Intelligent driving mode determination method, electronic equipment and medium

Abstract

The invention relates to the field of vehicle intelligent driving control, in particular to an intelligent driving mode determination method, electronic equipment and a medium. The method comprises the following steps: in response to a vehicle request, receiving current driver health data and a driver health data reference value sent by a wearable device; determining an emotion state of the driver according to the health data and the data reference value; and determining an intelligent driving mode according to the driver emotional state and the face image. The method further comprises the following steps: determining health data of the driver at the current moment according to sensor data of the driver at the current moment; determining a driver health data reference value according to the sensor data of the driver in the set period; in response to a vehicle request, the health data and the data reference value are sent to a vehicle end. The driver health data, the driver emotional state and the intelligent driving mode are associated, so that the vehicle can timely and accurately adjust the intelligent driving mode according to the driver emotional state, and the driving safety is ensured.

Description

Technical Field

[0001] The present invention relates to the field of vehicle intelligent driving control, and in particular to an intelligent driving mode determination method, electronic equipment and medium. Background Art

[0002] The existing intelligent driving systems below level L3 in automobiles mainly interact with drivers through the car's own accelerator pedal, brake pedal, multi-function steering wheel, instrument display, HUD, other switch buttons and sensors. For the basic driver operation recognition functions of existing cars at this stage, such as driver leaving seat sensing, steering wheel hands-off sensing, face recognition fatigue driving sensing, driver distraction monitoring, and accelerator misstep recognition, they all remain at the stage of the car's collection of driver actions and apparent states. The accuracy of the information collected by the above sensors is generally high, and usually a large functional redundancy is left to ensure driving comfort, which may cause the intelligent driving mode to fail and reduce the driving experience; and the above functions are mainly used to determine the conditions of intelligent driving, and lack the initiative of interaction to improve the comfort and intelligence of intelligent driving.

[0003] The above intelligent driving interaction methods only stop at the car collecting the driver's driving intention and executing the corresponding actions based on the driving intention expressed by the driver. The intelligent driving function only includes the driver's active activation and mode adjustment of the function. The car cannot accurately determine and adjust the intelligent driving plan for driving safety problems that may be caused by the driver's own emotions.

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

[0005] The purpose of the present invention is to provide a method for determining an intelligent driving mode, an electronic device and a medium to solve the problem in the prior art that the intelligent driving solution cannot be adjusted based on the driver's emotions.

[0006] In order to achieve the above object, the present invention adopts the following technical solutions: In a first aspect, the present invention provides an intelligent driving mode determination method, the intelligent driving mode determination method, applied to a vehicle side, comprising: In response to a vehicle request, receiving current driver health data and a driver health data reference value sent by a wearable device; Determining the driver's emotional state according to the current driver health data and the driver health data reference value; An intelligent driving mode is determined according to the driver's emotional state and the driver's facial image.

[0007] As a further preferred technical solution, after receiving the current driver health data and the driver health data reference value sent by the wearable device in response to the vehicle request, the method further includes: Determining whether the current driver health data and the driver health data reference value are complete; If yes, then performing an operation of determining the driver's emotional state according to the current driver health data and the driver health data reference value; If not, a data download request is sent to the wearable device in a loop until the complete current driver health data and driver health data reference value are received within the request number threshold, or the number of data download requests reaches the request number threshold. If the current driver health data and driver health data reference value are still missing when the number of data download requests reaches the request number threshold, the default health data value of the missing data is called and a data missing prompt is sent.

[0008] As a further preferred technical solution, the determining the driver's emotional state according to the current driver health data and the driver health data reference value includes: Determining a driver health data weight coefficient according to 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 used to characterize the correlation between the driver health data reference value and the driver's emotional state; The driver's emotional state is determined based on the current driver health data and the driver health data weight coefficient.

[0009] As a further preferred technical solution, the determining the driver health data weight coefficient according to the driver health data reference value, the default health data value and the driver health data default weight coefficient includes: Determining a driver health data weight coefficient calibration ratio according to the driver health data reference value and the default health data value; The driver health data weight coefficient is determined according to the driver health data weight coefficient calibration ratio and the driver health data default weight coefficient.

