Intelligent driving mode determination method, electronic device, and medium

Abstract

The present application relates to the field of vehicle intelligent driving control, and in particular, to an intelligent driving mode determination method, an electronic device and a medium.The method comprises: in response to a vehicle request, receiving current-time driver health data and driver health data reference values sent by a wearable device; determining a driver emotional state according to the health data and the data reference values; and determining an intelligent driving mode according to the driver emotional state and a facial image.The method further comprises: determining current-time driver health data according to sensor data of the driver at the current time; determining driver health data reference values according to sensor data of the driver within a set period; and in response to a vehicle request, sending the health data and the data reference values to the vehicle end.The present application associates driver health data, driver emotional state and intelligent driving mode, and facilitates the vehicle to timely and accurately adjust the intelligent driving mode according to the driver emotional state, thereby ensuring driving safety.

Description

Technical Field

[0001] This invention relates to the field of intelligent driving control for vehicles, and more specifically, to a method for determining intelligent driving modes, an electronic device, and a medium. Background Technology

[0002] Current autonomous driving systems below Level 3 primarily interact with drivers through the vehicle's accelerator pedal, brake pedal, multi-function steering wheel, instrument display, head-up display, other switches and sensors. Basic driver recognition functions such as driver off-seat sensing, steering wheel hands-off sensing, facial recognition for driver fatigue detection, driver distraction monitoring, and accelerator pedal misoperation detection are limited to collecting the driver's actions and visual state. The information collected by these sensors is generally inaccurate, often leaving significant functional redundancy to ensure driving comfort, which may lead to the failure of intelligent driving modes and a reduced driving experience. Furthermore, these functions mainly determine intelligent driving conditions and lack proactive interaction to improve driving comfort and overall intelligence.

[0003] The above-mentioned intelligent driving interaction methods only involve the car collecting the driver's driving intentions and executing corresponding actions based on those intentions. Intelligent driving functions only include the driver's active activation of functions and mode adjustments; the car cannot accurately determine and adjust its intelligent driving plan to address driving safety issues that might arise from the driver's emotional state.

[0004] In view of this, the present invention is hereby proposed. Summary of the Invention

[0005] The purpose of this invention is to provide a method, electronic device, and medium for determining intelligent driving modes, in order to solve the problem that existing technologies cannot adjust intelligent driving schemes based on driver emotions.

[0006] To achieve the above objectives, the present invention adopts the following technical solution:

[0007] In a first aspect, the present invention provides a method for determining an intelligent driving mode, which is applied to a vehicle and includes:

[0008] In response to a vehicle request, the system receives the current driver health data and driver health data reference values ​​sent by the wearable device.

[0009] The driver's emotional state is determined based on the current driver's health data and the driver's health data reference value;

[0010] The intelligent driving mode is determined based on the driver's emotional state and facial image.

[0011] As a further preferred technical solution, after receiving the current driver health data and driver health data reference value sent by the wearable device in response to the vehicle request, the solution further includes:

[0012] Determine whether the current driver health data and the driver health data reference value are complete;

[0013] If so, then perform the operation of determining the driver's emotional state based on the current driver health data and the driver health data reference value;

[0014] If not, data download requests are sent to the wearable device in a loop until complete current driver health data and driver health data reference values ​​are received within the request count threshold, or the number of data download requests reaches the request count threshold. If the current driver health data and driver health data reference values ​​are still missing when the number of data download requests reaches the request count threshold, the default health data value of the missing data is called and a data missing prompt is sent.

[0015] As a further preferred technical solution, determining the driver's emotional state based on the current driver's health data and the driver's health data reference value includes:

[0016] Based on the driver health data reference value, the default health data value, and the driver health data default weight coefficient, the driver health data weight coefficient is determined; the driver health data weight coefficient is used to characterize the correlation between the driver health data reference value and the driver's emotional state.

[0017] The driver's emotional state is determined based on the driver's health data at the current moment and the driver's health data weighting coefficient.

[0018] As a further preferred technical solution, the step of determining the driver health data weight coefficient based on the driver health data reference value, the default health data value, and the default weight coefficient of the driver health data includes:

[0019] Based on the driver health data reference value and the default health data value, determine the driver health data weight coefficient calibration ratio;

[0020] The driver health data weight coefficient is determined based on the driver health data weight coefficient calibration ratio and the driver health data default weight coefficient.

