceleration, braking and steering strategies are dynamically adjusted based on the collected multi-scenario data. The system optimizes the vehicle's response behavior based on the driver's historical operating patterns and real-time external environment changes:

[0075] R opt =f(V t ,A s ,T r ,E e );

[0076] Among them, R opt is the optimized vehicle response, V t is the current speed of the vehicle, A s is the acceleration parameter, T r is the steering response time, E e For external environmental factors;

[0077] S83. The system adjusts driving strategies in different scenarios based on the optimization results. In urban road scenarios, low-speed steering control is given priority. On highways, the system adjusts the balance between acceleration and braking. On mountain roads, the system focuses on the linkage control of steering and braking. The system dynamically adjusts the response speed and strength of acceleration, braking and steering according to specific scenarios.

[0078] S84. The system combines historical data with real-time data to update the model based on the learning results and continuously updates the adaptive learning model:

[0079] M upd =M prev +α×ΔR;

[0080] Among them, M upd is the updated model, M prev are the previous model parameters, α is the learning rate, and ΔR is the response adjustment amount;

[0081] S85. By real-time monitoring of the driver's operation and vehicle operating status, combined with changes in the external environment, the acceleration, braking and steering strategies of the vehicle are adjusted, and the vehicle's response is adjusted at any time according to the driver's operation frequency, road complexity and vehicle dynamics;

[0082] S86. The system improves the global driving strategy in multiple scenarios through long-term driving data accumulation. Whenever the vehicle operates in a similar driving scenario, the system optimizes based on historical data and adjusts the strategy in the same and similar scenarios in the future, optimizing the global driving strategy through long-term data accumulation.

[0083] Optional modules include:

[0084] UWB sensor network module: collects multi-level dynamic environmental data around the vehicle and in-vehicle status information in real time, and adjusts the sensing range, sensitivity and acquisition frequency according to the dynamic complexity of the external environment and the driving status of the vehicle;

[0085] Adaptive learning algorithm module: Generates a multi-level three-dimensional environmental model based on the collected data, dynamically adjusts sensor weights, optimizes model generation and updating, and provides real-time environmental feedback;

[0086] Multi-factor weight assessment module: Through the multi-factor weight model of time priority, space priority, and speed priority, it predicts potential risks, dynamically adjusts weight parameters, and generates a priority list containing real-time risk events;

[0087] Behavior prediction and path adjustment module: According to the dynamic path and behavior intention of the external target, combined with the driver's behavior pattern and vehicle status, the vehicle's driving path, speed and driving mode are adjusted to achieve intelligent adjustment;

[0088] Passenger health monitoring module: monitors the health status of passengers through in-vehicle sensors, automatically adjusts the driving mode based on the driving path and environmental changes, and switches to low-speed or autonomous driving mode when abnormal health data is detected;

[0089] Energy scheduling module: optimizes energy allocation to key systems in real time based on energy demand forecast results and external environmental conditions, and prioritizes energy allocation to safety systems in emergency situations;

[0090] Collision prediction and protection module: Based on a multi-dimensional collision prediction model, it evaluates the relative position, speed, and acceleration of the vehicle, obstacles, and external targets in real time, and activates a step-by-step protection mechanism to deal with different collision risk levels;

[0091] Driving assistance optimization module: Based on multi-scenario adaptive learning algorithms, it optimizes acceleration, braking and steering response strategies, adjusts global driving strategies through long-term data accumulation, and optimizes the vehicle's driving performance in different driving environments

[0092] The beneficial effects of the present invention are:

[0093] The present invention adopts the multi-level dynamic environment data acquisition technology of the UWB sensor network to achieve high-precision perception of the vehicle's surrounding environment. Compared with traditional sensors, the present invention can not only accurately capture obstacles, terrain changes and traffic flow in complex and dynamic driving environments, but also adjust the perception range and accuracy in real time according to the vehicle's current driving status. This dynamic perception method enables the vehicle to quickly adapt to changes in the external environment, significantly improving the perception accuracy and response speed; the present invention uses a multi-factor weight model to combine multidimensional factors such as time, space, and speed to predict and evaluate risk events. The model can dynamically adjust the weights of each factor, generate a priority list of risk events, and provide the system with accurate risk prediction and evaluation results. In particular, through the fusion of external target behavior prediction and driver historical behavior patterns, the system can intelligently adjust the driving path and driving mode, achieving more accurate path planning and safety optimization, and overcoming the defects of single risk assessment and insufficient dynamic response capability in the prior art.

[0094] The present invention uses a multi-dimensional collision prediction model. The system can comprehensively consider the vehicle speed, acceleration, relative position and dynamic behavior of obstacles to accurately predict potential collision scenarios. According to the prediction results, the system gradually activates the multi-level protection mechanism and starts the protection measures in stages, from preliminary warnings and deceleration prompts to activation of higher-level protection systems to ensure the safety of vehicles and passengers under complex road conditions. An adaptive energy scheduling model is introduced, which can dynamically optimize the allocation of vehicle energy according to different driving scenarios and energy requirements. By real-time monitoring of external environmental conditions, the system can adjust the energy allocation plan, especially in emergency situations, giving priority to providing energy support to key safety systems to ensure the safety performance of the vehicle. This adaptive energy management greatly improves the energy efficiency of the vehicle and solves the problems of fixed allocation and lack of flexibility of traditional energy management systems.

