Vehicle risk avoiding control method and device based on multi-mode vital sign detection and electronic equipment

Abstract

The invention relates to the technical field of vehicle control, and provides a vehicle risk avoiding control method and device based on multi-mode vital sign detection and electronic equipment. Comprising the following steps: acquiring respiratory rate, heart rate, body temperature and brain wave data of a driver, and calculating a sign abnormity index based on the acquired data; if the sign abnormity index is greater than or equal to a preset index and the vehicle speed is less than or equal to a preset vehicle speed, executing a first-level response; if the sign abnormity index is larger than or equal to the preset threshold value and the vehicle speed is larger than the preset vehicle speed, second-level response is executed; wherein the first-level response comprises outputting an early warning signal, and the second-level response comprises controlling safe parking of the vehicle. Through multi-mode vital sign detection and a grading response strategy for risk avoidance, sudden health crisis of the driver can be reliably handled, and the driving safety is improved.

Description

Technical Field

[0001] The present application relates to the field of vehicle control technology, and in particular to a vehicle risk avoidance control method, device and electronic equipment based on multimodal vital sign detection. Background Art

[0002] Existing vehicle health monitoring systems often rely on steering wheel torque detection or single physiological sensors (such as heart rate monitors), making it difficult to accurately identify sudden health risks. For example, when determining coma solely based on a sudden drop in heart rate, normal behaviors such as brief eye closure and body temperature fluctuations can easily lead to misjudgments, resulting in frequent false triggering of the system. These false alarms not only degrade the user experience but can also potentially trigger secondary accidents caused by emergency braking.

[0003] Traditional evasive control lacks a scenario-based mechanism. Directly triggering emergency braking in low-speed, congested situations can cause the vehicle to stop unexpectedly in traffic and lead to a rear-end collision. In high-speed situations, it only uses audible and visual alarms, failing to promptly take over the vehicle should the driver become completely incapacitated. This indiscriminate response strategy fails to address safety requirements at varying speeds and lacks comprehensive evasive path planning capabilities. Loss of control can lead to the vehicle swerving into the oncoming lane or colliding with obstacles.

[0004] In summary, existing technologies are difficult to reliably respond to sudden health crises of drivers. Summary of the Invention

[0005] In view of this, the embodiments of the present application provide a vehicle hazard avoidance control method, device and electronic equipment based on multimodal vital signs detection. Through multimodal vital signs detection and a graded response strategy for hazard avoidance, it can reliably respond to sudden health crises of the driver and improve driving safety.

[0006] A first aspect of an embodiment of the present application provides a vehicle risk avoidance control method based on multimodal vital sign detection, comprising: Collect the driver's respiratory rate, heart rate, body temperature and brain wave data, and calculate the abnormal vital signs index based on the collected data; If the abnormal vital sign index is greater than or equal to the preset index, and the vehicle speed is less than or equal to the preset speed, a first-level response is executed; If the abnormal vital sign index is greater than or equal to a preset threshold, and the vehicle speed is greater than a preset speed, executing a secondary response; Among them, the first-level response includes outputting a warning signal, and the second-level response includes controlling the vehicle to stop safely.

[0007] In one embodiment, the abnormal physical sign index is calculated by the following formula: ; Among them, HR represents the collected heart rate, HR 基线Indicates the heart rate baseline value, σ HR represents the standard deviation of the heart rate history data, RR represents the respiratory rate obtained, and RR 基线 Represents the baseline value of respiratory rate, σ RR Represents the standard deviation of respiratory rate historical data, Δ T Indicates the temperature change collected per unit time. σ T represents the standard deviation of body temperature variation, E α Indicates the collected α wave energy value, E β Indicates the collected β wave energy value, w 1. w 2. w 3. w 4 is the weight coefficient.

[0008] In one embodiment, the heart rate baseline value is calculated based on the driver's resting heart rate data collected over multiple consecutive days.

[0009] In one embodiment, the secondary response includes: Based on the environmental data obtained by the environmental perception unit and combined with high-precision map data, safe parking path planning is carried out to generate a planned path; Control the vehicle to travel along the planned path and stop at the designated stop location.

[0010] In one embodiment, while controlling the vehicle to travel along the planned path, a collision time is also calculated. If the collision time is less than a preset time threshold, the path is replanned.