[0010] As a further preferred technical solution, the determining of the intelligent driving mode according to the driver's emotional state and the driver's facial image includes: In the case where the driver's emotional state is a calm state, judging whether the driver is in a fatigue state according to the driver's facial image; if so, determining the intelligent driving mode to be a fatigue state intelligent driving mode; if not, determining the intelligent driving mode to be a calm emotional intelligent driving mode; When the driving emotional state is not a calm state, the intelligent driving mode is determined to be an intelligent driving mode corresponding to the emotion.

[0011] In a second aspect, the present invention provides an intelligent driving mode determination method, which is applied to a wearable device, comprising: Determine the driver's health data at the current moment according to the sensor data of the driver at the current moment; Determine the driver's health data reference value based on the driver's sensor data within a set period; In response to a vehicle request, the current driver health data and the driver health data reference value are sent to the vehicle end, so that the vehicle end determines the intelligent driving mode according to the current driver health data and the driver health data reference value.

[0012] As a further preferred technical solution, after sending the current driver health data and the driver health data reference value to the vehicle end in response to the vehicle request, the method further includes: In response to a data download request sent by the vehicle end, the current driver health data and the driver health data reference value are sent to the vehicle end.

[0013] As a further preferred technical solution, after sending the current driver health data and the driver health data reference value to the vehicle side in response to the data download request sent by the vehicle side, the method further includes: Receive the data missing prompt sent by the vehicle end, and control the wearable device to perform self-check.

[0014] In a third aspect, the present invention provides an electronic device, comprising: at least one processor, and a memory communicatively coupled to at least one of the processors; The memory stores instructions that can be executed by at least one of the processors, and the instructions are executed by at least one of the processors so that at least one of the processors can perform the above method.

[0015] In a fourth aspect, the present invention provides a computer-readable storage medium, on which computer instructions are stored, and the computer instructions are used to enable a computer to execute the above method.

[0016] Compared with the prior art, the present invention has the following beneficial effects: Another intelligent driving mode determination method provided by the present invention is applied to the vehicle side. After receiving the current driver health data and driver health data reference value sent by the wearable device, the driver's emotional state is determined according to the current driver health data and driver health data reference value, and then the intelligent driving mode is determined according to the driver's emotional state and the driver's facial image. This method is applied to the vehicle side, and by associating the current driver health data, the driver health data reference value, the driver's emotional state and the intelligent driving mode, it is convenient for the vehicle to adjust the intelligent driving mode according to the driver's emotional state in a timely and accurate manner to ensure driving safety, and further determine the intelligent driving mode in combination with the driver's facial image, so that the obtained intelligent driving mode is more accurate.

[0017] The present invention provides a method for determining an intelligent driving mode, which is applied to a wearable device, including determining the driver's health data at the current moment according to the driver's sensor data at the current moment, determining the driver's health data reference value according to the driver's sensor data within a set period, and then responding to the vehicle request, sending the driver's health data at the current moment and the driver's health data reference value to the vehicle end, so that the vehicle end determines the intelligent driving mode according to the driver's health data at the current moment and the driver's health data reference value. This method is applied to wearable devices, and the data collected by the sensor is more accurate and reliable, not just the apparent data, and the driver's health data obtained at the current moment can better reflect the driver's emotions earlier from a deep level, so it is not an apparent emotion, and the driver's health data obtained at the current moment and the driver's health data reference value are convenient for the vehicle end to accurately determine the intelligent driving mode based on this. In addition, the sensor data in this method uses data within a set period, which is beneficial for the vehicle end to avoid the influence of individual and environmental factors on the determination of the driving mode. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] In order to more clearly illustrate the specific implementation methods of the present invention or the technical solutions in the prior art, the drawings required for use in the specific implementation methods or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are some implementation methods of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0019] Figure 1 is a flowchart of the intelligent driving mode determination method provided in Example 1; Figure 2 is a flowchart of the intelligent driving mode determination method provided in Example 2; Figure 3 is a structural diagram of an intelligent driving mode determination device provided in Example 3; Figure 4is a schematic diagram of the structure of the intelligent driving mode determination device provided in Example 4; Figure 5 It is a schematic diagram of the structure of the electronic device provided in Example 5. DETAILED DESCRIPTION

[0020] The following is a description of exemplary embodiments of the present application in conjunction with the accompanying drawings, including various details of the embodiments of the present application to facilitate understanding, which should be considered as merely exemplary. Therefore, it should be recognized by those of ordinary skill in the art that various changes and modifications can be made to the embodiments described herein without departing from the scope and spirit of the present application. Similarly, for the sake of clarity and conciseness, the description of well-known functions and structures is omitted in the following description.