[0095] The present invention monitors the health status data of passengers in real time. The system can automatically adjust the driving mode in combination with the driving path and external environmental changes. When the health status of the passenger is abnormal, the system automatically switches to low-speed or automatic driving mode according to the environmental complexity and risk assessment results to ensure the safety of the passengers. This function makes up for the problem of insufficient linkage between the health monitoring system and the driving mode in the prior art, and improves the safety of passengers. Through the multi-scenario adaptive learning algorithm, the present invention can continuously optimize the vehicle's acceleration, braking and steering response strategies in different driving environments. The system can continuously optimize the global driving strategy through long-term data accumulation and real-time environmental feedback, and improve the vehicle's adaptability in complex environments. This optimization method based on adaptive learning significantly improves the driving performance of the vehicle, enabling it to show the best response effect in different driving scenarios. BRIEF DESCRIPTION OF THE DRAWINGS

[0096] The accompanying drawings are used to provide a further understanding of the present invention and constitute a part of the specification. Together with the embodiments of the present invention, they are used to explain the present invention and do not constitute a limitation of the present invention. In the accompanying drawings:

[0097] Figure 1 A flowchart of the multifunctional integrated vehicle safety control method and system based on UWB proposed by the present invention;

[0098] Figure 2 This is a flow chart of risk event prediction and evaluation and path adjustment based on a multi-factor weight model in the present invention;

[0099] Figure 3 Schematic diagram of the adaptive energy scheduling model for key system energy allocation in the present invention. DETAILED DESCRIPTION

[0100] The present invention will now be described in further detail with reference to the accompanying drawings. These drawings are simplified schematic diagrams, which only illustrate the basic structure of the present invention in a schematic manner, and therefore only show the components related to the present invention.

[0101] refer to Figure 1-3 , a multifunctional integrated vehicle safety control method and system based on UWB, comprising the following steps:

[0102] S1. Through the UWB sensor network configured around the vehicle, multi-level dynamic environment data around the vehicle and vehicle status information are collected in real time. The sensor automatically adjusts the perception range, sensitivity and frequency according to the dynamic complexity of the external environment and the current driving status of the vehicle, and generates a multi-level three-dimensional environment model through an adaptive learning algorithm;

[0103] S2. Based on the generated multi-level three-dimensional environmental model, a multi-factor weight model of time priority, space priority, speed priority and dynamic behavior prediction is introduced to predict and evaluate potential risk events. The model dynamically adjusts the risk weight parameters according to changes in the environment and vehicle status, and automatically generates a priority list containing real-time risk events;

[0104] S3. Based on the generated priority list, the system obtains the dynamic path and behavior intention data of the external target through UWB communication with the external device. The system adjusts the vehicle's path in combination with the behavior prediction results of the external target, and integrates it with the driver's behavior pattern database to intelligently adjust the driving path, speed and driving mode;

[0105] S4. The system proactively adjusts the vehicle's driving mode based on real-time monitoring of passenger health data, combined with route adjustments and external environmental changes. When passenger health data is abnormal, the system prioritizes switching to low-speed and autonomous driving modes based on environmental complexity and the results of dynamic vehicle route adjustments.

[0106] S5. Combine the generated 3D environment model with the route adjustment data to predict energy demand based on the current environment, driving status and future driving route. Through the adaptive energy scheduling model, the energy distribution of the vehicle is optimized in real time. The system pre-plans the energy consumption priority in different scenarios based on external factors, and gives priority to ensuring the energy supply of key equipment in an emergency.

[0107] S6. Based on the generated three-dimensional environment model and path adjustment information, the system uses a multi-dimensional collision prediction model to predict complex collision scenarios. The system predicts potential collision types by analyzing the relative position, speed, acceleration and deceleration trends, and direction changes between the vehicle and obstacles and external targets. According to the severity and probability of collision prediction, the system gradually activates a phased adaptive multi-level protection mechanism;

[0108] S7. The system analyzes the driver's current state and external environment based on the driver's behavior pattern library and the behavior pattern fusion mechanism. When the driver approaches the vehicle, the system automatically matches his behavior pattern and adjusts the settings in the vehicle. It also optimizes the vehicle startup process and provides a seamless entry experience based on the vehicle's current energy state and the driver's behavior pattern.

[0109] S8. Through the multi-scenario adaptive learning algorithm, the driving assistance functions in each scenario are optimized and adjusted with multiple objectives. The acceleration, steering and braking responses are optimized based on the external environment changes, the driver's operating mode and the vehicle performance. In similar environments, the system further optimizes the driving strategy based on the previous optimization results to achieve vehicle adaptive multi-scenario learning.

[0110] In this implementation, S1 specifically includes:

[0111] S11, by configuring multiple UWB sensors around the vehicle, collecting multi-level dynamic environmental data around the vehicle and in-vehicle status information in real time, the environmental data including the spatial position of obstacles, relative speed, terrain changes, weather conditions and traffic flow density, and classifying the data into static environmental data and dynamic environmental data;

[0112] S12. Based on the collected static and dynamic environmental data, conduct real-time assessment of environmental complexity and adjust the sampling frequency and accuracy of sensors in real time to adapt to complex changes in the environment:

[0113]

[0114] Among them, C e represents the complexity of the environment, T represents the traffic flow density, W represents the impact of weather conditions, V represents the current speed of the vehicle, represents the relative movement change of the obstacle, α and β are adjustment factors, and ∈ is the stability term;

[0115] S13. Based on the evaluated environmental complexity results, the system dynamically adjusts the data weights of each sensor through an adaptive learning algorithm:

[0116]

[0117] in, is the weight of sensor data, δ is the adaptive learning rate, is the result of weight adjustment according to the complexity of the environment, γ represents the weight factor for obstacle changes, and η is the balance coefficient;

[0118] S14. After adjusting the sensor weights, the system generates a multi-level three-dimensional environment model based on the weighted sensor data. The model includes the location of dynamic obstacles, the slope change of the terrain, the traffic flow density and its change trend. The three-dimensional environment model is continuously updated according to the real-time data and the changes in the sensor weights:

[0119]

[0120] Among them, M 3D is the three-dimensional environment model, P i (t) represents the data packet collected by the sensor at time t, W s (t) is the sensor weight at that moment.

