Vehicle control strategy determination method, vehicle, storage medium and computer product

Abstract

The invention discloses a vehicle control strategy determination method, a vehicle, a storage medium and a computer product, and relates to the technical field of vehicles, and the vehicle control strategy determination method specifically comprises the following steps: detecting the surrounding environment of the vehicle to obtain a detection result; when it is determined that the vehicle is in the emergency state according to the detection result, actual emotion data of a driver of the vehicle are obtained; and determining a scare state level corresponding to the driver based on the actual emotion data, and screening a plurality of preset emergency control strategies according to the scare state level to determine a target emergency control strategy. The technical effect that the vehicle can screen out the target emergency control strategy matched with the frightened state of the driver is achieved, and then potential safety hazards existing in the emergency state of the vehicle are reduced.

Description

Technical Field

[0001] The present application relates to the field of vehicle technology, and in particular to a method for determining a vehicle control strategy, a vehicle, a storage medium, and a computer program product. Background Art

[0002] With the continuous development of the automobile industry, automobiles have become the preferred means of transportation for more and more users in their daily travel. In related technologies, when a driver is driving a vehicle, he usually observes the surroundings of the vehicle to ensure the safety of the vehicle during driving.

[0003] However, when the driver observes an emergency situation around the vehicle, he or she usually enters a frightened state. In this way, the driver is prone to control the vehicle to perform incorrect avoidance operations in the frightened state, thereby causing a greater safety hazard to the vehicle in an emergency situation. Summary of the invention

[0004] The main purpose of this application is to provide a method for determining a vehicle control strategy, a vehicle, a storage medium and a computer program product, aiming to solve the technical problem in the related art that there are major safety hazards in vehicles under emergency conditions.

[0005] To achieve the above objectives, the present application proposes a method for determining a vehicle control strategy, the method comprising:

[0006] Detecting the surrounding environment of the vehicle to obtain a detection result;

[0007] When it is determined according to the detection result that the vehicle is in an emergency state, obtaining actual emotion data of the driver of the vehicle;

[0008] The startle state level corresponding to the driver is determined based on the actual emotion data, and a plurality of preset emergency control strategies are screened according to the startle state level to determine a target emergency control strategy.

[0009] In one embodiment, the step of obtaining the actual emotion data of the driver of the vehicle comprises:

[0010] Detecting the driver of the vehicle to obtain various biosignal data corresponding to the driver;

[0011] The actual emotion data of the driver is determined according to each of the bio-signal data.

[0012] In one embodiment, the step of determining the actual emotion data of the driver according to each of the bio-signal data comprises:

[0013] Determining facial change features corresponding to the driver according to facial signal data included in each of the bio-signal data, and determining actual emotion data of the driver based on the facial change features;

[0014] or,

[0015] Determining the sound change characteristics corresponding to the driver according to the sound signal data included in each of the bio-signal data, and determining the actual emotion data of the driver based on the sound change characteristics;

[0016] or,

[0017] The physiological signal data included in each of the biological signal data determines the physiological change characteristics corresponding to the driver, and the actual emotion data of the driver is determined based on the physiological change characteristics.

[0018] In one embodiment, the step of determining the actual emotion data of the driver according to each of the bio-signal data further includes:

[0019] Determine the biological change characteristic signal corresponding to each of the biological signal data, and fuse the biological change characteristic signals to obtain a fused change characteristic signal;

[0020] The actual emotion data of the driver is determined according to the fused change characteristic signal.

[0021] In one embodiment, the step of determining the fright state level corresponding to the driver based on the actual emotion data comprises:

[0022] Determine a plurality of preset emotion data ranges and preset fright state levels corresponding to the plurality of preset emotion data ranges;

[0023] Screening the plurality of preset emotion data ranges based on the actual emotion data to determine a target emotion data range that matches the actual emotion data;

[0024] The preset fright state level corresponding to the target emotion data range is determined as the fright state level corresponding to the driver.

[0025] In one embodiment, the step of screening a plurality of preset emergency control strategies according to the fright state level to determine a target emergency control strategy includes:

[0026] Determining a plurality of preset emergency control strategies and a plurality of preset fright state levels corresponding to each of the plurality of preset emergency control strategies;

[0027] screening the plurality of preset fright state levels according to the fright state level to determine a target fright state level that matches the fright state level;

[0028] The preset emergency control strategy corresponding to the target fright state level is determined as the target emergency control strategy.