[0021] As mentioned in the background technology, the existing technology has the problem that the intelligent driving interaction mode only stays at the point where the car collects the driver's driving intention, and performs the corresponding action based on the driving intention expressed by the driver. For driving safety problems that may be caused by the driver's own emotions, the car cannot accurately determine and adjust the intelligent driving solution. In this regard, the present invention uses the current driver health data and driver health data reference value sent by the wearable device to judge the driver's emotional state, and then determines the intelligent driving mode in combination with the driver's facial image. The present invention is further described in detail below in conjunction with the embodiments.

[0022] Example 1 Figure 1 This is a flow chart of an intelligent driving mode determination method provided by this embodiment. The method of this embodiment is applicable to the vehicle side, which may be a vehicle computer or ECU, etc. For ease of understanding, this embodiment uses the vehicle computer as the execution subject of the method.

[0023] like Figure 1 As shown, this embodiment provides a method for determining an intelligent driving mode, comprising the following steps: S110. In response to a vehicle request, receiving current driver health data and a driver health data reference value sent by a wearable device.

[0024] The intelligent driving mode determination method of this embodiment receives the current driver health data and the driver health data reference value sent by the wearable device after responding to the vehicle request. The vehicle request includes automatically sending the request after the vehicle is started, or sending the request after the user issues an interactive instruction.

[0025] Among them, the driver's health data at the current moment includes skin sweat output, body temperature, galvanic skin response index, respiratory rate, heart rate and blood oxygen concentration, and the driver's health data reference value includes average skin sweat output, average body temperature, average galvanic skin response index, average respiratory rate, average heart rate and average blood oxygen concentration.

[0026] S120. Determine the driver's emotional state according to the current driver health data and the driver health data reference value.

[0027] Optionally, determining the driver's emotional state according to the current driver health data and the driver health data reference value includes: Determining a driver health data weight coefficient according to 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 used to characterize the correlation between the driver health data reference value and the driver's emotional state; The driver's emotional state is determined based on the current driver health data and the driver health data weight coefficient.

[0028] The default health data value is the default value of the health data pre-stored locally on the vehicle side. The driver health data default weight coefficient is set according to actual needs and can be calibrated. It can be understood that the default health data value and the driver health data default weight coefficient are corresponding.

[0029] Optionally, determining the driver's emotional state based on the current driver health data and the driver health data weight coefficient includes: inputting the current driver health data and the driver health data weight coefficient into a driver emotional state scoring formula, and outputting the driver emotional state score; and determining the driver's emotional state based on the scoring range to which the driver emotional state score belongs.

[0030] Optionally, the driver's emotional state scoring formula is: ES=a*S+b*T+c*E+d*R+e*P+f*B. Wherein ES is the driver's emotional state score, a is the weight coefficient of the skin perspiration in the driver's health data, b is the weight coefficient of the skin temperature in the driver's health data, c is the weight coefficient of the galvanic skin response index in the driver's health data, d is the weight coefficient of the respiratory rate in the driver's health data, e is the weight coefficient of the driver's health data center rate, f is the weight coefficient of the blood oxygen concentration in the driver's health data, S is the skin perspiration, T is the skin temperature, E is the galvanic skin response index, R is the respiratory rate, P is the heart rate, and B is the blood oxygen concentration. The units of the above driver health data weight coefficients are multiplied by 1 with the corresponding units of the driver's health data at the current moment. For example, when the driver's health data is skin temperature, the unit of the skin temperature is ℃, and the unit of the skin temperature weight coefficient is ℃ -1 The driver emotional state scoring formula integrates the driver's health data into a specific score, and organically transitions from health data to emotional scores.

[0031] The score calculated by the above formula is between 0 and 100, and the evaluation score can be divided into 7 score ranges: 0-20 (fear state), 21-30 (nausea state), 31-40 (sad state), 41-60 (calm state), 61-80 (happy state), 81-90 (surprised state), 91-100 (angry state). Emotions are divided into 7 basic emotion types, and the scores are graded to make the emotion judgment data more concrete.