[0121] In this implementation, S2 specifically includes:

[0122] S21, the system obtains factor information related to time, space, and speed based on the generated multi-level three-dimensional environmental model;

[0123] S22. Introduce a multi-factor weight model of time priority, space priority, and speed priority to perform weighted calculations on time, space, and speed factors:

[0124]

[0125] Among them, R w is the risk weight, T f is the time factor, S f is the space factor, V f is the speed factor, α, β, γ are weight coefficients, and Δt is the time change;

[0126] S23. Based on the weight model, the system introduces dynamic behavior prediction factors to predict the behavior path of external targets. It uses the movement information of external targets, combined with historical data and real-time data, to predict their future behavior:

[0127]

[0128] Among them, P d represents the dynamic behavior prediction value, O i (t) is the moving trajectory of the external target at time t, V t is the current speed of the vehicle, S i is the distance between the target and the vehicle;

[0129] S24, the system predicts the value P based on time, space, speed factors and dynamic behavior d , generate a priority list of risk events, sort through the priority list, high priority events are processed first, and low priority events enter the waiting queue.

[0130] In this implementation, S3 specifically includes:

[0131] S31, the system communicates with the external device through UWB according to the generated priority list to obtain the dynamic path and behavior intention data of the external target, wherein the external target data includes the relative position, speed, direction and behavior prediction of the target;

[0132] S32, route adjustment combined with external behavior prediction The system performs route adjustment based on the dynamic behavior prediction value of the external target and the driving state of the vehicle:

[0133]

[0134] Among them, P adj is the path adjustment amount, V t is the current speed of the vehicle, So is the relative distance of the external target, P d is the dynamic behavior prediction value, λ1 is the adjustment coefficient, T r is the response time, μ1 is the prediction weight, ∈ is the stability term;

[0135] S33, the system combines the behavior prediction of external targets with the driver's historical behavior pattern to optimize the vehicle's path, speed and driving mode, and intelligently adjusts the vehicle based on the driver's behavior pattern and changes in the current external environment;

[0136] S34, adjust P based on path adj And intelligent adjustment, dynamically adjust the vehicle's driving speed and driving mode, and the speed and path adjustments are carried out in conjunction, so that the vehicle can maintain driving safety and stability in a complex external environment. By real-time analysis of external target data and the driver's behavior patterns, the optimal driving mode and speed adjustment plan are selected.

[0137] In this implementation manner, the S4 specifically includes:

[0138] S41. Monitor the health status of passengers in real time through sensors in the vehicle, including heart rate, breathing rate, body temperature and posture changes, conduct real-time analysis based on the health status data, and identify any abnormalities;

[0139] S42, combining the acquired health status data with the current driving route and external environmental conditions. If the passenger's health status is abnormal, the system decides whether to adjust the driving mode by comprehensively considering the current driving route and weather conditions;

[0140] S43, the system adjusts the amount P of the route based on the passenger's health data adj , determine whether the vehicle's speed and path need to be adjusted:

[0141]

[0142] Among them, H adj is the adjustment amount based on health status, H s Indicates the change of passenger health status, P adj is the path adjustment amount, θ is the adjustment coefficient, T h is the time interval for health data collection, ∈ h is a stable term;

[0143] S44. When the system detects an abnormal health condition of a passenger, it will automatically switch to a low-speed mode or an automatic driving mode based on the complexity of the road and weather conditions. The switching of the driving mode will be based on the urgency of the passenger's health condition and the risk assessment results of the external environment.

[0144] S45. By real-time monitoring of the health status of passengers and continuously updating the three-dimensional environmental model in combination with UWB communication of external devices, the system further optimizes the driving mode and path adjustment based on the updated environmental model, so as to match the health status of passengers with the driving environment and provide a real-time responsive driving solution.

[0145] In this implementation manner, S5 specifically includes:

[0146] S51, based on the current driving path, external environment and vehicle operating status, predict the energy demand in different future driving scenarios combined with real-time road conditions, driving speed and vehicle load, so that the energy demand matches the actual driving scenario, and the system adjusts the energy prediction value in real time according to the changing environmental conditions to optimize energy utilization;

[0147] S52, dynamically adjusting energy demand by real-time monitoring of external environmental data. After combining external factors with energy demand, the energy allocation scheme can be adjusted according to the complexity of different driving scenarios to maximize vehicle energy efficiency and driving performance;

[0148] S53. The system dynamically optimizes the energy distribution of the vehicle using an adaptive energy scheduling model based on the energy demand prediction results and external environmental conditions, adjusts the energy distribution of each key system in real time, and optimizes the energy distribution strategy based on the driver's driving habits and historical driving data through a built-in self-learning algorithm;

[0149] S54. When abnormal health conditions of passengers, bad weather or traffic accidents are detected, the system will prioritize energy allocation to key safety systems. In emergency situations, the system will adjust energy priorities according to the risk level of the emergency, so that key systems can maintain normal operation even when energy is insufficient.

[0150] S55. The system can further optimize energy demand based on real-time feedback from external data in extreme environments. In extremely low temperature environments, the system gives priority to providing energy to key systems and adjusts energy consumption based on real-time feedback to adapt to special scenarios of low temperature and high energy consumption.