[0011] In one embodiment, controlling the vehicle to travel along the planned path and dock at a designated docking location includes: The vehicle's deceleration is controlled in sections through the wire control brake system, and the wheel angle is adjusted based on the four-wheel independent steering system, so that the vehicle can stop at the designated parking position along the planned path.

[0012] In one embodiment, the safe parking path planning is performed and when generating the planned path, the path curvature radius is greater than or equal to the minimum turning radius that can be executed by the four-wheel steering system, the right emergency lane or shoulder is preferentially selected as the designated parking position, and the width of the designated parking position is greater than or equal to the preset width.

[0013] In one embodiment, controlling the vehicle to travel along the planned path and dock at a designated docking location further includes: The main controller is used to control the vehicle to travel along the planned path and stop at the designated stop position; When the main controller fails, the switching sub-controller controls the vehicle to travel along the planned path and stop at a designated stop position.

[0014] The second aspect of the embodiment of the application provides a vehicle safety control device based on multi-modal vital sign detection, comprising: The acquisition module is configured to acquire the breathing rate, heart rate, body temperature and brain wave data of the driver, and calculate a vital sign abnormality index based on the acquired data. The control module is configured to execute a first-level response if the vital sign abnormality index is greater than or equal to a preset index and the vehicle speed is less than or equal to a preset vehicle speed, and execute a second-level response if the vital sign abnormality index is greater than or equal to a preset threshold and the vehicle speed is greater than the preset vehicle speed. The first-level response comprises outputting a warning signal, and the second-level response comprises controlling the vehicle to safely stop.

[0015] The third aspect of the embodiment of the application provides an electronic device, comprising a processor, a memory, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to enable the electronic device to implement the vehicle safety control method based on multi-modal vital sign detection provided by the first aspect of the embodiment of the application.

[0016] The fourth aspect of the embodiment of the application provides a computer program product comprising a computer program, which, when executed, causes the method according to the first aspect of the embodiment of the application to be performed.

[0017] The vehicle safety control method based on multi-modal vital sign detection provided by the first aspect of the embodiment of the application comprises: acquiring the breathing rate, heart rate, body temperature and brain wave data of the driver, and calculating a vital sign abnormality index based on the acquired data; executing a first-level response if the vital sign abnormality index is greater than or equal to a preset index and the vehicle speed is less than or equal to a preset vehicle speed; and executing a second-level response if the vital sign abnormality index is greater than or equal to a preset threshold and the vehicle speed is greater than the preset vehicle speed; wherein the first-level response comprises outputting a warning signal, and the second-level response comprises controlling the vehicle to safely stop. By synchronously acquiring four types of heterogeneous physiological data, i.e., breathing, heart rate, body temperature and brain wave, the technical bottleneck of high false positive rate of a single sensor is broken. The introduction of the vital sign abnormality index realizes quantitative evaluation of the physiological state, and the double determination conditions of the vehicle speed threshold make the first-level response (low-speed warning) and the second-level response (high-speed safety) form a logical closed loop. This hierarchical control strategy significantly reduces the probability of false triggering while ensuring safety, and provides a basic decision logic for autonomous safety of the vehicle.

[0018] It can be understood that the beneficial effects of the second aspect to the fourth aspect described above can be referred to the related description in the first aspect described above, and will not be repeated here. BRIEF DESCRIPTION OF DRAWINGS

[0019] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings needed to be used in the embodiments or prior art description will be briefly introduced as follows. Obviously, the drawings in the following description only some embodiments of the present application, and for those skilled in the art, other drawings can also be obtained without creative labor on the basis of these drawings.

[0020] Figure 1 is a flowchart of a vehicle risk avoidance control method based on multi-modal vital sign detection provided by an embodiment of the present application; Figure 2 is a flowchart of a vehicle risk avoidance control method based on multi-modal vital sign detection provided by another embodiment of the present application; Figure 3 is a structural schematic diagram of a vehicle risk avoidance control device based on multi-modal vital sign detection provided by an embodiment of the present application; Figure 4 is a structural schematic diagram of an electronic device provided by an embodiment of the present application. DETAILED DESCRIPTION

[0021] In the following description, specific details such as specific system structures, techniques, etc. are presented in order to thoroughly understand the embodiments of the present application. However, it should be clear to those skilled in the art that the present application can also be implemented in other embodiments without these specific details. In other cases, detailed descriptions of well-known systems, devices, circuits and methods are omitted to avoid unnecessary details that hinder the description of the present application.