[0029] In one embodiment, after the step of screening a plurality of preset emergency control strategies according to the fright state level to determine a target emergency control strategy, the method further includes:

[0030] Determine a target hardware device corresponding to the target emergency control strategy, wherein the target hardware device is a hardware device that needs to take over operation when the target emergency control strategy is running;

[0031] Detecting the target hardware device to determine whether the driver operates the target hardware device;

[0032] If it is determined that the driver has not operated the target hardware device, the target hardware device is controlled based on the target emergency control strategy to cause the vehicle to perform an emergency braking operation.

[0033] In one embodiment, after the step of determining the fright state level corresponding to the driver based on the actual emotion data, the method further includes:

[0034] Performing a verification operation on the fright state level according to each biological change characteristic signal of the driver to obtain a verification result;

[0035] When it is determined that the verification result is a passed verification, the step of screening a plurality of preset emergency control strategies according to the fright state level to determine a target emergency control strategy is performed.

[0036] In one embodiment, the step of performing a verification operation on the fright state level according to each biological change characteristic signal of the driver to obtain a verification result includes:

[0037] Determining the signal change threshold corresponding to each biological change characteristic signal, and comparing each biological change characteristic signal with the corresponding signal change threshold to obtain a plurality of comparison results;

[0038] When it is determined that there is a target comparison result among the plurality of comparison results, determining the verification result as verification failure, wherein the target comparison result is that the biological change characteristic signal does not reach the signal change threshold;

[0039] When it is determined that the target comparison result does not exist in the plurality of comparison results, the verification result is determined to be verification passed.

[0040] In addition, to achieve the above-mentioned purpose, the present application also proposes a vehicle, which includes: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the computer program is configured to implement the steps of the method for determining the vehicle control strategy as described above.

[0041] In addition, to achieve the above-mentioned purpose, the present application also proposes a storage medium, which is a computer-readable storage medium, and a computer program is stored on the storage medium. When the computer program is executed by the processor, the steps of the method for determining the vehicle control strategy as described above are implemented.

[0042] In addition, to achieve the above-mentioned purpose, the present application also provides a computer program product, which includes a computer program, and when the computer program is executed by a processor, it implements the steps of the method for determining the vehicle control strategy as described above.

[0043] The method for determining a vehicle control strategy provided in an embodiment of the present application obtains a detection result by detecting the surrounding environment of the vehicle; when it is determined that the vehicle is in an emergency state according to the detection result, actual emotional data of the driver of the vehicle is obtained; based on the actual emotional data, the corresponding fright state level of the driver is determined, and multiple preset emergency control strategies are screened according to the fright state level to determine a target emergency control strategy.

[0044] In this embodiment, the vehicle first detects the surrounding environment while driving to obtain the result. Then, when the vehicle determines that it is in an emergency state requiring emergency braking based on the detection result, the vehicle detects the driver to obtain the actual emotion data corresponding to the driver. Finally, the vehicle processes the actual emotion data to determine whether the driver is in a frightened state based on the actual emotion data and determines the fright state level corresponding to the driver. The vehicle then screens multiple preset emergency control strategies based on the fright state level to determine the target emergency control strategy that matches the fright state level.

[0045] In this way, the present application solves the technical problem in the related art that vehicles have great safety hazards in emergency situations. That is, the present application accurately identifies whether the driver is in a frightened state when it is determined that there is an emergency situation around, and determines the degree of the driver's fright state, so as to screen out a suitable emergency control strategy according to the degree of the fright state, thereby achieving the technical effect of enabling the vehicle to screen out a target emergency control strategy that matches the driver's fright state, thereby reducing the safety hazards of the vehicle in emergency situations. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present application and, together with the description, serve to explain the principles of the present application.

[0047] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, for ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0048] Figure 1 A flow chart of a method for determining a vehicle control strategy according to an embodiment of the present invention;

[0049] Figure 2 A flow chart of a second embodiment of a method for determining a vehicle control strategy of the present application;

[0050] Figure 3 A flow chart of a method for determining a vehicle control strategy according to a third embodiment of the present invention;

[0051] Figure 4 A brief flowchart of a method for determining a vehicle control strategy for this application;

[0052] Figure 5 A schematic diagram of the device structure of the hardware operating environment involved in the method for determining the vehicle control strategy in the embodiment of the present application.

[0053] The purpose, features and advantages of this application will be further described in conjunction with the embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0054] It should be understood that the specific embodiments described herein are only used to explain the technical solutions of the present application and are not used to limit the present application.