[0032] Since the driver's personal physiological differences and climate environment lead to sensor data differences, which may cause the driver's emotional state score to be offset, this embodiment determines the driver's health data weight coefficient according to the driver's health data reference value, the default health data value and the driver's health data default weight coefficient to eliminate the score offset caused by the driver's personal physiological differences and the climate environment. Among them, the default health data value and the driver's health data default weight coefficient can be used as a standard. The driver's emotional state score calculated using the above default health data value and the driver's health data default weight coefficient is a standard score, and there is no score offset phenomenon. Based on this, when the driver's health data reference value and the default health data value are inconsistent, it is considered that the sensor data difference is generated, so the driver's health data weight coefficient needs to be adjusted. Of course, if they are consistent, then the driver's health data weight coefficient is the driver's health data default weight coefficient, and no further adjustment is required. This method comprehensively considers the driver's health data reference value, the default health data value and the driver's health data default weight coefficient, and the obtained driver's health data weight coefficient is more accurate and reliable.

[0033] Optionally, determining the driver health data weight coefficient according to the driver health data reference value, the default health data value and the driver health data default weight coefficient includes: Determining a driver health data weight coefficient calibration ratio according to the driver health data reference value and the default health data value; The driver health data weight coefficient is determined according to the weight coefficient calibration ratio and the driver health data default weight coefficient.

[0034] The above-mentioned driver health data weight coefficient calibration ratio can be calculated by the following formula: X=driver health data reference value / default health data value. The above-mentioned driver health data weight coefficient can be calculated by the following formula: driver health data default weight coefficient / X. As mentioned earlier, due to the differences in sensor data caused by personal physiological differences and climatic environment of drivers, the driver's emotional state score may be offset. This implementation method calibrates the health data weight coefficient by introducing the driver health data weight coefficient calibration ratio to avoid score offset. If the health data weight coefficient is not calibrated, the obtained score will deviate from the actual situation, thereby affecting the driver's emotional state recognition result.

[0035] Take the average body temperature as an example of driver health data. For example, the average body temperature reference value is 36.8℃, and the default average body temperature is 36.5℃. Then, the average body temperature weight coefficient calibration ratio is X=36.8 / 36.5=1.01. If the default weight coefficient of the average body temperature is 0.2℃ -1 , then the average body temperature weight coefficient = 0.2℃ -1 / 1.01=0.198℃ -1 .

[0036] S130: Determine an intelligent driving mode according to the driver's emotional state and the driver's facial image.

[0037] Among them, the driver's facial image can be captured by using an in-vehicle camera component.

[0038] Optionally, determining the intelligent driving mode according to the driver's emotional state and the driver's facial image includes: In the case where the driver's emotional state is a calm state, judging whether the driver is in a fatigue state according to the driver's facial image; if so, determining the intelligent driving mode to be a fatigue state intelligent driving mode; if not, determining the intelligent driving mode to be a calm emotional intelligent driving mode; When the driving emotional state is not a calm state, the intelligent driving mode is determined to be an intelligent driving mode corresponding to the emotion.

[0039] In the case where the driver's emotional state is calm, this optional implementation further combines the driver's facial image to determine whether the driver is in a fatigued state. If so, it is determined that the driver is in a fatigued state, and the intelligent driving mode is determined to be a fatigued intelligent driving mode. If not, it is determined that the driver is not in a fatigued state, and the intelligent driving mode is determined to be a calm emotional intelligent driving mode. In the case that the driver's emotional state is not calm, the intelligent driving mode is determined to be an intelligent driving mode corresponding to the emotion. For example, if the driver's emotional state 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 throttle mis-stepping correction, emergency steering assist, torque limit, speed limit, following distance, automatic braking, lane keeping, and emergency steering assist can be calibrated.

[0040] The above-mentioned intelligent driving mode determination method is applied to the vehicle side. After receiving the current driver health data and driver health data reference value sent by the wearable device, the driver's emotional state is determined according to the current driver health data and driver health data reference value, and then the intelligent driving mode is determined according to the driver's emotional state and the driver's facial image. This method is applied to the vehicle side. By associating the current driver health data, driver health data reference value, driver emotional state and intelligent driving mode, it is convenient for the vehicle to adjust the intelligent driving mode according to the driver's emotional state in a timely and accurate manner to ensure driving safety, and further determine the intelligent driving mode in combination with the driver's facial image, so that the obtained intelligent driving mode is more accurate.