[0022] It should be understood that when used in the specification and the appended claims of the present application, the term "comprising" indicates the presence of the described features, whole, steps, operations, elements and / or components, but does not exclude the presence or addition of one or more other features, whole, steps, operations, elements, components and / or sets thereof.

[0023] It should also be understood that the term "and / or" used in the specification and the appended claims of the present application means any combination of one or more of the associated listed items and all possible combinations, and includes these combinations.

[0024] As used in this specification and the appended claims, the term "if" can be interpreted as "when" or "upon" or "in response to determining" or "in response to detecting," depending on the context. Similarly, the phrase "if it is determined" or "if [described condition or event] is detected" can be interpreted as meaning "upon determination" or "in response to determining" or "upon detection of [described condition or event]" or "in response to detecting [described condition or event]," depending on the context.

[0025] In addition, in the description of the present application specification and the appended claims, the terms "first", "second", "third", etc. are only used to distinguish the descriptions and cannot be understood as indicating or implying relative importance.

[0026] References to "one embodiment" or "some embodiments" in this specification mean that a particular feature, structure, or characteristic described in conjunction with that embodiment is included in one or more embodiments of the present application. Thus, phrases such as "in one embodiment," "in some embodiments," "in other embodiments," and "in other embodiments" appearing in various places in this specification do not necessarily refer to the same embodiment, but rather mean "one or more but not all embodiments," unless otherwise specifically emphasized. The terms "including," "comprising," "having," and variations thereof all mean "including but not limited to," unless otherwise specifically emphasized.

[0027] like Figure 1 As shown, the vehicle risk avoidance control method based on multimodal vital sign detection provided by the embodiment of the present application includes the following steps S101 to S103: Step S101: collecting the driver's respiratory rate, heart rate, body temperature and brain wave data, and calculating the abnormal vital sign index based on the collected data; Step S102: If the abnormal vital sign index is greater than or equal to a preset index and the vehicle speed is less than or equal to a preset speed, executing a first-level response; Step S103: If the abnormal vital sign index is greater than or equal to the preset threshold, and the vehicle speed is greater than the preset speed, executing a secondary response; Among them, the first-level response includes outputting a warning signal, and the second-level response includes controlling the vehicle to stop safely.

[0028] In the application, the Level 1 response warning signal is output as an audible and visual alarm to prompt the driver to take over. The audible and visual alarm can include a 5Hz steering wheel vibration and a red flashing instrument panel (brightness 500cd / m²). If the Level 1 response is triggered three times consecutively within 10 minutes, the data is automatically uploaded to the cloud for manual review.

[0029] In the application, the preset index can be set to 0.8, and the preset speed can be set to 30km / h. The preset index and preset speed are set values ​​and can be dynamically adjusted according to actual needs.

[0030] This embodiment of the application overcomes the technical bottleneck of high false positive rates associated with single sensors by simultaneously collecting four types of heterogeneous physiological data: respiration, heart rate, body temperature, and brain waves. The introduction of a vital sign abnormality index enables quantitative assessment of physiological status. Combined with the dual judgment criteria of vehicle speed thresholds, this creates a logically closed loop between the primary response (low-speed warning) and the secondary response (high-speed avoidance). This layered control strategy significantly reduces the probability of false triggers while ensuring safety, providing the fundamental decision-making logic for autonomous vehicle avoidance.

[0031] In one embodiment, the abnormal physical sign index is calculated by the following formula: ; Among them, HR represents the collected heart rate, HR 基线 Indicates the heart rate baseline value, σ HR represents the standard deviation of the heart rate history data, RR represents the respiratory rate obtained, and RR 基线 Represents the baseline value of respiratory rate, σ RR Represents the standard deviation of respiratory rate historical data, Δ T Indicates the temperature change collected per unit time. σ T represents the standard deviation of body temperature variation, E α Indicates the collected α wave energy value, E β Indicates the collected β wave energy value, w 1. w 2. w 3. w 4 is the weight coefficient. In the embodiment of the present application, w 1. w 2. w 3. w 4 take the values ​​as 0.3, 0.3, 0.2, and 0.2 respectively.