[0055] In order to better understand the technical solution of the present application, a detailed description will be given below in conjunction with the accompanying drawings and specific implementation methods.

[0056] In this embodiment, for the convenience of description, the following is an explanation of a vehicle equipped with an emotion detection module, an environment detection module, a VCU (Vehicle Control Unit), a high-precision emotion detection module, or a mobile terminal, a data storage control terminal, a PC and other terminals connected to an electronic control unit of the vehicle as the execution subject. Among them, the emotion detection module can be specifically composed of physiological signal detection units such as a camera, a microphone, and a heart rate sensor.

[0057] Based on the above-mentioned vehicle, an overall concept of a method for determining a vehicle control strategy of the present application is proposed herein.

[0058] With the continuous development of the automobile industry, cars have become the preferred means of transportation for more and more users in their daily travel. In the related technology, when a driver is driving a vehicle, he usually observes the surroundings of the vehicle to ensure the safety of the vehicle during driving. However, when the driver observes an emergency situation around the vehicle, he usually enters a frightened state. In this way, the driver is prone to control the vehicle to perform incorrect avoidance operations in a frightened state, which in turn causes a greater safety hazard to the vehicle in an emergency.

[0059] In response to the above phenomenon, the present application provides a method for determining a vehicle control strategy, which includes: detecting the surrounding environment of the vehicle to obtain a detection result; when it is determined that the vehicle is in an emergency state according to the detection result, obtaining actual emotional data of the driver of the vehicle; determining the fright state level corresponding to the driver based on the actual emotional data, and screening multiple preset emergency control strategies according to the fright state level to determine a target emergency control strategy.

[0060] In this way, the present application solves the technical problem in the related art that vehicles have great safety hazards in emergency situations. That is, the present application accurately identifies whether the driver is in a frightened state when it is determined that there is an emergency situation around, and determines the degree of the driver's fright state, so as to screen out a suitable emergency control strategy according to the degree of the fright state, thereby achieving the technical effect of enabling the vehicle to screen out a target emergency control strategy that matches the driver's fright state, thereby reducing the safety hazards of the vehicle in emergency situations.

[0061] Based on the overall concept of the method for determining the vehicle control strategy of the present application, the embodiment of the present application provides a method for determining the vehicle control strategy, referring to Figure 1 , Figure 1 This is a flow chart of the first embodiment of the method for determining the vehicle control strategy of the present application.

[0062] In this embodiment, the method for determining the vehicle control strategy includes steps S10 to S30:

[0063] Step S10: Detect the surrounding environment of the vehicle to obtain a detection result;

[0064] In this embodiment, when the vehicle is driving, the vehicle controller configured in the vehicle first calls the environment detection module configured on the vehicle to detect the vehicle's surrounding environment, so as to determine whether there is a high-speed moving object approaching the vehicle in the vehicle's surrounding environment to generate a detection result.

[0065] Exemplarily, for example, when a vehicle is driving, the VCU configured inside the vehicle first controls the environmental detection module configured on the vehicle to detect the vehicle's surrounding environment through the radar detection unit configured in the environmental detection module to obtain a point cloud set. The VCU then determines the driving parameters of other moving objects around the vehicle based on the point cloud set, and generates detection results based on each driving parameter.

[0066] In this way, the vehicle can detect the surrounding environment while driving, and determine whether there are high-speed moving objects approaching itself in the surrounding environment.

[0067] Step S20: when it is determined according to the detection result that the vehicle is in an emergency state, obtaining actual emotion data of the driver of the vehicle;

[0068] It should be noted that the emergency state is a state where an emergency situation exists around the vehicle and the vehicle needs to perform emergency braking. It is understandable that when there is a high-speed moving object approaching the vehicle in the surrounding environment of the vehicle, the driver will enter a state of shock to varying degrees, and the driver is prone to misoperation in a state of shock. In this case, the vehicle needs to perform braking operations in advance before the driver performs an erroneous operation to avoid risks. In addition, the actual emotion data is data that can reflect the driver's emotional state. It is understandable that the driver's emotional state type can be judged as any one of surprise, calmness or anger based on the actual emotion data.

[0069] In this embodiment, when the vehicle controller determines that the detection result is that there are high-speed moving objects around the vehicle, it determines that the vehicle is in an emergency state requiring emergency braking. At this time, the vehicle controller calls the emotion detection module configured in the vehicle to detect the driver of the vehicle, thereby obtaining multiple bio-signal data of the driver, and then determines the actual emotion data that can indicate the driver's emotional state based on each bio-signal data.