[0151] S56. The system continuously monitors the vehicle's energy consumption data and external feedback information, and optimizes the energy scheduling strategy through a real-time adaptive learning algorithm. This feedback mechanism allows the system to respond quickly to changes in the external environment and dynamically adjust energy distribution, so that the vehicle can achieve energy efficiency optimization and ensure driving safety in complex and emergency environments.

[0152] In this implementation manner, S6 specifically includes:

[0153] S61. By collecting multi-dimensional information of the vehicle's speed, acceleration, relative position and direction change of external targets, the system builds a collision prediction model to calculate potential collision scenarios. The system updates the collision prediction in real time based on the multi-dimensional information:

[0154] C pred =f(V t ,a t ,O d ,Δθ);

[0155] Among them, C pred is the collision prediction value, V t represents the current speed of the vehicle, a t is the current acceleration, O d is the relative distance of the external obstacle, Δθ is the direction change between the vehicle and the obstacle;

[0156] S62. Based on the collision prediction results, the system calculates the potential collision risk level, and the system adjusts the risk level according to the current driving state of the vehicle and external environmental factors:

[0157]

[0158] Among them, R level is the collision risk level, W e is the environmental impact factor, E s is the vehicle status influencing factor, ∈ r is the stability coefficient;

[0159] S63: When the system detects a high-risk collision, it gradually activates the protection mechanism according to the risk level, including early warning and deceleration prompts, activating higher-level protection systems, and activating each protection measure in sequence to ensure the safety of the vehicle and passengers;

[0160] S64. After the protection mechanism is activated, the system continuously monitors the vehicle's speed, acceleration and dynamic information of external obstacles, and adjusts the protection measures according to the real-time collision prediction results, so that the protection plan is optimized as the external environment changes;

[0161] S65: The system works with the energy management system to prioritize energy allocation for key protection systems. If the vehicle is in a situation of insufficient energy or high energy consumption, the system will automatically adjust the energy allocation plan to enable key protection systems to work normally.

[0162] S66. When the protection mechanism is activated, the system makes dynamic adjustments based on the driver's operation. The system optimizes the protection plan based on the driver's operation input, so that the driver's operation is properly responded to, thereby optimizing the execution effect of the protection mechanism.

[0163] In this implementation manner, S8 specifically includes:

[0164] S81. Collect acceleration, braking, and steering response data of the vehicle and external environment data according to different driving environments, and construct response models under different driving scenarios to reflect the different effects of each scenario on vehicle behavior;

[0165] S82. Through adaptive learning algorithms, the vehicle's acceleration, braking and steering strategies are dynamically adjusted based on the collected multi-scenario data. The system optimizes the vehicle's response behavior based on the driver's historical operating patterns and real-time external environment changes:

[0166] R opt =f(V t ,A s ,T r ,E e );

[0167] Among them, R opt is the optimized vehicle response, V t is the current speed of the vehicle, A s is the acceleration parameter, T r is the steering response time, E e For external environmental factors;

[0168] S83. The system adjusts driving strategies in different scenarios based on the optimization results. In urban road scenarios, low-speed steering control is given priority. On highways, the system adjusts the balance between acceleration and braking. On mountain roads, the system focuses on the linkage control of steering and braking. The system dynamically adjusts the response speed and strength of acceleration, braking and steering according to specific scenarios.

[0169] S84. The system combines historical data with real-time data to update the model based on the learning results and continuously updates the adaptive learning model:

[0170] M upd =M prev +α×ΔR;

[0171] Among them, M upd is the updated model, M prev are the previous model parameters, α is the learning rate, and ΔR is the response adjustment amount;

[0172] S85. By real-time monitoring of the driver's operation and vehicle operating status, combined with changes in the external environment, the acceleration, braking and steering strategies of the vehicle are adjusted, and the vehicle's response is adjusted at any time according to the driver's operation frequency, road complexity and vehicle dynamics;

[0173] S86. The system improves the global driving strategy in multiple scenarios through long-term driving data accumulation. Whenever the vehicle operates in a similar driving scenario, the system optimizes based on historical data and adjusts the strategy in the same and similar scenarios in the future, optimizing the global driving strategy through long-term data accumulation.

[0174] In this embodiment, the following modules are included:

[0175] UWB sensor network module: collects multi-level dynamic environmental data around the vehicle and in-vehicle status information in real time, and adjusts the sensing range, sensitivity and acquisition frequency according to the dynamic complexity of the external environment and the driving status of the vehicle;

[0176] Adaptive learning algorithm module: Generates a multi-level three-dimensional environmental model based on the collected data, dynamically adjusts sensor weights, optimizes model generation and updating, and provides real-time environmental feedback;

[0177] Multi-factor weight assessment module: Through the multi-factor weight model of time priority, space priority, and speed priority, it predicts potential risks, dynamically adjusts weight parameters, and generates a priority list containing real-time risk events;

[0178] Behavior prediction and path adjustment module: According to the dynamic path and behavior intention of the external target, combined with the driver's behavior pattern and vehicle status, the vehicle's driving path, speed and driving mode are adjusted to achieve intelligent adjustment;

[0179] Passenger health monitoring module: monitors the health status of passengers through in-vehicle sensors, automatically adjusts the driving mode based on the driving path and environmental changes, and switches to low-speed or autonomous driving mode when abnormal health data is detected;

[0180] Energy scheduling module: optimizes energy allocation to key systems in real time based on energy demand forecast results and external environmental conditions, and prioritizes energy allocation to safety systems in emergency situations;

[0181] Collision prediction and protection module: Based on a multi-dimensional collision prediction model, it evaluates the relative position, speed, and acceleration of the vehicle, obstacles, and external targets in real time, and activates a step-by-step protection mechanism to deal with different collision risk levels;

[0182] Driving assistance optimization module: Based on a multi-scenario adaptive learning algorithm, it optimizes acceleration, braking and steering response strategies. Through long-term data accumulation, it adjusts the global driving strategy and optimizes the vehicle's driving performance in different driving environments.