[0032] In applications, such as Figure 2As shown, the millimeter wave radar is used to collect respiratory and heart rate data, the infrared thermal imaging is used to acquire question data, and the brain wave sensor is used to acquire brain wave data. The working frequency of the millimeter wave radar is 60GHz, and the respiratory frequency detection accuracy is ±1 / minute; the infrared thermal imaging sensor dynamically tracks the ROI area of the driver's face, and the temperature resolution is ≤0.1℃; the brain wave sensor determines the consciousness state through the energy ratio of alpha wave (8-13Hz) and beta wave (14-30Hz), and the sampling frequency is ≥256Hz.

[0033] In application, the data collected by the millimeter wave radar, the infrared thermal imaging and the brain wave sensor are also processed through a synchronization mechanism. Including: Timestamp alignment: PTP (Precision Time Protocol) is used to unify the data of each sensor to the same time reference, with an error of ≤1ms.

[0034] Multi-source data fusion: Millimeter wave radar: output respiratory frequency (10Hz), heart rate (10Hz) original signal, extract effective frequency band (respiration: 0.1-0.5Hz, heart rate: 0.8-3Hz) after FFT filtering.

[0035] Infrared thermal imaging: output the average temperature of the driver's forehead ROI area at a frequency of 5Hz, and eliminate the interference of sunlight reflection on the vehicle window by background difference method.

[0036] Brain wave sensor: collect 256Hz original EEG signal, calculate alpha / beta wave energy ratio after band pass filtering (8-30Hz), and update every 0.5 seconds.

[0037] In application, the collected data is also subjected to Kalman filtering, wherein the Kalman filtering parameters are as follows: State variable: respiratory frequency, heart rate, body temperature, alpha / beta wave energy ratio. Process noise covariance Q=diag(0.01, 0.01, 0.1, 0.05). Observation noise covariance R=diag(0.1, 0.2, 0.05, 0.1).

[0038] The application also provides a millimeter wave radar, infrared thermal imaging and brain wave sensor related parameters as follows: The embodiment of the application provides a complete mathematical expression of a VAI calculation formula, which converts an abstract vital sign abnormality degree into a calculable physical quantity. Four parameters in the formula correspond to heart rate deviation, respiration rate deviation, body temperature change rate and brain wave energy ratio respectively, and comprehensively cover the core physiological indicators defined in the disclosure. The standardization processing of each parameter (the measured value and baseline deviation divided by the standard deviation) effectively eliminates the influence of individual differences, and the introduction of the weight coefficient strengthens the dominant role of heart rate and respiration, and ensures the robustness of the algorithm under complex working conditions.

[0039] In one embodiment, the heart rate baseline value is calculated based on the driver's resting heart rate data collected continuously for multiple days.

[0040] In application, the driver's resting state data is collected continuously for 7 days (≥30 minutes per day), and the mean value ±3σ is calculated as the initial threshold.

[0041] The embodiment of the application establishes a dynamic personalized benchmark. By long-term monitoring and learning the normal physiological characteristics of the driver, the misjudgment of the static threshold to special physical people (such as athletes with low resting heart rate) is avoided. The statistical characteristics of the historical data enable the system to adaptively adjust the abnormal judgment boundary, and improve the compatibility of chronic disease patients, which is a key technical support for realizing precision medical level detection.

[0042] In one embodiment, the secondary response includes: Based on the environment data obtained by the environment perception unit, combined with high-precision map data, a safe parking path is planned, and a planned path is generated; The vehicle is controlled to travel according to the planned path and park to a specified parking position.

[0043] In application, the environment is perceived based on the fusion of forward millimeter wave radar (detection distance ≥ 150m) and surround-view camera, and a safe parking path is generated combined with high-precision map, and an emergency lane or shoulder is preferentially selected. For curved road or obstacle scenes, the wheel angle is adjusted through the four-wheel independent steering system to ensure the stability of lane changing or side parking of the vehicle. At the same time, the electronic stability program (ESP) and the brake-by-wire system (EHB) can also be coordinated to reduce the speed by 0.3g to avoid rear-end collision caused by emergency braking.