[0021] As a further preferred technical solution, determining the intelligent driving mode based on the driver's emotional state and facial image includes:

[0022] If the driver's emotional state is calm, the system determines whether the driver is fatigued based on the driver's facial image. If so, the system is set to Fatigue Driving Mode; otherwise, the system is set to Calm Driving Mode.

[0023] If the driver's emotional state is not calm, the intelligent driving mode is determined to be the intelligent driving mode corresponding to the emotional state.

[0024] Secondly, the present invention provides a method for determining an intelligent driving mode, applied to a wearable device, comprising:

[0025] Determine the driver's health data at the current moment based on the sensor data of the driver at the current moment;

[0026] Based on the sensor data of the driver within a set period, determine the driver's health data reference value;

[0027] In response to a vehicle request, the driver's current health data and the driver's health data reference value are sent to the vehicle, so that the vehicle can determine the intelligent driving mode based on the driver's current health data and the driver's health data reference value.

[0028] As a further preferred technical solution, after responding to a vehicle request and sending the current driver health data and the driver health data reference value to the vehicle, the solution further includes:

[0029] In response to a data download request sent by the vehicle, the driver's current health data and the driver's health data reference value are sent to the vehicle.

[0030] As a further preferred technical solution, after responding to the data download request sent by the vehicle terminal and sending the current driver health data and the driver health data reference value to the vehicle terminal, the solution further includes:

[0031] The device receives a data loss warning from the vehicle and controls the wearable device to perform a self-test.

[0032] Thirdly, the present invention provides an electronic device, comprising:

[0033] At least one processor, and a memory communicatively connected to at least one of the processors;

[0034] The memory stores instructions that can be executed by at least one of the processors, which are executed by at least one of the processors to enable at least one of the processors to perform the method described above.

[0035] Fourthly, the present invention provides a computer-readable storage medium storing computer instructions for causing a computer to perform the above-described method.

[0036] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0037] Another intelligent driving mode determination method provided by this invention is applied to the vehicle. After receiving the driver's current health data and driver health data reference value sent by the wearable device, the driver's emotional state is determined based on the current driver health data and driver health data reference value. Then, the intelligent driving mode is determined based on the driver's emotional state and the driver's facial image. This method, applied to the vehicle, correlates the current driver health data, driver health data reference value, driver emotional state, and intelligent driving mode, facilitating timely and accurate adjustment of the intelligent driving mode based on the driver's emotional state to ensure driving safety. Furthermore, by combining the driver's facial image to determine the intelligent driving mode, the resulting intelligent driving mode is more accurate.

[0038] This invention provides a method for determining an intelligent driving mode applied to wearable devices. The method includes determining the driver's current health data based on sensor data, determining a driver health data reference value based on sensor data collected within a set period, and then, in response to a vehicle request, sending the current driver health data and the driver health data reference value to the vehicle. This allows the vehicle to determine the intelligent driving mode based on the current driver health data and the driver health data reference value. This method, applied to wearable devices, allows for more accurate and reliable data collected by sensors, going beyond mere surface-level data. Furthermore, the current driver health data reflects the driver's emotions at a deeper and earlier level, thus avoiding superficial emotional states. The resulting current driver health data and driver health data reference value facilitate accurate intelligent driving mode determination by the vehicle. Additionally, the method uses sensor data collected within a set period, helping the vehicle avoid the influence of individual and environmental factors on driving mode determination. Attached Figure Description

[0039] To more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the specific embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0040] Figure 1 This is a flowchart illustrating the intelligent driving mode determination method provided in Example 1;

[0041] Figure 2 This is a flowchart illustrating the intelligent driving mode determination method provided in Example 2;

[0042] Figure 3 This is a schematic diagram of the intelligent driving mode determination device provided in Embodiment 3;

[0043] Figure 4 This is a schematic diagram of the intelligent driving mode determination device provided in Embodiment 4;

[0044] Figure 5 This is a schematic diagram of the electronic device provided in Example 5. Detailed Implementation

[0045] The following description, in conjunction with the accompanying drawings, illustrates exemplary embodiments of this application, including various details to aid understanding. These should be considered merely exemplary. Therefore, those skilled in the art will recognize that various changes and modifications can be made to the embodiments described herein without departing from the scope and spirit of this application. Similarly, for clarity and brevity, descriptions of well-known functions and structures are omitted in the following description.