[0183] Embodiment 1:

[0184] In order to verify the feasibility of the present invention in implementation, the present invention is applied to the morning rush hour of the Third Ring Road section in Beijing on October 1, 2024. The weather is foggy and accompanied by light rain, the road surface is slippery, and the traffic flow is dense. The urban traffic environment in which the vehicle is located is complex, and there are a large number of pedestrians, cyclists, cars and various traffic signals around it. At the same time, weather conditions have an adverse effect on visibility. The vehicle sensor system in the prior art often finds it difficult to effectively perceive the dynamic environment and predict potential dangers under such conditions, resulting in insufficient safety. Traditional cameras and radar systems find it difficult to quickly and accurately identify and process multiple dynamic changes under low visibility and high traffic density. In complex traffic conditions, the driver's burden is increased, and it is difficult for the system to respond to various emergencies in a timely and effective manner. To solve these problems, the multifunctional integrated vehicle-mounted safety control system based on UWB of the present invention provides a complete solution, which significantly improves safety and response speed.

[0185] In this scenario, the UWB sensor network of the present invention is used to collect multi-level dynamic environmental data around the vehicle in real time. Compared with traditional cameras and radars, UWB sensors have higher accuracy and penetration, and can accurately capture the relative position, speed, and movement direction of pedestrians, cyclists, and other vehicles in haze weather and low visibility. During driving, the UWB sensor senses in real time that a cyclist 50 meters ahead suddenly changes lanes. The system immediately analyzes its movement trajectory based on the data provided by the sensor, and combines the current driving state of the vehicle to quickly evaluate the potential collision risk through a multi-factor weight model. The system predicts that the cyclist's behavior will cause the vehicle to approach within 2 meters of the cyclist within 5 seconds. Based on this risk assessment, the system decides to adjust the path and fine-tune the lane to the right by adjusting the vehicle's driving trajectory, avoiding possible collisions. The entire adjustment process takes only 500 milliseconds, which is about 40% faster than traditional systems, ensuring the safe operation of vehicles in high-density traffic environments.

[0186] When the vehicle passed a busy intersection, the system detected that a car approaching from the side was accelerating. Through the multi-dimensional collision prediction model, the system analyzed the relative position, speed and acceleration of the two vehicles and predicted a 72% risk of collision. The system immediately activated the collision protection mechanism, first sending an audible and visual warning signal to the driver, prompting him to slow down. When the two vehicles were 6 meters apart, the system automatically initiated emergency braking to avoid a potential side collision. The response time of the entire collision warning and protection mechanism is 700 milliseconds, which is 40% less than the reaction time of the traditional collision warning system, ensuring the safety of the vehicle in emergencies.

[0187] In terms of energy management, the present invention uses an adaptive energy scheduling model to dynamically allocate energy according to the real-time driving status and external environmental conditions of the vehicle. In congested traffic, due to frequent start-stop operations, the system prioritizes allocating energy to the braking and steering systems to ensure that key systems can work normally under high load conditions. When the weather suddenly turns bad, the system detects that the rainfall has increased and immediately allocates more energy to the air conditioning and wiper systems, improving the driver's field of vision and comfort. When it detects that the energy consumption is too high, the system further optimizes the energy usage plan, reducing unnecessary energy consumption while ensuring the operation of key safety systems. This energy scheduling strategy improves the overall energy efficiency of the vehicle and avoids energy waste. Especially in emergency situations, it can ensure that key systems such as braking and steering always have sufficient energy supply.

[0188] The system also uses in-vehicle sensors to monitor the passengers' health status in real time, including data such as the passengers' heart rate, breathing rate and body temperature. When it detects that the passenger's heart rate is abnormally high, the system immediately analyzes the current driving route, external environment and weather conditions, and automatically switches the vehicle's driving mode to low-speed mode to reduce the impact of emergency operations on the passengers' physical condition. In this way, the system effectively ensures the safety of passengers in complex traffic environments and responds quickly to health abnormalities. The design of linking passenger health monitoring with driving mode not only enhances the intelligence of the system, but also adds an important layer of protection for vehicle safety management.

[0189] Through real-time data collection of UWB sensors, the system can accurately capture dynamic information of obstacles and external targets within a range of 50 meters, and the perception accuracy is improved by more than 30%. The system completes path adjustment within 500 milliseconds, which is 40% faster than the reaction speed of traditional systems. In terms of collision prediction, the system's reaction time in side collision scenarios is 700 milliseconds, successfully avoiding up to 72% of collision risks. In traffic congestion and bad weather conditions, the energy management system prioritizes 35% of energy for the braking and steering systems, and increases 20% of energy for air conditioning and wipers, ensuring efficient operation of the vehicle. After detecting an abnormal heart rate in a passenger, the health monitoring system completes the switching of driving modes in less than 2 seconds, effectively ensuring the safety of passengers.