[0041] Further, after receiving the current driver health data and the driver health data reference value sent by the wearable device in response to the vehicle request, the method further includes: Determining whether the current driver health data and the driver health data reference value are complete; If yes, then performing an operation of determining the driver's emotional state according to the current driver health data and the driver health data reference value; If not, a data download request is sent to the wearable device in a loop until the complete current driver health data and driver health data reference value are received within the request number threshold, or the number of data download requests reaches the request number threshold. If the current driver health data and driver health data reference value are still missing when the number of data download requests reaches the request number threshold, the default health data value of the missing data is called and a data missing prompt is sent.

[0042] This embodiment further judges after the driver receives the current driver health data and driver health data reference value sent by the wearable device. If the above data and data reference value are complete, the subsequent operation of determining the driver's emotional state will continue. If it is incomplete, it is necessary to send a data download request to the wearable device in a loop. When the complete data and data reference value are obtained within the request number threshold range (such as within 3 times), or the download request number reaches the request number threshold (such as 3 times), the download request is stopped. If the complete data and data reference value have been obtained when the download request is stopped, the subsequent operation of determining the driver's emotional state will continue. If the complete data and data reference value are not obtained, the default health data value of the missing data is called, and a data missing prompt is sent. The above missing prompt can be sent to the display module (such as the central control screen) on the vehicle computer for the driver to view and check the device, or sent to the wearable device to remind the driver to check the device.

[0043] Example 2 Figure 2 This is a flow chart of a method for determining an intelligent driving mode provided in this embodiment. The method of this embodiment is applicable to wearable devices. A wearable device refers to a portable device that is worn directly on the body or integrated into the user's clothes or accessories. A wearable device is not only a hardware device, but also realizes powerful functions through software support, data interaction, and cloud interaction. Wearable devices include smart watches, smart headband lights, and smart glasses lights. For ease of understanding, this embodiment uses a smart watch as the executor of the method.

[0044] like Figure 2 As shown, this embodiment provides a method for determining an intelligent driving mode, comprising the following steps: S210: Determine the driver's health data at the current moment according to the sensor data of the driver at the current moment.

[0045] The sensor data include skin conductance, skin temperature, bioelectrical impedance, respiratory rate collected by bioelectrical impedance sensor, respiratory rate and heart rate collected by PPG sensor, heart rate collected by ECG sensor and blood oxygen concentration collected by SpO2 sensor. The driver's health data at the current moment includes skin sweat output, body temperature, galvanic skin response index, respiratory rate, heart rate and blood oxygen concentration.

[0046] Among them, skin conductance can be collected by a skin conductance sensor, skin temperature can be collected by a skin temperature sensor, and bioimpedance can be collected by a bioimpedance sensor. The PPG sensor is a sensor that can collect heart rate and respiratory rate using the photoelectric capacitance labeling method. The ECG sensor is an electrocardiogram sensor, which mainly includes the P wave, PR interval, QRS wave, ST segment, T wave and QT interval data in the electrocardiogram; the smart watch 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.

[0047] Optionally, the amount of skin sweat is calculated using the following formula: S=a1·G, where S is the amount of skin sweat, in mL; a1 is a coefficient greater than 0; and G is skin conductance.

[0048] Body temperature is the temperature of your skin.

[0049] The galvanic skin response index is calculated using the following formula: E=b1·Z, where E is the galvanic skin response index, b1 is a coefficient greater than 0, and Z is the bioelectrical impedance.

[0050] Respiratory rate is calculated using the following formula: ; Where R is the respiratory rate (times / minute), f(p) is the respiratory rate (times / minute) collected by the PPG sensor, and f(z) is the respiratory rate (times / minute) collected by the bioimpedance sensor.

[0051] Heart rate is calculated using the following formula: ; Where P is the heart rate (beats / minute), F(P) is the heart rate (beats / minute) collected by the PPG sensor, and F(E) is the heart rate (beats / minute) collected by the ECG sensor.

[0052] The blood oxygen concentration is the blood oxygen concentration collected by the SpO2 sensor.

[0053] S220: Determine a reference value of the driver's health data based on the sensor data of the driver within a set period.

[0054] Among them, the driver health data reference values ​​include average skin perspiration, average body temperature, average skin conductivity index, average respiratory rate, average heart rate and average blood oxygen concentration.

[0055] Optionally, determining the driver health data reference value according to the sensor data of the driver within a set period includes: Based on skin conductance, the average amount of sweat produced by the skin is determined; Based on skin temperature, determine average body temperature; Based on the bioelectrical impedance, the average galvanic skin response index was determined; Determine the average respiratory rate based on the respiratory rate collected by the bioimpedance sensor and the respiratory rate collected by the PPG sensor; Determine an average heart rate based on the heart rate collected by the PPG sensor and the heart rate collected by the ECG sensor; The average blood oxygen concentration is determined based on the blood oxygen concentration collected by the SpO2 sensor.