[0046] As mentioned in the background section, existing intelligent driving interaction methods only rely on the car collecting the driver's driving intentions and executing corresponding actions based on those intentions. However, the car cannot accurately determine and adjust its intelligent driving scheme to address driving safety issues that might arise from the driver's emotional state. To address this, this invention uses driver health data and reference values ​​sent from a wearable device to determine the driver's emotional state, and then combines this with facial images to determine the intelligent driving mode. The invention will be further described in detail below with reference to embodiments.

[0047] Example 1

[0048] Figure 1 This is a flowchart of a method for determining an intelligent driving mode provided in this embodiment. The method in this embodiment is applicable to the vehicle end, which can be a vehicle infotainment system or an ECU, etc. For ease of understanding, this embodiment uses the vehicle infotainment system as the executing entity of the method.

[0049] like Figure 1 As shown, this embodiment provides a method for determining an intelligent driving mode, including the following steps:

[0050] S110, In response to a vehicle request, receive the current driver health data and driver health data reference value sent by the wearable device.

[0051] The intelligent driving mode determination method in this embodiment receives the current driver health data and driver health data reference values ​​sent by the wearable device after responding to a vehicle request. Vehicle requests can be sent automatically after the vehicle starts, or sent by the user after issuing an interaction command.

[0052] The driver's health data at the current moment includes skin perspiration, body temperature, skin conductance index, respiratory rate, heart rate, and blood oxygen concentration. The driver's health data reference values ​​include average skin perspiration, average body temperature, average skin conductance index, average respiratory rate, average heart rate, and average blood oxygen concentration.

[0053] S120. Determine the driver's emotional state based on the current driver's health data and the driver's health data reference value.

[0054] Optionally, determining the driver's emotional state based on the current driver's health data and the driver's health data reference value includes:

[0055] Based on the driver health data reference value, the default health data value, and the driver health data default weight coefficient, the driver health data weight coefficient is determined; the driver health data weight coefficient is used to characterize the correlation between the driver health data reference value and the driver's emotional state.

[0056] The driver's emotional state is determined based on the driver's health data at the current moment and the driver's health data weighting coefficient.

[0057] The default health data values ​​are the default values ​​of health data pre-stored locally on the vehicle. The default weighting coefficient for driver health data can be set and calibrated according to actual needs. It is understandable that the default health data values ​​and the default weighting coefficients for driver health data correspond.

[0058] Optionally, determining the driver's emotional state based on the current driver's health data and the driver's health data weighting coefficient includes: inputting the current driver's health data and the driver's health data weighting coefficient into the driver's emotional state scoring formula, and outputting the driver's emotional state score; and determining the driver's emotional state based on the scoring interval to which the driver's emotional state score belongs.

[0059] Optionally, the driver's emotional state scoring formula is: ES = a*S + b*T + c*E + d*R + e*P + f*B. In this formula, ES is the driver's emotional state score, a is the weighting coefficient of skin perspiration in the driver's health data, b is the weighting coefficient of skin temperature in the driver's health data, c is the weighting coefficient of skin conductance index in the driver's health data, d is the weighting coefficient of respiratory rate in the driver's health data, e is the weighting coefficient of heart rate in the driver's health data, f is the weighting coefficient of blood oxygen concentration in the driver's health data, S is skin perspiration, T is skin temperature, E is skin conductance index, R is respiratory rate, P is heart rate, and B is blood oxygen concentration. The units of the driver's health data weighting coefficients are multiplied by the units of the corresponding driver's health data at the current moment, and the result is 1. For example, when the driver's health data is skin temperature, the unit of skin temperature is °C, and the unit of the skin temperature weighting coefficient is °C. -1 The driver emotional state scoring formula integrates driver health data and represents it as a specific score, organically transitioning from health data to emotional scores.

[0060] The scores calculated using the above formula range from 0 to 100, which can be divided into 7 score ranges: 0-20 (fear), 21-30 (nausea), 31-40 (sadness), 41-60 (calm), 61-80 (happiness), 81-90 (surprise), and 91-100 (anger). Classifying emotions into 7 basic emotion types and grading the scores makes the emotion assessment data more concrete.