[0190] Table 1 Vehicle safety control system related data table

[0191]

[0192] From the table data, we can see that the vehicle has achieved environmental perception within a range of 50 meters through the UWB sensor network, with a perception accuracy of ±10 centimeters, significantly improving the perception ability in complex environments. When encountering emergencies such as pedestrians or cyclists changing lanes, the system can complete the path adjustment within 500 milliseconds to avoid potential collision risks. Through the multi-dimensional collision prediction model, the system can predict 72% of the collision risks under real-time perception, and make a protective response within 700 milliseconds, starting deceleration or emergency braking, successfully avoiding side collisions. In terms of energy management, the system prioritizes 35% of the energy to the braking and steering systems in traffic congestion to ensure the stable operation of key systems. At the same time, when the weather deteriorates, 20% of the energy is allocated to air conditioning and wipers to improve driving comfort and safety. When abnormal passenger health is detected, the system can switch to low-speed or automatic driving mode within 2 seconds to ensure passenger safety, and dynamically adjust the driving mode in combination with the external environment, demonstrating the advantages of intelligent health monitoring and driving linkage.

[0193] In summary, the present invention successfully achieves safe control and energy efficiency optimization of vehicles in complex environments through multiple technical innovations such as sensor networks, adaptive learning algorithms, intelligent energy scheduling and protection mechanisms, significantly improves the overall performance of the system, and has a level of safety and intelligence that cannot be achieved by existing technologies.

[0194] The above description is only a preferred specific implementation manner of the present invention, but the protection scope of the present invention is not limited thereto. Any technician familiar with the technical field can make equivalent replacements or changes according to the technical scheme and inventive concept of the present invention within the technical scope disclosed by the present invention, which should be covered by the protection scope of the present invention.

Claims

1. A multifunctional integrated vehicle safety control method based on UWB, characterized in that: The steps include: S1. Through the UWB sensor network configured around the vehicle, multi-level dynamic environment data around the vehicle and vehicle status information are collected in real time. The sensor automatically adjusts the perception range, sensitivity and frequency according to the dynamic complexity of the external environment and the current driving status of the vehicle, and generates a multi-level three-dimensional environment model through an adaptive learning algorithm; S2. Based on the generated multi-level three-dimensional environmental model, a multi-factor weight model of time priority, space priority, speed priority and dynamic behavior prediction is introduced to predict and evaluate potential risk events. The model dynamically adjusts the risk weight parameters according to changes in the environment and vehicle status, and automatically generates a priority list containing real-time risk events; S3. Based on the generated priority list, the system obtains the dynamic path and behavior intention data of the external target through UWB communication with the external device. The system adjusts the vehicle's path in combination with the behavior prediction results of the external target, and integrates it with the driver's behavior pattern database to intelligently adjust the driving path, speed and driving mode; S4. The system proactively adjusts the vehicle's driving mode based on real-time monitoring of passenger health data, combined with route adjustments and external environmental changes. When passenger health data is abnormal, the system prioritizes switching to low-speed and autonomous driving modes based on environmental complexity and the results of dynamic vehicle route adjustments. S5. Combine the generated 3D environment model with the route adjustment data to predict energy demand based on the current environment, driving status and future driving route. Through the adaptive energy scheduling model, the energy distribution of the vehicle is optimized in real time. The system pre-plans the energy consumption priority in different scenarios based on external factors, and gives priority to ensuring the energy supply of key equipment in an emergency. S6. Based on the generated three-dimensional environment model and path adjustment information, the system uses a multi-dimensional collision prediction model to predict complex collision scenarios. The system predicts potential collision types by analyzing the relative position, speed, acceleration and deceleration trends, and direction changes between the vehicle and obstacles and external targets. According to the severity and probability of collision prediction, the system gradually activates a phased adaptive multi-level protection mechanism; S7. The system analyzes the driver's current state and external environment based on the driver's behavior pattern library and the behavior pattern fusion mechanism. When the driver approaches the vehicle, the system automatically matches his behavior pattern and adjusts the settings in the vehicle. It also optimizes the vehicle startup process and provides a seamless entry experience based on the vehicle's current energy state and the driver's behavior pattern. S8. Through the multi-scenario adaptive learning algorithm, the driving assistance functions in each scenario are optimized and adjusted with multiple objectives. The acceleration, steering and braking responses are optimized based on the external environment changes, the driver's operating mode and the vehicle performance. In similar environments, the system further optimizes the driving strategy based on the previous optimization results to achieve vehicle adaptive multi-scenario learning.

2. The multifunctional integrated vehicle safety control method based on UWB according to claim 1 is characterized in that: The S1 specifically includes: S11, by configuring multiple UWB sensors around the vehicle, collecting multi-level dynamic environmental data around the vehicle and in-vehicle status information in real time, the environmental data including the spatial position of obstacles, relative speed, terrain changes, weather conditions and traffic flow density, and classifying the data into static environmental data and dynamic environmental data; S12. Based on the collected static and dynamic environmental data, conduct real-time assessment of environmental complexity and adjust the sampling frequency and accuracy of sensors in real time to adapt to complex changes in the environment: Among them, C e represents the complexity of the environment, T represents the traffic flow density, W represents the impact of weather conditions, V represents the current speed of the vehicle, represents the relative movement change of the obstacle, α and β are adjustment factors, and ∈ is the stability term; S13. Based on the evaluated environmental complexity results, the system dynamically adjusts the data weights of each sensor through an adaptive learning algorithm: in, is the weight of sensor data, δ is the adaptive learning rate, is the result of weight adjustment according to the complexity of the environment, γ represents the weight factor for obstacle changes, and η is the balance coefficient; S14. After adjusting the sensor weights, the system generates a multi-level three-dimensional environment model based on the weighted sensor data. The model includes the location of dynamic obstacles, the slope change of the terrain, the traffic flow density and its change trend. The three-dimensional environment model is continuously updated according to the real-time data and the changes in the sensor weights: Among them, M 3D is the three-dimensional environment model, P i (t) represents the data packet collected by the sensor at time t, W s (t) is the sensor weight at that moment.