[0056] The average amount of sweat secreted from the skin is calculated using the following formula: , where i is the sampling number, G(i) is the skin conductance collected at the i-th time, and m is the coefficient used to correct the skin conductance to the amount of skin sweat. The value of m is obtained based on medical statistical experiments, and its value is greater than 0. Its unit is the same as the unit of skin sweat after multiplying it with the unit of skin conductance.

[0057] The average body temperature is calculated using the following formula: , where i is the sampling number and T(i) is the skin temperature of the i-th sampling.

[0058] The average galvanic skin response index was calculated using the following formula: , where i is the sampling number, Z(i) is the bioimpedance of the i-th sampling, and n is the coefficient used to correct the bioimpedance to the galvanic skin response index. The value of n is obtained based on medical statistical experiments, and its value is greater than 0. Its unit is the same as the unit of the galvanic skin response index after multiplying it with the unit of the bioimpedance.

[0059] The average respiratory rate was calculated using the following formula: , where i is the sampling number, and R(i) is the respiratory frequency calculated by the i-th sampling. R(i) is calculated using the formula in S110.

[0060] The average heart rate is calculated using the following formula: , where i is the sampling number, and P(i) is the heart rate calculated at the i-th sampling. P(i) is calculated using the formula in S110.

[0061] The average blood oxygen concentration is calculated using the following formula: , where i is the sampling number, and B(i) is the blood oxygen concentration of the i-th sampling.

[0062] Optionally, the above-mentioned set period is 7 days, and the sampling interval in the set period can be 30 minutes. The driver health data reference value is refreshed periodically, and the data that cannot be refreshed uses the storage value of the smart watch in the previous period.

[0063] It should be noted that the sequence of S110 and S120 in this embodiment is only an example. In fact, S120 can be executed at the same time as S110 or before S110. The sequence of the two has no effect on the effect of this embodiment.

[0064] S230. In response to a vehicle request, the current driver health data and the driver health data reference value are sent to the vehicle side, so that the vehicle side determines the intelligent driving mode according to the current driver health data and the driver health data reference value.

[0065] After receiving the vehicle request, this step automatically sends the current driver health data and driver health data reference value to the vehicle end.

[0066] The above-mentioned intelligent driving mode determination method is applied to wearable devices, including determining the driver's health data at the current moment according to the driver's sensor data at the current moment, determining the driver's health data reference value according to the driver's sensor data within a set period, and then responding to the vehicle request, sending the driver's health data at the current moment and the driver's health data reference value to the vehicle end, so that the vehicle end determines the intelligent driving mode according to the driver's health data at the current moment and the driver's health data reference value. This method is applied to wearable devices, and the data collected by the sensor is more accurate and reliable, not just the apparent data, and the driver's health data obtained at the current moment can better reflect the driver's emotions earlier from a deeper level, so it is not an apparent emotion. The obtained driver's health data at the current moment and the driver's health data reference value facilitate the vehicle end to accurately determine the intelligent driving mode based on this. In addition, the sensor data in this method uses data within a set period, which is beneficial for the vehicle end to avoid the influence of individual and environmental factors on the determination of the driving mode.

[0067] Further, after sending the current driver health data and the driver health data reference value to the vehicle end in response to the vehicle request, it also includes: In response to a data download request sent by the vehicle end, the current driver health data and the driver health data reference value are sent to the vehicle end.

[0068] In some cases (such as equipment failure or network failure), the wearable device may not be able to send complete data. At this time, the vehicle side will send a data download request. Upon receiving the request, it will resend the current driver health data and driver health data reference value to the vehicle side so that the vehicle side can obtain the required data in time.

[0069] Further, after sending the current driver health data and the driver health data reference value to the vehicle side in response to the data download request sent by the vehicle side, it also includes: Receive the data missing prompt sent by the vehicle end, and control the wearable device to perform self-check.

[0070] When receiving the data missing prompt sent by the vehicle, it means that the wearable device may have a fault, so it is necessary to perform a device self-check to better identify and resolve the problem.