[0061] Because individual physiological differences among drivers and variations in climate can lead to discrepancies in sensor data, driver emotional state scores may be skewed. Therefore, this embodiment determines a driver health data weighting coefficient based on a driver health data reference value, a default health data value, and a default weighting coefficient for driver health data. This eliminates score deviations caused by individual physiological differences and climate conditions. The default health data value and the default weighting coefficient can be used as standards. The driver emotional state score calculated using these default health data values ​​and the default weighting coefficient is a standard score and does not exhibit score deviation. Therefore, if the driver health data reference value and the default health data value are inconsistent, it is considered that a sensor data discrepancy has occurred, and the driver health data weighting coefficient needs to be adjusted. Conversely, if they are consistent, the driver health data weighting coefficient is the default weighting coefficient and does not require further adjustment. This method comprehensively considers the driver health data reference value, the default health data value, and the default weighting coefficient, resulting in a more accurate and reliable driver health data weighting coefficient.

[0062] Optionally, determining the driver health data weighting coefficient based on the driver health data reference value, the default health data value, and the driver health data default weighting coefficient includes:

[0063] Based on the driver health data reference value and the default health data value, determine the driver health data weight coefficient calibration ratio;

[0064] The driver health data weighting coefficient is determined based on the weighting coefficient calibration ratio and the default weighting coefficient of the driver health data.

[0065] The aforementioned driver health data weighting coefficient calibration ratio can be calculated using the following formula: X = Driver health data reference value / Default health data value. The aforementioned driver health data weighting coefficient can be calculated using the following formula: Driver health data default weighting coefficient / X. As mentioned earlier, due to individual physiological differences among drivers and variations in climate and environment leading to differences in sensor data, the driver's emotional state score may be biased. This implementation method calibrates the health data weighting coefficient by introducing a driver health data weighting coefficient calibration ratio to avoid score bias. If the health data weighting coefficient is not calibrated, the obtained score will deviate from the actual situation, thus affecting the driver's emotional state recognition result.

[0066] Taking driver health data as average body temperature as an example, assuming the average body temperature reference value is 36.8℃ and the default average body temperature is 36.5℃, then the average body temperature weighting coefficient calibration ratio X = 36.8 / 36.5 = 1.01. If the default weighting coefficient for this average body temperature is 0.2℃... -1 Therefore, the average body temperature weighting coefficient is 0.2℃. -1 / 1.01=0.198℃ -1 .

[0067] S130. Determine the intelligent driving mode based on the driver's emotional state and facial image.

[0068] The driver's facial image can be captured using an in-vehicle camera module.

[0069] Optionally, determining the intelligent driving mode based on the driver's emotional state and facial image includes:

[0070] If the driver's emotional state is calm, the system determines whether the driver is fatigued based on the driver's facial image. If so, the system is set to Fatigue Driving Mode; otherwise, the system is set to Calm Driving Mode.

[0071] If the driver's emotional state is not calm, the intelligent driving mode is determined to be the intelligent driving mode corresponding to the emotional state.

[0072] This optional implementation further determines whether the driver is fatigued by combining facial images when the driver's emotional state is calm. If so, the driver is identified as fatigued, and the intelligent driving mode is determined to be a fatigue-based intelligent driving mode. If not, the driver is identified as not fatigued, and the intelligent driving mode is determined to be a calm emotional intelligent driving mode. When the driver's emotional state is not calm, the intelligent driving mode is determined to be the corresponding emotional intelligent driving mode. For example, if the driver is happy, the intelligent driving mode is determined to be a happy emotional intelligent driving mode. Different intelligent driving modes can be calibrated, and performance and parameters such as accelerator pedal misoperation correction, emergency steering assist, torque limiting, speed limiting, following distance, automatic braking, lane keeping assist, and emergency steering assist can be calibrated.

[0073] The aforementioned intelligent driving mode determination method is applied to the vehicle. After receiving the driver's current health data and reference values ​​from the wearable device, the driver's emotional state is determined based on these data. Then, the intelligent driving mode is determined based on the driver's emotional state and facial image. This method, applied to the vehicle, correlates the current driver's health data, reference values, emotional state, and intelligent driving mode, allowing the vehicle to adjust the intelligent driving mode promptly and accurately based on the driver's emotional state, ensuring driving safety. Furthermore, by combining the driver's facial image with the intelligent driving mode determination, the resulting intelligent driving mode is more accurate.

[0074] Furthermore, after receiving the current driver health data and driver health data reference value sent by the wearable device in response to the vehicle request, the method further includes:

[0075] Determine whether the current driver health data and the driver health data reference value are complete;

[0076] If so, then perform the operation of determining the driver's emotional state based on the current driver health data and the driver health data reference value;

[0077] If not, data download requests are sent to the wearable device in a loop until complete current driver health data and driver health data reference values ​​are received within the request count threshold, or the number of data download requests reaches the request count threshold. If the current driver health data and driver health data reference values ​​are still missing when the number of data download requests reaches the request count threshold, the default health data value of the missing data is called and a data missing prompt is sent.