3. The multifunctional integrated vehicle safety control method based on UWB according to claim 1 is characterized in that: The S2 specifically includes: S21, the system obtains factor information related to time, space, and speed based on the generated multi-level three-dimensional environmental model; S22. Introduce a multi-factor weight model of time priority, space priority, and speed priority to perform weighted calculations on time, space, and speed factors: Among them, R w is the risk weight, T f is the time factor, S f is the space factor, V f is the speed factor, α, β, γ are weight coefficients, and Δt is the time change; S23. Based on the weight model, the system introduces dynamic behavior prediction factors to predict the behavior path of external targets. It uses the movement information of external targets, combined with historical data and real-time data, to predict their future behavior: Among them, P d represents the dynamic behavior prediction value, O i (t) is the moving trajectory of the external target at time t, V t is the current speed of the vehicle, S i is the distance between the target and the vehicle; S24, the system predicts the value P based on time, space, speed factors and dynamic behavior d , generate a priority list of risk events, sort through the priority list, high priority events are processed first, and low priority events enter the waiting queue.

4. The multifunctional integrated vehicle safety control method based on UWB according to claim 1 is characterized in that: The S3 specifically includes: S31, the system communicates with the external device through UWB according to the generated priority list to obtain the dynamic path and behavior intention data of the external target, wherein the external target data includes the relative position, speed, direction and behavior prediction of the target; S32, route adjustment combined with external behavior prediction The system performs route adjustment based on the dynamic behavior prediction value of the external target and the driving state of the vehicle: Among them, P adj is the path adjustment amount, V t is the current speed of the vehicle, S o is the relative distance of the external target, P d is the dynamic behavior prediction value, λ1 is the adjustment coefficient, T r is the response time, μ1 is the prediction weight, ∈ is the stability term; S33, the system combines the behavior prediction of external targets with the driver's historical behavior pattern to optimize the vehicle's path, speed and driving mode, and intelligently adjusts the vehicle based on the driver's behavior pattern and changes in the current external environment; S34, adjust P based on path adj And intelligent adjustment, dynamically adjust the vehicle's driving speed and driving mode, and the speed and path adjustments are carried out in conjunction, so that the vehicle can maintain driving safety and stability in a complex external environment. By real-time analysis of external target data and the driver's behavior patterns, the optimal driving mode and speed adjustment plan are selected.

5. The multifunctional integrated vehicle safety control method based on UWB according to claim 1 is characterized in that: The S4 specifically includes: S41. Monitor the health status of passengers in real time through sensors in the vehicle, including heart rate, breathing rate, body temperature and posture changes, conduct real-time analysis based on the health status data, and identify any abnormalities; S42, combining the acquired health status data with the current driving route and external environmental conditions. If the passenger's health status is abnormal, the system decides whether to adjust the driving mode by comprehensively considering the current driving route and weather conditions; S43, the system adjusts the path according to the passenger health data and the amount P adj , determine whether the vehicle's speed and path need to be adjusted: Among them, H adj is the adjustment amount based on health status, H s Indicates the change of passenger health status, P adj is the path adjustment amount, θ is the adjustment coefficient, T h is the time interval for health data collection, ∈ h is a stable term; S44. When the system detects an abnormal health condition of a passenger, it will automatically switch to a low-speed mode or an automatic driving mode based on the complexity of the road and weather conditions. The switching of the driving mode will be based on the urgency of the passenger's health condition and the risk assessment results of the external environment. S45. By real-time monitoring of the health status of passengers and continuously updating the three-dimensional environmental model in combination with UWB communication of external devices, the system further optimizes the driving mode and path adjustment based on the updated environmental model, so as to match the health status of passengers with the driving environment and provide a real-time responsive driving solution.

6. The multifunctional integrated vehicle safety control method based on UWB according to claim 1 is characterized in that: The S5 specifically includes: S51, based on the current driving path, external environment and vehicle operating status, predict the energy demand in different future driving scenarios combined with real-time road conditions, driving speed and vehicle load, so that the energy demand matches the actual driving scenario, and the system adjusts the energy prediction value in real time according to the changing environmental conditions to optimize energy utilization; S52, dynamically adjusting energy demand by real-time monitoring of external environmental data. After combining external factors with energy demand, the energy allocation scheme can be adjusted according to the complexity of different driving scenarios to maximize vehicle energy efficiency and driving performance; S53. The system dynamically optimizes the energy distribution of the vehicle using an adaptive energy scheduling model based on the energy demand prediction results and external environmental conditions, adjusts the energy distribution of each key system in real time, and optimizes the energy distribution strategy based on the driver's driving habits and historical driving data through a built-in self-learning algorithm; S54. When abnormal health conditions of passengers, bad weather or traffic accidents are detected, the system will prioritize energy allocation to key safety systems. In emergency situations, the system will adjust energy priorities according to the risk level of the emergency, so that key systems can maintain normal operation even when energy is insufficient. S55. The system can further optimize energy demand based on real-time feedback from external data in extreme environments. In extremely low temperature environments, the system gives priority to providing energy to key systems and adjusts energy consumption based on real-time feedback to adapt to special scenarios of low temperature and high energy consumption. S56. The system continuously monitors the vehicle's energy consumption data and external feedback information, and optimizes the energy scheduling strategy through a real-time adaptive learning algorithm. This feedback mechanism allows the system to respond quickly to changes in the external environment and dynamically adjust energy distribution, so that the vehicle can achieve energy efficiency optimization and ensure driving safety in complex and emergency environments.