[0070] Exemplarily, for example, when the VCU determines based on the point cloud set that there is a high-speed moving object approaching the vehicle in the vehicle's surrounding environment, it determines that the vehicle is in an emergency state requiring emergency braking. At this time, the VCU controls the emotion detection module configured in the vehicle, so as to detect the driver driving the vehicle through the physiological signal detection units such as the camera, microphone, heart rate sensor configured in the emotion detection module, so as to obtain the driver's facial signal data, sound signal data, heart rate signal data and other biological signal data, and obtain actual emotion data that can indicate the driver's emotion type based on each biological signal data.

[0071] In this way, the vehicle can call on its own configured physiological signal detection units such as cameras, microphones, blood pressure sensors, etc. to detect the driver to obtain actual emotional data that can indicate the driver's emotional category, and then judge whether the driver is in a frightened state based on the actual emotional data, and determine the fright state level corresponding to the frightened state.

[0072] In a feasible implementation manner, the step of “obtaining actual emotion data of the driver of the vehicle” in the above step S20 may specifically include steps S201 to S202:

[0073] Step S201: detecting the driver of the vehicle by the emotion detection module to obtain various biological signal data corresponding to the driver;

[0074] Step S202: determining the actual emotion data of the driver according to each of the bio-signal data.

[0075] It should be noted that the bio-signal data is signal data that can change with the driver's emotions, and specifically may include: facial signal data, sound signal data and physiological signal data.

[0076] In this embodiment, when the vehicle controller determines that the vehicle is in an emergency state, it first calls the emotion detection module configured on the vehicle to detect the driver in the vehicle, thereby obtaining the driver's corresponding facial signal data, sound signal data, physiological signal data and other bio-signal data, and then the vehicle controller inputs each bio-signal data into the high-precision emotion recognition module configured in the vehicle, so that the high-precision emotion recognition module performs feature processing on each bio-signal data to obtain actual emotion data that can indicate the driver's emotional state, and the high-precision emotion recognition module uploads the obtained actual emotion data to the vehicle controller.

[0077] For example, when the VCU determines that the vehicle is in an emergency state, it first controls the emotion detection module configured on the vehicle, so that the emotion detection module detects the driver through the camera configured by itself to obtain target image data including the driver and with a frame rate of more than 30 frames per second. The VCU then processes the target image data to extract facial signal data that can reflect the driver's facial information. At the same time, the emotion detection module calls the microphone configured by itself to detect the driver to obtain a target voice signal including the sound emitted by the driver and with a sampling rate of more than 16kHz. The VCU then processes the target voice signal to extract At the same time, the emotion detection module calls the heart rate detection unit configured by itself to access the heart rate detection device worn by the driver, so as to obtain the physiological signal data that can reflect the driver's heart rate information through the heart rate detection device, and upload the physiological signal data to the VCU. After that, the VCU inputs the facial signal data, sound signal data and physiological signal data into the high-precision emotion recognition module configured in the vehicle. The high-precision emotion recognition module processes the various biological signal data to obtain the actual emotion data that can indicate the driver's emotional state, and the high-precision emotion recognition module uploads the obtained actual emotion data to the VCU.

[0078] In this way, the vehicle can call on its own configured physiological signal detection units such as cameras, microphones, blood pressure sensors, etc. to detect the driver to obtain actual emotional data that can indicate the driver's emotional category, and then judge whether the driver is in a frightened state based on the actual emotional data, and determine the fright state level corresponding to the frightened state.

[0079] In a feasible implementation manner, the above step S202 may specifically include steps S2021 to S2023:

[0080] Step S2021: determining the facial change features corresponding to the driver according to the facial signal data included in each of the bio-signal data, and determining the actual emotion data of the driver based on the facial change features; or,

[0081] Step S2022: determining the sound change characteristics corresponding to the driver according to the sound signal data included in each of the bio-signal data, and determining the actual emotion data of the driver based on the sound change characteristics; or,

[0082] Step S2023: The physiological signal data contained in each of the biological signal data determine the physiological change characteristics corresponding to the driver, and determine the actual emotional data of the driver based on the physiological change characteristics.