[0078] This embodiment performs further judgment after the driver receives the current driver health data and driver health data reference value sent by the wearable device. If the data and reference value are complete, the subsequent operation of determining the driver's emotional state continues. If incomplete, data download requests need to be sent to the wearable device repeatedly. When complete data and reference value are obtained within the request count threshold (e.g., within 3 times), or when the number of download requests reaches the request count threshold (e.g., 3 times), the download request stops. If complete data and reference value have been obtained when the download request stops, the subsequent operation of determining the driver's emotional state continues. If incomplete data and reference value are not obtained, the default health data value for the missing data is called, and a data missing prompt is sent. This missing prompt can be sent to the display module on the vehicle's infotainment system (e.g., the central control screen) for the driver to view and check the device, or sent to the wearable device to remind the driver to check the device.

[0079] Example 2

[0080] Figure 2 This is a flowchart of a method for determining an intelligent driving mode provided in this embodiment. The method of this embodiment is applicable to wearable devices. Wearable devices refer to portable devices that are worn directly on the body or integrated into the user's clothing or accessories. Wearable devices are not only hardware devices, but also achieve powerful functions through software support, data interaction, and cloud interaction. Wearable devices include smartwatches, smart headband lights, and smart glasses lights. For ease of understanding, this embodiment uses a smartwatch as the subject of the method.

[0081] like Figure 2 As shown, this embodiment provides a method for determining an intelligent driving mode, including the following steps:

[0082] S210. Determine the driver's health data at the current moment based on the sensor data of the driver at the current moment.

[0083] The sensor data includes skin conductivity, skin temperature, bioelectrical impedance, respiratory rate collected by the bioelectrical impedance sensor, respiratory rate and heart rate collected by the PPG sensor, heart rate collected by the ECG sensor, and blood oxygen concentration collected by the SpO2 sensor. The driver's health data at the current moment includes skin perspiration, body temperature, skin conductivity index, respiratory rate, heart rate, and blood oxygen concentration.

[0084] Among them, skin conductance can be acquired using a skin conductance sensor, skin temperature can be acquired using a skin temperature sensor, and bioelectrical impedance can be acquired using a bioelectrical impedance sensor. The PPG sensor is a sensor that uses photoplethysmography (PPG) to acquire heart rate and respiratory rate. The ECG sensor is an electrocardiogram sensor, mainly containing data on the P wave, PR interval, QRS complex, ST segment, T wave, and QT interval from the electrocardiogram; the smartwatch can store typical medical heartbeat pulse data, including multiple normal heartbeat pulses and pathological arrhythmia pulse data, as a basis for pathological judgment and comparison. The SpO2 sensor is a blood oxygen saturation sensor.

[0085] Optionally, the amount of perspiration is calculated using the following formula: S = a1·G, where S is the amount of perspiration in mL; a1 is a coefficient greater than 0; and G is the skin conductivity.

[0086] Body temperature is the same as skin temperature.

[0087] The skin conductance response index is calculated using the following formula: E=b1·Z, where E is the skin conductance response index, b1 is a coefficient greater than 0, and Z is the bioelectrical impedance.

[0088] Respiratory rate is calculated using the following formula:

[0089] ;

[0090] In the formula, R is the respiratory rate (breaths / min), f(p) is the respiratory rate (breaths / min) collected by the PPG sensor, and f(z) is the respiratory rate (breaths / min) collected by the bioelectrical impedance sensor.

[0091] Heart rate is calculated using the following formula:

[0092] ;

[0093] In the formula, P is the heart rate (beats / min), F(P) is the heart rate (beats / min) collected by the PPG sensor, and F(E) is the heart rate (beats / min) collected by the ECG sensor.

[0094] Blood oxygen concentration is the blood oxygen concentration collected by the SpO2 sensor.

[0095] S220. Determine the driver's health data reference value based on the sensor data of the driver within a set period.

[0096] The driver health data reference values ​​include average skin perspiration, average body temperature, average skin conductance index, average respiratory rate, average heart rate, and average blood oxygen concentration.