[0044] In application, the related parameters of the environment perception unit (including forward millimeter wave radar and surround-view camera) and the warning map are as follows: In application, the related parameters of the four-wheel independent steering system, the electronic stability program (ESP) and the brake-by-wire system (EHB) are as follows: The integrated application of environmental perception and high-precision maps in the secondary response of this embodiment addresses the blind spots in traditional autonomous driving path planning during emergency avoidance. By constructing a joint spatial model of sensor data and map information in real time, the system can quickly generate a safe path that takes into account both traffic regulations and terrain constraints. This collaborative perception mechanism enables the vehicle to autonomously select the optimal stopping point even when the driver is incapacitated, significantly improving the probability of survival in complex road conditions.

[0045] In one embodiment, while controlling the vehicle to travel along the planned path, a collision time is also calculated. If the collision time is less than a preset time threshold, the path is replanned.

[0046] In the application, if there is a moving obstacle in the target path (such as a rapidly approaching vehicle from behind), calculate TTC (Time to Collision): TTC = (Distance from vehicle to obstacle) / (Vehicle speed - Obstacle speed). If TTC is less than 5 seconds, re-route to the left lane (must meet lane change conditions).

[0047] The inclusion of a collision time threshold mechanism in this embodiment enables dynamic risk prediction. TTC (Time to Collision) is continuously calculated during path execution, triggering path replanning when the potential collision risk exceeds a preset threshold. This creates a closed-loop control loop of perception, decision-making, execution, and feedback. This design effectively addresses path failures caused by unexpected obstacles (such as vehicles approaching at high speed), enabling the hazard avoidance system to adapt to real-time environments.

[0048] In one embodiment, controlling the vehicle to travel along the planned path and dock at a designated docking location includes: The vehicle's deceleration is controlled in sections through the wire control brake system, and the wheel angle is adjusted based on the four-wheel independent steering system, so that the vehicle can stop at the designated parking position along the planned path.

[0049] The coordinated control of wire-controlled braking and four-wheel steering in this embodiment surpasses the responsiveness limits of traditional chassis systems. The segmented deceleration strategy balances deceleration efficiency and vehicle stability through a braking curve that begins slowly and ends quickly. Four-wheel independent steering implements Ackermann geometry correction, and these two elements work together to ensure the vehicle accurately tracks the emergency path at high speeds. This electromechanical coupling control solution eliminates understeer or sideslip caused by sudden braking, providing execution-level support for safe parking.

[0050] In one embodiment, the safe parking path planning is performed and when generating the planned path, the path curvature radius is greater than or equal to the minimum turning radius that can be executed by the four-wheel steering system, the right emergency lane or shoulder is preferentially selected as the designated parking position, and the width of the designated parking position is greater than or equal to the preset width.

[0051] In application, the priority of the specified stop position is as follows: First priority: emergency lane (width ≥ 2.5m, no static obstacle).

[0052] Second priority: right shoulder (width ≥ 1.5m).

[0053] Third priority: current lane slow down to stop (turn on double flash, V2X broadcast).

[0054] The triple constraint conditions (radius of curvature, lane priority, width threshold) of the path planning of the embodiments of the application establish a quantifiable safety evaluation standard. The minimum turning radius limit ensures that the path meets the mechanical characteristics of the vehicle, the emergency lane priority strategy meets the requirements of traffic regulations, and the width threshold guarantees the feasibility of stopping. This path generation algorithm based on physical constraints converts abstract safety requirements into executable mathematical boundary conditions, avoiding planning mechanically infeasible paths.

[0055] In one embodiment, the control vehicle travels according to the planned path and stops at the specified stop position, further comprising: using a main controller to control the vehicle to travel according to the planned path and stop at the specified stop position; when the main controller fails, switching to a backup controller to control the vehicle to travel according to the planned path and stop at the specified stop position.

[0056] In application, the parameters of the dual processing architecture of the main controller and the backup controller are as follows: The dual controller hot backup architecture of the embodiments of the application realizes system-level fault tolerance through a fault detection and seamless switching mechanism. The main processor is responsible for regular decision-making, and the backup processor continuously monitors its running state. Once a fault is detected, it immediately takes over control. This redundant design ensures that in extreme cases such as processor failure (such as crash, bus error), the risk avoidance process can still be executed completely, greatly improving the functional safety level of the system.