[0083] In this embodiment, after the vehicle controller inputs the collected bio-signal data into the high-precision emotion recognition module, the high-precision emotion recognition module first extracts the facial signal data contained in each bio-signal data, and processes the facial signal data to identify the facial change characteristics corresponding to the driver. The high-precision emotion recognition module then queries a preset facial emotion mapping table based on the facial change characteristics to determine the actual emotion data corresponding to the driver. Alternatively, the high-precision emotion recognition module extracts the sound signal data contained in each bio-signal data, and processes the sound signal data to identify the sound change characteristics corresponding to the driver. The high-precision emotion recognition module then queries a preset sound emotion mapping table based on the sound change characteristics to determine the actual emotion data corresponding to the driver. Alternatively, the high-precision emotion recognition module extracts the physiological signal data contained in each bio-signal data, and processes the physiological signal data to identify the physiological change characteristics corresponding to the driver. The high-precision emotion recognition module then queries a preset physiological emotion mapping table based on the physiological change characteristics to determine the actual emotion data corresponding to the driver. The high-precision emotion recognition module then uploads the acquired actual emotion data to the vehicle controller.

[0084] Exemplarily, for example, after the VCU inputs the collected bio-signal data into the high-precision emotion recognition module, the high-precision emotion recognition module first extracts the facial signal data contained in the bio-signal data, and performs feature processing on the facial signal data to determine the actual pupil motion features that can indicate the movement of the driver's pupil position. After that, the high-precision emotion recognition module reads the storage module configured in the vehicle to obtain a facial emotion mapping table containing multiple benchmark pupil motion features and benchmark emotion data corresponding to each of the multiple benchmark pupil motion features. The high-precision emotion recognition module then queries the facial emotion mapping table based on the acquired pupil motion features to determine a target pupil motion feature that matches the actual pupil motion feature from the multiple benchmark pupil motion features, and determines the benchmark emotion data corresponding to the target pupil motion feature as the actual emotion data corresponding to the driver, and then uploads the acquired actual emotion data to the VCU;

[0085] or,

[0086] The high-precision emotion recognition module can also extract the sound signal data contained in each biological signal data, and process the sound signal data based on MFCC (Mel Frequency Cepstral Coefficients) to extract the actual tone change features that can indicate the driver's voice change. After that, the high-precision emotion recognition module reads the storage module to obtain a sound emotion mapping table containing multiple benchmark tone change features and benchmark emotion data corresponding to each of the multiple benchmark tone change features. The high-precision emotion recognition module then queries the sound emotion mapping table based on the actual tone change features obtained to determine a target tone change feature that matches the actual tone change feature among the multiple benchmark tone change features, and determines the benchmark emotion data corresponding to the target tone change feature in the sound emotion mapping table as the actual emotion data corresponding to the driver, and then uploads the obtained actual emotion data to the VCU;

[0087] or,

[0088] The high-precision emotion recognition module can also extract the physiological signal values ​​contained in each biological signal data, and perform FFT (Fast Fourier Transform) processing on the physiological signal values ​​for frequency analysis, so as to obtain the actual heart rate variability characteristics that can indicate the driver's heart rate changes. After that, the high-precision emotion recognition module reads the storage module to obtain a physiological emotion mapping table containing multiple benchmark heart rate variability characteristics and benchmark emotion data corresponding to each of the multiple benchmark heart rate variability characteristics. The high-precision emotion recognition module then queries the physiological emotion mapping table based on the actual heart rate variability characteristics obtained to determine the target heart rate variability characteristics that match the actual heart rate variability characteristics among the multiple benchmark heart rate variability characteristics, and determines the benchmark emotion data corresponding to the target heart rate variability characteristics in the physiological emotion mapping table as the actual emotion data corresponding to the driver, and then uploads the obtained actual emotion data to the VCU.

[0089] In this way, the vehicle can call on its own configured physiological signal detection units such as cameras, microphones, blood pressure sensors, etc. to detect the driver to obtain actual emotional data that can indicate the driver's emotional category, and then judge whether the driver is in a frightened state based on the actual emotional data, and determine the fright state level corresponding to the frightened state.

[0090] In a feasible implementation manner, the above step S202 may further include steps S2024 to S2025:

[0091] Step S2024: determining the biological change characteristic signals corresponding to the respective biological signal data, and fusing the respective biological change characteristic signals to obtain a fused change characteristic signal;

[0092] Step S2025: Determine the actual emotion data of the driver according to the fused change characteristic signal.

[0093] In this embodiment, after the vehicle controller inputs the collected bio-signal data into the high-precision emotion recognition module, the high-precision emotion recognition module can also first process the bio-signal data to obtain the biological change feature signal corresponding to each bio-signal data. The high-precision emotion recognition module then fuses the biological change feature signals to obtain a fused change feature signal. After that, the high-precision emotion recognition module queries a preset feature emotion mapping table according to the fused change feature signal to determine the actual emotion data that matches the fused change feature signal.