[0097] Optionally, determining the driver's health data reference value based on the driver's sensor data within a set period includes:

[0098] The average amount of perspiration produced by the skin is determined based on skin conductivity.

[0099] Average body temperature is determined based on skin temperature;

[0100] The mean skin conductance response index is determined based on bioelectrical impedance.

[0101] The average respiratory rate was determined based on the respiratory rate collected by the bioelectrical impedance sensor and the respiratory rate collected by the PPG sensor.

[0102] The average heart rate is determined based on the heart rate collected by the PPG sensor and the heart rate collected by the ECG sensor.

[0103] The average blood oxygen concentration is determined based on the blood oxygen concentration collected by the SpO2 sensor.

[0104] The average amount of perspiration produced by the skin is calculated using the following formula: In the formula, i represents the number of samplings, G(i) represents the skin conductance collected in the i-th sampling, and m is a coefficient used to correct the skin conductance to the amount of perspiration. The value of m is derived from medical statistical experiments, and is greater than 0. Its unit is multiplied by the unit of skin conductance and then multiplied by the unit of perspiration.

[0105] Average body temperature is calculated using the following formula: In the formula, i is the number of samplings, and T(i) is the skin temperature of the i-th sampling.

[0106] The average skin conductance index is calculated using the following formula: In the formula, i represents the number of samplings, Z(i) represents the bioelectrical impedance of the i-th sampling, and n is a coefficient used to correct the bioelectrical impedance to the skin conductance response index. The value of n is derived from medical statistical experiments, and is greater than 0. Its unit, after being multiplied by the unit of bioelectrical impedance, is the same as the unit of the skin conductance response index.

[0107] The average respiratory rate is calculated using the following formula: In the formula, i represents the number of samplings, and R(i) represents the respiratory rate calculated from the i-th sampling. R(i) is calculated using the formula in S110.

[0108] Average heart rate is calculated using the following formula: In the formula, i represents the number of samplings, and P(i) represents the heart rate calculated from the i-th sampling. P(i) is calculated using the formula in S110.

[0109] Mean blood oxygen concentration is calculated using the following formula: In the formula, i represents the number of samplings, and B(i) represents the blood oxygen concentration of the i-th sampling.

[0110] Optionally, the above-mentioned cycle is set to 7 days, and the sampling interval within the set cycle can be 30 minutes. The driver's health data reference values ​​are refreshed periodically, and data that cannot be refreshed uses the stored values ​​from the smartwatch in the previous cycle.

[0111] It should be noted that the order of S110 and S120 in this embodiment is only an example. In fact, S120 can be executed at the same time as or before S110. The order of the two has no effect on the effect of this embodiment.

[0112] S230. In response to a vehicle request, the driver's health data at the current moment and the driver's health data reference value are sent to the vehicle terminal, so that the vehicle terminal can determine the intelligent driving mode based on the driver's health data at the current moment and the driver's health data reference value.

[0113] Upon receiving a vehicle request, this step automatically sends the current driver's health data and driver health data reference values ​​to the vehicle.

[0114] The aforementioned intelligent driving mode determination method is applied to wearable devices. It includes determining the driver's current health data based on sensor data, determining a driver health data reference value based on sensor data collected within a set period, and then, in response to a vehicle request, sending the current driver health data and the driver health data reference value to the vehicle. This allows the vehicle to determine the intelligent driving mode based on the current driver health data and the driver health data reference value. This method, applied to wearable devices, ensures more accurate and reliable data collected by sensors, going beyond mere surface-level data. Furthermore, the current driver health data reflects the driver's emotions at a deeper and earlier level, thus avoiding superficial emotions. The resulting current driver health data and driver health data reference value facilitate accurate intelligent driving mode determination by the vehicle. Additionally, the sensor data used in this method is collected within a set period, helping the vehicle avoid the influence of individual and environmental factors on driving mode determination.

[0115] Furthermore, after responding to a vehicle request and sending the current driver health data and the driver health data reference value to the vehicle, the method further includes:

[0116] In response to a data download request sent by the vehicle, the driver's current health data and the driver's health data reference value are sent to the vehicle.

[0117] In certain situations (such as equipment failure or network failure), wearable devices may be unable to send complete data. In this case, the vehicle will send a data download request. Upon receiving the request, the vehicle will resend the current driver health data and driver health data reference values ​​to the vehicle so that the vehicle can obtain the required data in a timely manner.