[0057] In one embodiment, an emergency power module is also provided, which is independent of the super capacitor group of the vehicle battery, ensuring that the system can still operate for at least 10 minutes after the vehicle is powered off.

[0058] In application, the capacity of the super capacitor group is 100F, the voltage is 48V, and the peak power is 5kW (for 10 minutes). The priority power supply is: electronic brake system (EHB), steering motor, V2X communication module. The performance verification test data of the super capacitor group provided by the embodiments of the application are as follows: The multi-level redundancy design (dual processors + emergency power supply) in this embodiment ensures system operation even under extreme conditions. Multi-sensor data fusion prevents false triggering due to environmental interference (such as elevated body temperature caused by high temperatures). The system is compatible with both traditional fuel vehicles and new energy vehicles, supporting CAN / FlexRay bus protocols.

[0059] In one embodiment, if any wheel steering motor fails to respond, differential braking (applying 10 bar pressure to the opposite wheel) is activated to assist steering. If the V2X module fails, it switches to the cellular network (LTE Cat. 12) to transmit a distress signal.

[0060] In one embodiment, the timing constraints of the entire process of this method are as follows: It should be understood that the size of the serial numbers of the steps in the above embodiments does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of this application.

[0061] The present embodiment of the application conducts the following test on a highway driver experiencing a sudden myocardial infarction. The test conditions are: Vehicle model: Pure electric vehicle (curb mass 1800kg, wheelbase 2.8m, track 1.6m).

[0062] Sensor configuration: millimeter-wave radar (TI AWR2944, 60GHz, breathing detection error ±1 breath / minute); infrared thermal imaging (FLIR ADK™, resolution 320×240); brainwave sensor (NeuroSky MindWave Mobile 2, sampling rate 512Hz).

[0063] Environmental parameters: daytime, dry asphalt road, vehicle speed 110km / h, straight road, right emergency lane available width 2.2m.

[0064] The execution process and data records are as follows: The present embodiment of the application conducts the following test on a driver coma scenario on a city curve. The test conditions are: Road environment: Curved urban main road (curvature radius 50m), vehicle speed 60km / h, parallel vehicles in the left lane (distance 15m, relative speed -5km / h).

[0065] Driver's status: Sudden cerebral hemorrhage leading to loss of consciousness (alpha waves accounted for 82% and lasted for 5 seconds).

[0066] The control strategy is as follows: The initial planned right lane change was blocked (by a guardrail on the right), so the system switched to the left lane and stopped. The turning radius was dynamically adjusted to R = 8m (four-wheel steering coordination: δ1 = 18°, δ2 = 6°, K = 0.33). Due to the center of gravity shift in the curve, braking deceleration was limited to 0.25g (brake pressure 28 bar) to prevent skidding. The left vehicle's TTC was calculated in real time to be 4.2 seconds (less than the 5-second threshold), triggering emergency braking at 0.4g (brake pressure 45 bar).

[0067] This embodiment of the application performs system redundancy verification (main processor failure). The simulated fault injection is as follows: The fault type is: the main MCU (NXP S32G274A) crashes at t = 2 seconds (CAN bus unresponsive). The test scenario is: vehicle speed 80 km / h, driver incapacitation detection is triggered. The redundancy switching process is as follows: The present application also provides a vehicle hazard avoidance control device based on multimodal vital sign detection, configured to execute the steps of the aforementioned vehicle hazard avoidance control method based on multimodal vital sign detection. The vehicle hazard avoidance control device based on multimodal vital sign detection can be a virtual appliance within an electronic device, executed by the electronic device's processor, or it can be the electronic device itself.

[0068] like Figure 3 As shown, the vehicle risk avoidance control device 100 based on multimodal vital sign detection provided by the embodiment of the present application includes: The acquisition module 101 is used to collect the driver's respiratory rate, heart rate, body temperature and brain wave data, and calculate the abnormal vital sign index based on the collected data; The control module 102 is configured to execute a primary response if the abnormal vital sign index is greater than or equal to a preset index and the vehicle speed is less than or equal to a preset speed; and execute a secondary response if the abnormal vital sign index is greater than or equal to a preset threshold and the vehicle speed is greater than a preset speed; Among them, the first-level response includes outputting a warning signal, and the second-level response includes controlling the vehicle to stop safely.