[0071] Example 3 like Figure 3 As shown, this embodiment provides an intelligent driving mode determination device, including: The data receiving module 101 is used to receive the current driver health data and the driver health data reference value sent by the wearable device in response to the vehicle request; A driver emotional state determination module 102, configured to determine the driver emotional state according to the current driver health data and the driver health data reference value; The intelligent driving mode determination module 103 is used to determine the intelligent driving mode according to the driver's emotional state and the driver's facial image.

[0072] The device is used to execute the method described in Example 1, and thus has at least functional modules and beneficial effects corresponding to the above method.

[0073] Example 4 like Figure 4 As shown, this embodiment provides an intelligent driving mode determination device, including: The current moment driver health data determination module 201 is used to determine the current moment driver health data according to the current moment driver sensor data; A driver health data reference value determination module 202, used to determine a driver health data reference value based on the sensor data of the driver within a set period; The data sending module 203 is used to send the current driver health data and the driver health data reference value to the vehicle side in response to the vehicle request, so that the vehicle side determines the intelligent driving mode according to the current driver health data and the driver health data reference value.

[0074] The device is used to execute the method described in Example 2, and thus has at least functional modules and beneficial effects corresponding to the above method.

[0075] Example 5 like Figure 5 As shown, this embodiment provides an electronic device, including: at least one processor; and a memory communicatively connected to at least one of the processors; wherein, The memory stores instructions executable by at least one of the processors, and the instructions are executed by at least one of the processors to enable at least one of the processors to perform the above method. At least one processor in the electronic device can perform the above method, and thus has at least the same advantages as the above method.

[0076] Optionally, the electronic device also includes interfaces for connecting various components, including high-speed interfaces and low-speed interfaces. The various components are connected to each other using different buses and can be installed on a common mainboard or installed in other ways as needed. The processor can process instructions executed in the electronic device, including instructions stored in or on a memory to display graphical information of a GUI (Graphical User Interface) on an external input / output device (such as a display device coupled to an interface). In other embodiments, if necessary, multiple processors can be used together with multiple memories, and / or multiple buses can be used together with multiple memories. Similarly, multiple electronic devices can be connected (for example, as a server array, a group of blade servers, or a multi-processor system), and each device provides some necessary operations. Figure 5 A processor 301 is taken as an example.

[0077] The memory 302 is a computer-readable storage medium that can be used to store software programs, computer executable programs and modules, such as program instructions / modules corresponding to the intelligent driving mode determination method in the embodiment of the present 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, that is, realizing the above-mentioned intelligent driving mode determination method.

[0078] The memory 302 may mainly include a program storage area and a data storage area, wherein the program storage area may store an operating system and at least one application required for a function; the data storage area may store data created according to the use of the terminal, etc. In addition, the memory 302 may include a high-speed random access memory, and may also include a non-volatile memory, such as at least one disk storage device, a flash memory device, or other non-volatile solid-state storage device. In some instances, the memory 302 may further include a memory remotely arranged relative to the processor 301, and these remote memories may be connected to the device via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0079] The electronic device may further include: an input device 303 and an output device 304. The processor 301, the memory 302, the input device 303 and the output device 304 may be connected via a bus or other means. Figure 5The example of connecting through bus is taken in the following.

[0080] The input device 303 can receive input digital or character information, and the output device 304 can include a display device, an auxiliary lighting device (e.g., LED), and a tactile feedback device (e.g., a vibration motor), etc. The display device can 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 can be a touch screen.

[0081] Example 6 This embodiment provides a computer-readable storage medium, on which computer instructions are stored, and the computer instructions are used to enable a computer to execute the above method. The computer instructions on the computer-readable storage medium are used to enable a computer to execute the above method, and thus have at least the same advantages as the above method.

[0082] The medium in the present invention may adopt any combination of one or more computer-readable media. The medium may be a computer-readable signal medium or a computer-readable storage medium. The medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or device, or any combination thereof. More specific examples of the medium (a non-exhaustive list) include: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination thereof. In this document, the medium may be any tangible medium containing or storing a program that may be used by or in combination with an instruction execution system, device, or device.

[0083] Computer-readable signal media may include data signals propagated in baseband or as part of a carrier wave, which carry computer-readable program code. Such propagated data signals may take a variety of forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the above. Computer-readable signal media may also be any computer-readable medium other than a computer-readable storage medium, which may send, propagate, or transmit a program for use by or in conjunction with an instruction execution system, apparatus, or device.