[0118] Furthermore, after responding to the data download request sent by the vehicle terminal and sending the current driver health data and the driver health data reference value to the vehicle terminal, the method further includes:

[0119] The device receives a data loss warning from the vehicle and controls the wearable device to perform a self-test.

[0120] When a data loss notification is received from the vehicle, it indicates that the wearable device may be malfunctioning. Therefore, a device self-check is required to better identify and resolve the problem.

[0121] Example 3

[0122] like Figure 3 As shown, this embodiment provides an intelligent driving mode determination device, including:

[0123] Data receiving module 101 is used to receive the current driver health data and driver health data reference value sent by the wearable device in response to a vehicle request;

[0124] The driver emotional state determination module 102 is used to determine the driver's emotional state based on the driver's health data at the current moment and the driver's health data reference value;

[0125] The intelligent driving mode determination module 103 is used to determine the intelligent driving mode based on the driver's emotional state and the driver's facial image.

[0126] The device is used to perform the method described in Embodiment 1, and therefore has at least the functional modules and beneficial effects corresponding to the above method.

[0127] Example 4

[0128] like Figure 4 As shown, this embodiment provides an intelligent driving mode determination device, including:

[0129] The current driver health data determination module 201 is used to determine the current driver health data based on the sensor data of the driver at the current moment.

[0130] The driver health data reference value determination module 202 is used to determine the driver health data reference value based on the sensor data of the driver within a set period.

[0131] The data transmission module 203 is used to send the current driver health data and the driver health data reference value to the vehicle terminal in response to a vehicle request, so that the vehicle terminal can determine the intelligent driving mode based on the current driver health data and the driver health data reference value.

[0132] The device is used to perform the method described in Embodiment 2, and therefore has at least the functional modules and beneficial effects corresponding to the above method.

[0133] Example 5

[0134] like Figure 5 As shown, this embodiment provides an electronic device, including:

[0135] At least one processor; and

[0136] A memory communicatively connected to at least one of the processors; wherein,

[0137] The memory stores instructions executable by at least one of the processors to enable the processor to perform the described method. Since at least one processor in the electronic device is capable of performing the described method, it thus possesses at least the same advantages as the described method.

[0138] Optionally, the electronic device also includes interfaces for connecting the various components, including high-speed interfaces and low-speed interfaces. The components are interconnected using different buses and can be mounted on a common motherboard or otherwise installed as needed. The processor can process instructions executed within the electronic device, including instructions stored in or on memory to display graphical information of a GUI (Graphical User Interface) on an external input / output device (such as a display device coupled to the interface). In other embodiments, multiple processors can be used with multiple memories, and / or multiple buses can be used with multiple memories, if desired. Similarly, multiple electronic devices (e.g., as a server array, a group of blade servers, or a multiprocessor system) can be connected, each providing some of the necessary operations. Figure 5 Take processor 301 as an example.

[0139] The memory 302, as a computer-readable storage medium, can be used to store software programs, computer-executable programs, and modules, such as the program instructions / modules corresponding to the intelligent driving mode determination method in this embodiment of the invention. The processor 301 executes various functional applications and data processing of the device by running the software programs, instructions, and modules stored in the memory 302, thereby implementing the aforementioned intelligent driving mode determination method.

[0140] The memory 302 may primarily include a program storage area and a data storage area. The program storage area may store the operating system and applications required for at least one function; the data storage area may store data created based on terminal usage. Furthermore, the memory 302 may include high-speed random access memory and non-volatile memory, such as at least one disk storage device, flash memory device, or other non-volatile solid-state storage device. In some instances, the memory 302 may further include memory remotely located relative to the processor 301, which can be connected to the device via a network. Examples of such networks include, but are not limited to, the Internet, intranets, local area networks, mobile communication networks, and combinations thereof.

[0141] The electronic device may further include an input device 303 and an output device 304. The processor 301, memory 302, input device 303, and output device 304 can be connected via a bus or other means. Figure 5 Taking the example of a connection between China and Israel via a bus.

[0142] Input device 303 can receive input digital or character information, and output device 304 may include a display device, an auxiliary lighting device (e.g., an LED), and a haptic feedback device (e.g., a vibration motor). The display device may include, but is not limited to, a liquid crystal display (LCD), a light-emitting diode (LED) display, and a plasma display. In some embodiments, the display device may be a touchscreen.

[0143] Example 6

[0144] This embodiment provides a computer-readable storage medium storing computer instructions for causing a computer to perform the methods described above. The computer instructions on this computer-readable storage medium, used to cause a computer to perform the methods described above, thus have at least the same advantages as the methods described above.