[0069] In application, each module in the vehicle risk avoidance control device based on multimodal vital signs detection can be a software program module, or can be implemented by different logic circuits integrated in a processor, or can be implemented by multiple distributed processors.

[0070] like Figure 4 As shown, the embodiment of the present application further provides an electronic device 200, including: at least one processor 201 ( Figure 4Only one processor is shown in the figure), a memory 202, and a computer program 203 stored in the memory 202 and executable on at least one processor 201. When the processor 201 executes the computer program 203, the steps in the above-mentioned method embodiments are implemented.

[0071] In applications, electronic devices may include, but are not limited to, processors and memories. Those skilled in the art will appreciate that Figure 4 The electronic device is merely an example and does not limit the electronic device. The electronic device may include more or fewer components than shown in the figure, or may include a combination of certain components or different components.

[0072] In applications, a processor may be a central processing unit (CPU), other general-purpose processors, a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA), other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or any conventional processor.

[0073] In applications, in some embodiments, memory can be an internal storage unit of an electronic device, such as a hard drive or memory. In other embodiments, memory can also be an external storage device of the electronic device, such as a plug-in hard drive, a Smart Media Card (SMC), a Secure Digital (SD) card, or a flash memory card. Furthermore, memory can include both internal storage units and external storage devices. Memory is used to store operating systems, application programs, boot loaders, data, and other programs, such as computer program code. Memory can also be used to temporarily store data that has been output or is about to be output.

[0074] It should be noted that the information interaction, execution process, etc. between the above-mentioned devices / units are based on the same concept as the method embodiment of this application. Their specific functions and technical effects can be found in the method embodiment section and will not be repeated here.

[0075] Those skilled in the art can clearly understand that, for the convenience and brevity of description, only the division of the above-mentioned functional units and modules is used as an example for illustration. In actual applications, the above-mentioned functions can be distributed and completed by different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above. The functional units and modules in the embodiment can be integrated into one processing unit, or each unit can exist physically alone, or two or more units can be integrated into one unit. The above-mentioned integrated unit can be implemented in the form of hardware or in the form of software functional units. In addition, the specific names of the functional units and modules are only for the convenience of distinguishing each other, and are not used to limit the scope of protection of this application. The specific working process of the units and modules in the above-mentioned system can refer to the corresponding process in the aforementioned method embodiment, and will not be repeated here.

[0076] An embodiment of the present application further provides a computer-readable storage medium, which stores a computer program. When the computer program is executed by a processor, the steps in the above-mentioned method embodiments can be implemented.

[0077] An embodiment of the present application provides a computer program product, including a computer program. When the computer program product runs on an electronic device, the electronic device can implement the steps in the above-mentioned various method embodiments when executing the computer program product.

[0078] If the integrated unit is implemented as a software functional unit and sold or used as a standalone product, it can be stored in a computer-readable storage medium. Based on this understanding, the present application can implement all or part of the process steps in the above-mentioned method embodiments by using a computer program to instruct the relevant hardware. The computer program can be stored in a computer-readable storage medium. When executed by a processor, the computer program can implement the steps of each of the above-mentioned method embodiments. The computer program includes computer program code, which can be in source code form, object code form, executable file, or some intermediate form. Computer-readable media can include at least: any entity or device capable of carrying computer program code to a device / electronic device, recording media, computer memory, read-only memory (ROM), random access memory (RAM), electric carrier signals, telecommunication signals, and software distribution media. Examples include USB flash drives, removable hard drives, magnetic disks, or optical disks. In some jurisdictions, based on legislation and patent practice, computer-readable media cannot be electric carrier signals or telecommunication signals.

[0079] In the above embodiments, the description of each embodiment has its own focus. For parts that are not described or recorded in detail in a certain embodiment, reference can be made to the relevant description of other embodiments.

[0080] Those skilled in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0081] In the embodiments provided in this application, it should be understood that the disclosed devices and methods can be implemented in other ways. For example, the device embodiments described above are merely schematic. For example, the division of modules or units is only a logical function division. In actual implementation, there may be other division methods, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0082] Units described as separate components may or may not be physically separate, and components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of these units may be selected to achieve the purpose of this embodiment according to actual needs.