[0094] Exemplarily, for example, after the VCU inputs the collected bio-signal data into the high-precision emotion recognition module, the high-precision emotion recognition module can also first extract the facial signal data, sound signal data and physiological signal data contained in each bio-signal data, and process the facial signal data, sound signal data and physiological signal data respectively to obtain actual pupil movement features, actual voice tone change features and actual heart rate variability features. The high-precision emotion recognition module then fuses the actual pupil movement features, actual voice tone change features and actual heart rate variability features to obtain a fused feature vector. After that, the high-precision emotion recognition module processes the fused feature vector through its own configured vector machine SVM (Support Vector Machine) to query a feature emotion mapping table containing multiple benchmark fusion features and benchmark emotion data corresponding to each of the multiple benchmark fusion features, thereby determining the actual emotion data corresponding to the driver, and then uploading the acquired actual emotion data to the VCU.

[0095] In this way, the vehicle can call on its own configured physiological signal detection units such as cameras, microphones, blood pressure sensors, etc. to detect the driver to obtain actual emotional data that can indicate the driver's emotional category, and then judge whether the driver is in a frightened state based on the actual emotional data, and determine the fright state level corresponding to the frightened state.

[0096] Step S30: determining the fright state level corresponding to the driver based on the actual emotion data, and screening a plurality of preset emergency control strategies according to the fright state level to determine a target emergency control strategy;

[0097] It should be noted that the fright state level is a numerical value that can reflect the degree of the driver's fright state, specifically including: mild fright level, moderate fright level and severe fright level. Among them, the mild fright level indicates that the driver's fright state is relatively mild, and the driver will only slightly adjust the vehicle speed or direction in the frightened state. At this time, the driver's facial expression does not change obviously and the physiological signal data changes little. Similarly, the moderate fright level indicates that the driver's fright state is of medium degree, and the driver will obviously change the vehicle operation in the frightened state. At this time, the driver's facial expression and physiological signal data both have relatively obvious changes. Similarly, the severe fright level indicates that the driver's fright state is relatively severe, and the driver will make drastic operations such as sharp turns and sudden brakes in the frightened state. At this time, the driver's facial expression and physiological signal data both have greater changes.

[0098] In this embodiment, after acquiring the actual emotion data, the vehicle controller processes the actual emotion data to determine the driver's corresponding fright state level. The vehicle controller then screens multiple preset emergency control strategies according to the fright state level to determine a target emergency control strategy that matches the fright state level. The vehicle controller then controls the vehicle according to the target emergency control strategy to avoid vehicle safety accidents.

[0099] Exemplarily, for example, after obtaining the actual emotion data, the VCU further determines, based on the actual emotion data, whether the driver's corresponding fright level is a mild fright level, a moderate fright level, or a severe fright level. The VCU then screens a plurality of preset emergency control strategies based on the fright state level to determine a target emergency control strategy that matches the fright state level. The VCU then controls the vehicle according to the target emergency control strategy to remind the driver of surrounding high-speed moving objects through various hardware devices configured in the vehicle, and controls the vehicle to perform emergency braking operations when the driver fails to make correct operations.

[0100] In addition, in this embodiment and another embodiment, after obtaining the actual emotion data corresponding to the driver, the high-precision emotion recognition module can also read the above-mentioned storage module to obtain a preset emotion data threshold, and compare the actual emotion data with the emotion data threshold to obtain a comparison result. Thereafter, when the high-precision emotion recognition module determines that the driver is in a frightened state when the comparison result is that the actual emotion data reaches the emotion data threshold, and further determines the corresponding frightened state level of the driver based on the actual emotion data, so that the degree of the frightened state can be further determined while ensuring that the driver is in a frightened state, thereby improving the accuracy of driver emotion detection.

[0101] In this way, when the vehicle determines that the driver is in a frightened state, it can further determine the driver's degree of fright, and select a suitable target emergency control strategy from multiple preset emergency control strategies based on the degree of fright, and then control the vehicle according to the target emergency control strategy to avoid safety hazards in the vehicle.