[0084] The program code contained on the computer-readable medium can be transmitted using any appropriate medium, including but not limited to wireless, wire, optical cable, RF (Radio Frequency), etc., or any suitable combination of the above.

[0085] Computer program code for performing the operations of the present invention may be written in one or more programming languages ​​or a combination thereof, including object-oriented programming languages, such as Java, Smalltalk, C++, and conventional procedural programming languages, such as "C" or similar programming languages. The program code may be executed entirely on the user's computer, partially on the user's computer, as a separate software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (e.g., through the Internet using an Internet service provider).

[0086] It should be understood that the various forms of processes shown above can be used to reorder, add or delete steps. For example, the steps recorded in this application can be executed in parallel, sequentially or in different orders, as long as the expected results of the technical solution disclosed in this application can be achieved, and this document is not limited here.

[0087] The above specific implementations do not constitute a limitation on the protection scope of this application. It should be understood by those skilled in the art 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 in the protection scope of this application.

Claims

1. A method for determining an intelligent driving mode, applied to a vehicle, characterized in that: include: In response to a vehicle request, receiving current driver health data and a driver health data reference value sent by a wearable device; Determining the driver's emotional state according to the current driver health data and the driver health data reference value; An intelligent driving mode is determined according to the driver's emotional state and the driver's facial image.

2. The intelligent driving mode determination method according to claim 1, characterized in that: After receiving the current driver health data and the driver health data reference value sent by the wearable device in response to the vehicle request, the method further includes: Determining whether the current driver health data and the driver health data reference value are complete; If yes, then performing an operation of determining the driver's emotional state according to the current driver health data and the driver health data reference value; If not, a data download request is sent to the wearable device in a loop until the complete current driver health data and driver health data reference value are received within the request number threshold, or the number of data download requests reaches the request number threshold. If the current driver health data and driver health data reference value are still missing when the number of data download requests reaches the request number threshold, the default health data value of the missing data is called and a data missing prompt is sent.

3. The intelligent driving mode determination method according to claim 1, characterized in that: The determining the driver's emotional state according to the current driver health data and the driver health data reference value includes: Determining a driver health data weight coefficient according to 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 used to characterize the correlation between the driver health data reference value and the driver's emotional state; The driver's emotional state is determined based on the current driver health data and the driver health data weight coefficient.

4. The intelligent driving mode determination method according to claim 3, characterized in that: The determining the driver health data weight coefficient according to the driver health data reference value, 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 value and the default health data value; The driver health data weight coefficient is determined according to the driver health data weight coefficient calibration ratio and the driver health data default weight coefficient.

5. The intelligent driving mode determination method according to claim 1, characterized in that: The step of determining the intelligent driving mode according to the driver's emotional state and the driver's facial image includes: In the case where the driver's emotional state is a calm state, judging whether the driver is in a fatigue state according to the driver's facial image; if so, determining the intelligent driving mode to be a fatigue state intelligent driving mode; if not, determining the intelligent driving mode to be a calm emotional intelligent driving mode; When the driving emotional state is not a calm state, the intelligent driving mode is determined to be an intelligent driving mode corresponding to the emotion.

6. A method for determining an intelligent driving mode, applied to a wearable device, characterized in that: include: Determine the driver's health data at the current moment according to the sensor data of the driver at the current moment; Determine the driver's health data reference value based on the driver's sensor data within a set period; In response to a vehicle request, the current driver health data and the driver health data reference value are sent to the vehicle end, so that the vehicle end determines the intelligent driving mode according to the current driver health data and the driver health data reference value.

7. The intelligent driving mode determination method according to claim 6, characterized in that: After sending the current driver health data and the driver health data reference value to the vehicle end in response to the vehicle request, the method further includes: In response to a data download request sent by the vehicle end, the current driver health data and the driver health data reference value are sent to the vehicle end.

8. The intelligent driving mode determination method according to claim 7, characterized in that: After sending the current driver health data and the driver health data reference value to the vehicle end in response to the data download request sent by the vehicle end, the method further includes: Receive a data missing prompt sent by the vehicle end, and control the wearable device to perform a self-check.

9. An electronic device, characterized in that: include: at least one processor, and a memory communicatively coupled to at least one of the processors; The memory stores instructions executable by at least one of the processors, and the instructions are executed by at least one of the processors so that the at least one processor can execute the method according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that: The medium stores computer instructions, and the computer instructions are used to enable a computer to execute the method according to any one of claims 1 to 7.

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