[0083] The above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. These modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the various embodiments of the present application, and should all be included in the scope of protection of the present application.

Claims

1. A vehicle risk avoidance control method based on multimodal vital signs detection, characterized in that: include: Collect the driver's respiratory rate, heart rate, body temperature and brain wave data, and calculate the abnormal vital signs index based on the collected data; If the abnormal vital sign index is greater than or equal to the preset index, and the vehicle speed is less than or equal to the preset speed, a first-level response is executed; If the abnormal vital sign index is greater than or equal to a preset threshold, and the vehicle speed is greater than a preset speed, executing a secondary response; Among them, the first-level response includes outputting a warning signal, and the second-level response includes controlling the vehicle to stop safely.

2. The vehicle risk avoidance control method based on multimodal vital sign detection according to claim 1, characterized in that: The abnormal physical sign index is calculated by the following formula: ； Among them, HR represents the collected heart rate, HR 基线 Indicates the heart rate baseline value, σ HR represents the standard deviation of the heart rate history data, RR represents the respiratory rate obtained, and RR 基线 Represents the baseline value of respiratory rate, σ RR Represents the standard deviation of respiratory rate historical data, Δ T Indicates the temperature change collected per unit time. σ T represents the standard deviation of body temperature variation, E α Indicates the collected α wave energy value, E β Indicates the collected β wave energy value, w 1. w 2. w 3. w 4 is the weight coefficient.

3. The vehicle risk avoidance control method based on multimodal vital sign detection according to claim 2, characterized in that: The heart rate baseline value is calculated based on the driver's resting heart rate data collected over multiple consecutive days.

4. The vehicle risk avoidance control method based on multimodal vital sign detection according to claim 1, characterized in that: The secondary response includes: Based on the environmental data obtained by the environmental perception unit and combined with high-precision map data, safe parking path planning is carried out to generate a planned path; Control the vehicle to travel along the planned path and stop at the designated stop location.

5. The vehicle risk avoidance control method based on multimodal vital sign detection according to claim 4, characterized in that: When the vehicle is controlled to travel along the planned path, a collision time is also calculated. If the collision time is less than a preset time threshold, the path is replanned.

6. The vehicle risk avoidance control method based on multimodal vital sign detection according to claim 4, characterized in that: The controlling the vehicle to travel along the planned path and to stop at a designated stop position includes: The vehicle's deceleration is controlled in sections through the wire control brake system, and the wheel angle is adjusted based on the four-wheel independent steering system, so that the vehicle can stop at the designated parking position along the planned path.

7. The vehicle risk avoidance control method based on multimodal vital sign detection according to claim 4, characterized in that: The safe parking path planning is performed and when generating the planned path, the path curvature radius is greater than or equal to the minimum turning radius that the four-wheel steering system can execute, the right emergency lane or shoulder is preferentially selected as the designated parking position, and the width of the designated parking position is greater than or equal to the preset width.

8. The vehicle risk avoidance control method based on multimodal vital sign detection according to claim 4, characterized in that: The controlling the vehicle to travel along the planned path and to stop at a designated stop position further includes: The main controller is used to control the vehicle to travel along the planned path and stop at the designated stop position; When the main controller fails, the sub-controller is switched to control the vehicle to travel according to the planned path and stop at the designated stop position.

9. A vehicle risk avoidance control device based on multimodal vital signs detection, characterized in that: include: The acquisition module is used to collect the driver's respiratory rate, heart rate, body temperature and brain wave data, and calculate the abnormal vital sign index based on the collected data; a control module configured to execute a primary response if the abnormal vital sign index is greater than or equal to a preset index and the vehicle speed is less than or equal to a preset speed; and execute a secondary response if the abnormal vital sign index is greater than or equal to a preset threshold and the vehicle speed is greater than the preset speed; Among them, the first-level response includes outputting a warning signal, and the second-level response includes controlling the vehicle to stop safely.

10. An electronic device, characterized in that: The electronic device comprises a processor, a memory, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the computer program, the electronic device implements the method according to any one of claims 1 to 8.

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