[0102] In a feasible implementation manner, the step of "determining the fright state level corresponding to the driver based on the actual emotion data" in the above step S30 may specifically include steps S301 to S303:

[0103] Step S301: determining a plurality of preset emotion data ranges and preset fright state levels corresponding to the plurality of preset emotion data ranges;

[0104] Step S302: screening the plurality of preset emotion data ranges based on the actual emotion data to determine a target emotion data range that matches the actual emotion data;

[0105] Step S303: determining the preset fright state level corresponding to the target emotion data range as the fright state level corresponding to the driver.

[0106] In this embodiment, after acquiring the actual emotion data, the vehicle controller first determines multiple preset emotion data ranges and the preset fright state levels corresponding to the multiple preset emotion data ranges. Then, the vehicle controller compares the actual emotion data acquired with the multiple preset emotion data ranges respectively to determine the target emotion data range that matches the actual emotion data. Finally, the vehicle controller determines the preset fright state level corresponding to the target emotion data range as the fright state level corresponding to the target emotion data range.

[0107] Exemplarily, for example, after acquiring the actual emotion data, the VCU first determines the preset emotion data ranges corresponding to the mild fright level, the moderate fright level or the severe fright level, and then the VCU compares the acquired actual emotion data with multiple preset emotion data ranges, thereby determining the target emotion data range containing the actual emotion data in multiple preset emotion data ranges, and finally, if the fright state level corresponding to the target emotion data range is a severe fright level, the VCU determines that the fright state level corresponding to the driver is a severe fright level.

[0108] In this way, the vehicle can further determine the driver's degree of fright when it is determined that the driver is in a frightened state.

[0109] In a feasible implementation manner, the step of "screening multiple preset emergency control strategies according to the fright state level to determine the target emergency control strategy" in the above step S30 may specifically include steps S304 to S306:

[0110] Step S304: determining a plurality of preset emergency control strategies and preset fright state levels corresponding to the plurality of preset emergency control strategies;

[0111] Step S305: screening a plurality of preset startle state levels according to the startle state level to determine a target startle state level that matches the startle state level;

[0112] Step S306: determining the preset emergency control strategy corresponding to the target startle state level as the target emergency control strategy.

[0113] In this embodiment, after determining the fright state level corresponding to the driver, the vehicle controller further obtains multiple preset emergency control strategies and determines the preset fright state levels corresponding to each of the multiple preset emergency control strategies. Afterwards, the vehicle controller compares the obtained fright state level with the multiple preset fright state levels respectively to determine a target fright state level that matches the fright state level. Finally, the vehicle controller determines the preset emergency control strategy corresponding to the target fright state level as the target emergency control strategy to be adopted.

[0114] Exemplarily, for example, after determining that the driver's corresponding fright state level is a severe fright level, the VCU further obtains a strategy mapping table containing multiple preset emergency control strategies and the preset fright state levels corresponding to the multiple preset emergency control strategies. Thereafter, the VCU queries the strategy mapping table based on the severe fright level to determine, among the multiple preset emergency control strategies contained in the strategy mapping table, a preset emergency control strategy that can issue a reminder to the driver and control the vehicle to perform emergency braking as a target emergency control strategy that matches the severe fright level. The VCU calls upon various hardware devices configured in the vehicle according to the target emergency control strategy to remind the driver to pay attention to the surrounding high-speed moving objects, and controls the vehicle to perform emergency braking when the driver fails to make correct operations.

[0115] In this embodiment, when the vehicle is driving, the vehicle controller configured in the vehicle first calls the environment detection module configured on the vehicle to detect the environment around the vehicle, so as to determine whether there is a high-speed moving object approaching the vehicle in the environment around the vehicle to generate a detection result. After that, when the vehicle controller determines that the detection result is that there is a high-speed moving object around the vehicle, it determines that the vehicle is in an emergency state requiring emergency braking. At this time, the vehicle controller calls the emotion detection module configured in the vehicle to detect the driver of the vehicle, so as to obtain multiple biological signal data of the driver, and then determines the actual emotion data that can indicate the emotional state of the driver based on each biological signal data. Finally, the vehicle controller processes the actual emotion data to determine the corresponding fright state level of the driver. The vehicle controller then screens multiple preset emergency control strategies based on the fright state level to determine the target emergency control strategy that matches the fright state level. The vehicle controller then controls the vehicle according to the target emergency control strategy to avoid vehicle safety accidents.

[0116] In this way, the present application solves the technical problem in the related art that vehicles have great safety hazards in emergency situations. That is, the present application accurately identifies whether the driver is in a frightened state when it is determined that there is an emergency situation around, and determines the degree of the driver's fright state, so as to screen out a suitable emergency control strategy according to the degree of the fright state, thereby achieving the technical effect of enabling the vehicle to screen out a target emergency control strategy that matches the driver's fright state, thereby reducing the safety hazards of the vehicle in emergency situations.