[0145] The medium in this invention can be any combination of one or more computer-readable media. The medium can be a computer-readable signal medium or a computer-readable storage medium. The medium can be, for example,—but not limited to—an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of the medium (a non-exhaustive list) include: an electrical connection having one or more wires, a portable computer disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage device, magnetic storage device, or any suitable combination thereof. In this document, the medium can be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, apparatus, or device.

[0146] Computer-readable signal media may include data signals propagated in baseband or as part of a carrier wave, carrying computer-readable program code. Such propagated data signals may take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. Computer-readable signal media may also be any computer-readable medium other than computer-readable storage media, capable of sending, propagating, or transmitting programs for use by or in connection with an instruction execution system, apparatus, or device.

[0147] Program code contained on a computer-readable medium may be transmitted using any suitable medium, including but not limited to wireless, wire, optical fiber, RF (Radio Frequency), or any suitable combination thereof.

[0148] Computer program code for performing the operations of this invention can be written in one or more programming languages ​​or a combination thereof. Programming languages ​​include object-oriented programming languages—such as Java, Smalltalk, and C++—as well as conventional procedural programming languages—such as the "C" language or similar programming languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving remote computers, the remote computer can be connected to the user's computer via any type of network—including a local area network (LAN) or a wide area network (WAN)—or can be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0149] It should be understood that the various forms of processes shown above can be used to rearrange, add, or delete steps. For example, the steps described in this application can be executed in parallel, sequentially, or in different orders, as long as the desired result of the technical solution disclosed in this application can be achieved, and this is not limited herein.

[0150] The specific embodiments described above do not constitute a limitation on the scope of protection of this application. Those skilled in the art should understand that various modifications, combinations, sub-combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this application should be included within the scope of protection of this application.

Claims

1. A method for determining an intelligent driving mode, applied to a vehicle, characterized in that, The method comprises: receiving, in response to a vehicle request, current-time driver health data and driver health data reference values sent by a wearable device; determining a driver emotional state according to the current-time driver health data and the driver health data reference values; determining an intelligent driving mode according to the driver emotional state and a driver facial image; the determining of the driver emotional state according to the current-time driver health data and the driver health data reference values comprises: determining a driver health data weight coefficient according to the driver health data reference values, a default health data value and a driver health data default weight coefficient; the driver health data weight coefficient is used to represent the correlation between the driver health data reference values and the driver emotional state; determining the driver emotional state according to the current-time driver health data and the driver health data weight coefficient; the determining of the driver health data weight coefficient according to the driver health data reference values, the default health data value and the driver health data default weight coefficient comprises: determining a driver health data weight coefficient calibration ratio according to the driver health data reference values and the default health data value; determining the driver health data weight coefficient according to the driver health data weight coefficient calibration ratio and the driver health data default weight coefficient.

2. The intelligent driving mode determination method of claim 1, wherein After the receiving of the current-time driver health data and the driver health data reference values sent by the wearable device in response to the vehicle request, the method further comprises: judging whether the current-time driver health data and the driver health data reference values are complete; if yes, performing the operation of determining the driver emotional state according to the current-time driver health data and the driver health data reference values; if no, repeatedly sending a data download request to the wearable device until complete current-time driver health data and driver health data reference values are received within a request number threshold range, or the number of the data download requests reaches the request number threshold, if the current-time driver health data and the driver health data reference values are still incomplete when the number of the data download requests reaches the request number threshold, calling a default health data value of the missing data, and sending a data missing prompt. 3.The intelligent driving mode determination method of claim 1, wherein, the determining of the intelligent driving mode according to the driver emotional state and the driver facial image comprises: in the case that the driver emotional state is a calm state, judging whether the driver is in a fatigue state according to the driver facial image; if yes, determining the intelligent driving mode as a fatigue state intelligent driving mode, and if no, determining the intelligent driving mode as a calm emotional intelligent driving mode; in the case that the driver emotional state is not a calm state, determining the intelligent driving mode as a corresponding emotional intelligent driving mode.

4. An electronic device, comprising: The method comprises: at least one processor, and a memory in communication connection with the at least one processor; The memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to perform the method in any one of claims 1-3.

5. A computer readable storage medium, characterized in that, The medium stores computer instructions for causing a computer to perform the method in any one of claims 1-3.

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