7. The multifunctional integrated vehicle safety control method based on UWB according to claim 1 is characterized in that: The S6 specifically includes: S61. By collecting multi-dimensional information of the vehicle's speed, acceleration, relative position and direction change of external targets, the system builds a collision prediction model to calculate potential collision scenarios. The system updates the collision prediction in real time based on the multi-dimensional information: C pred =f(V t ,a t ,O d ,Δθ); Among them, C pred is the collision prediction value, V t represents the current speed of the vehicle, a t is the current acceleration, O d is the relative distance of the external obstacle, Δθ is the direction change between the vehicle and the obstacle; S62. Based on the collision prediction results, the system calculates the potential collision risk level, and the system adjusts the risk level according to the current driving state of the vehicle and external environmental factors: Among them, R level is the collision risk level, W e is the environmental impact factor, E s is the vehicle status influencing factor, ∈ r is the stability coefficient; S63: When the system detects a high-risk collision, it gradually activates the protection mechanism according to the risk level, including early warning and deceleration prompts, activating higher-level protection systems, and activating each protection measure in sequence to ensure the safety of the vehicle and passengers; S64. After the protection mechanism is activated, the system continuously monitors the vehicle's speed, acceleration and dynamic information of external obstacles, and adjusts the protection measures according to the real-time collision prediction results, so that the protection plan is optimized as the external environment changes; S65: The system works with the energy management system to prioritize energy allocation for key protection systems. If the vehicle is in a scenario of insufficient energy or high energy consumption, the system will automatically adjust the energy allocation plan to enable key protection systems to work normally. S66. When the protection mechanism is activated, the system makes dynamic adjustments based on the driver's operation. The system optimizes the protection plan based on the driver's operation input, so that the driver's operation is properly responded to, thereby optimizing the execution effect of the protection mechanism.

8. The multifunctional integrated vehicle safety control method based on UWB according to claim 1 is characterized in that: The S8 specifically includes: S81. Collect acceleration, braking, and steering response data of the vehicle and external environment data according to different driving environments, and construct response models under different driving scenarios to reflect the different effects of each scenario on vehicle behavior; S82. Through adaptive learning algorithms, the vehicle's acceleration, braking and steering strategies are dynamically adjusted based on the collected multi-scenario data. The system optimizes the vehicle's response behavior based on the driver's historical operating patterns and real-time external environment changes: R opt =f(V t ,A s ,T r ,E e ); Among them, R opt is the optimized vehicle response, V t is the current speed of the vehicle, A s is the acceleration parameter, T r is the steering response time, E e For external environmental factors; S83. The system adjusts driving strategies in different scenarios based on the optimization results. In urban road scenarios, low-speed steering control is given priority. On highways, the system adjusts the balance between acceleration and braking. On mountain roads, the system focuses on the linkage control of steering and braking. The system dynamically adjusts the response speed and strength of acceleration, braking and steering according to specific scenarios. S84. The system combines historical data with real-time data to update the model based on the learning results and continuously updates the adaptive learning model: M upd =M prev +α×ΔR; Among them, M upd is the updated model, M prev are the previous model parameters, α is the learning rate, and ΔR is the response adjustment amount; S85. By real-time monitoring of the driver's operation and vehicle operating status, combined with changes in the external environment, the acceleration, braking and steering strategies of the vehicle are adjusted, and the vehicle's response is adjusted at any time according to the driver's operation frequency, road complexity and vehicle dynamics; S86. The system improves the global driving strategy in multiple scenarios through long-term driving data accumulation. Whenever the vehicle operates in a similar driving scenario, the system optimizes based on historical data and adjusts the strategy in the same and similar scenarios in the future, optimizing the global driving strategy through long-term data accumulation.

9. A multifunctional integrated vehicle safety control system based on UWB, characterized in that: Includes the following modules: UWB sensor network module: collects multi-level dynamic environmental data around the vehicle and in-vehicle status information in real time, and adjusts the sensing range, sensitivity and acquisition frequency according to the dynamic complexity of the external environment and the driving status of the vehicle; Adaptive learning algorithm module: Generates a multi-level three-dimensional environmental model based on the collected data, dynamically adjusts sensor weights, optimizes model generation and updating, and provides real-time environmental feedback; Multi-factor weight assessment module: Through the multi-factor weight model of time priority, space priority, and speed priority, it predicts potential risks, dynamically adjusts weight parameters, and generates a priority list containing real-time risk events; Behavior prediction and path adjustment module: According to the dynamic path and behavior intention of the external target, combined with the driver's behavior pattern and vehicle status, the vehicle's driving path, speed and driving mode are adjusted to achieve intelligent adjustment; Passenger health monitoring module: monitors the health status of passengers through in-vehicle sensors, automatically adjusts the driving mode based on the driving path and environmental changes, and switches to low-speed or autonomous driving mode when abnormal health data is detected; Energy scheduling module: optimizes energy allocation to key systems in real time based on energy demand forecast results and external environmental conditions, and prioritizes energy allocation to safety systems in emergency situations; Collision prediction and protection module: Based on a multi-dimensional collision prediction model, it evaluates the relative position, speed, and acceleration of the vehicle, obstacles, and external targets in real time, and activates a step-by-step protection mechanism to deal with different collision risk levels; Driving assistance optimization module: Based on a multi-scenario adaptive learning algorithm, it optimizes acceleration, braking and steering response strategies. Through long-term data accumulation, it adjusts the global driving strategy and optimizes the vehicle's driving performance in different driving environments.

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