[0117] Based on the first embodiment of the present application, the second embodiment of the present application is proposed here. In the second embodiment of the present application, the same or similar contents as those of the above-mentioned embodiment 1 can be referred to the above introduction and will not be repeated later. Figure 2 , Figure 2 A flow chart of the second embodiment of the method for determining the vehicle control strategy of the present application is provided, such as Figure 2 As shown, after the above step S30, the method for determining the vehicle control strategy of the present application may further include steps A10 to A30:

[0118] Step A10: determining a target hardware device corresponding to the target emergency control strategy, wherein the target hardware device is a hardware device that needs to take over operation when the target emergency control strategy is running;

[0119] Step A20: Detecting the target hardware device to determine whether the driver operates the target hardware device;

[0120] Step A30: If it is determined that the driver has not operated the target hardware device, the target hardware device is controlled based on the target emergency control strategy to make the vehicle perform an emergency braking operation.

[0121] In this embodiment, after determining the target emergency control strategy, the vehicle controller first screens the hardware devices in the vehicle according to the target emergency control strategy to determine the target hardware devices that the vehicle controller needs to take over when the target emergency control strategy is running. After that, the vehicle controller detects the target hardware devices to determine whether the driver operates the target hardware devices. Finally, if the vehicle controller determines that the driver has not operated the target hardware devices, it controls the target hardware devices based on the target emergency control strategy to make the vehicle perform emergency braking operations.

[0122] Exemplarily, for example, after the VCU determines that the target emergency control strategy is the target emergency control strategy corresponding to the severe startle level, it first screens multiple hardware devices in the vehicle according to the target emergency control strategy to determine that the brake pedal in the vehicle is the target hardware device that needs to be taken over next among the multiple hardware devices. After that, the VCU detects the brake pedal to determine whether the driver has stepped on the brake pedal. Finally, if the VCU determines that the driver has not stepped on the brake pedal, it determines that the driver is in a frightened state and has failed to take evasive action in time. At this time, the VCU activates the early warning mechanism based on the target emergency control strategy to remind the driver to pay attention to the surrounding high-speed moving objects through the various hardware devices configured in the vehicle, and controls the operation of the brake pedal to perform emergency braking operations.

[0123] In this way, the vehicle can determine the target hardware device that needs to be taken over by the target emergency strategy after determining the target emergency strategy, and then take over the target hardware device in time when it detects that the driver is unable to actively operate the target hardware device to avoid an emergency, thereby avoiding safety hazards during vehicle driving.

[0124] Based on the first embodiment and / or the second embodiment of the present application, the third embodiment of the present application is proposed here. In the third embodiment of the present application, the same or similar contents as those of the above embodiments can be referred to the above introduction and will not be described in detail later. Figure 3 , Figure 3 A flow chart of the third embodiment of the method for determining the vehicle control strategy of the present application is provided, such as Figure 3 As shown, after the step of "determining the fright state level corresponding to the driver based on the actual emotion data" in the above step S30, the method for determining the vehicle control strategy of the present application may further include steps B10 to B20:

[0125] Step B10: performing a verification operation on the fright state level according to each biological change characteristic signal of the driver to obtain a verification result;

[0126] Step B20: When it is determined that the verification result is a passed verification, the step of screening a plurality of preset emergency control strategies according to the fright state level to determine a target emergency control strategy is performed.

[0127] In this embodiment, after determining the fright state level corresponding to the driver, the vehicle controller further performs a verification operation on the fright state level according to the biological change characteristic signals corresponding to the driver to obtain a verification result. Afterwards, if the vehicle controller determines that the verification result is passed, it screens multiple preset emergency control strategies according to the fright state level to determine a target emergency control strategy that matches the fright state level. The vehicle controller then controls the vehicle according to the target emergency control strategy to avoid vehicle safety accidents.

[0128] Exemplarily, for example, after the VCU determines that the driver's corresponding fright state level is a severe fright level, it can also first read the driver's corresponding actual pupil movement characteristics, actual voice tone change characteristics and actual heart rate variability characteristics, and the VCU then verifies the fright state level according to the actual pupil movement characteristics, the actual voice tone change characteristics and the actual heart rate variability characteristics to obtain a verification result. Thereafter, when the VCU determines that the verification result is passed, it determines that the driver is indeed in a severe fright state, and thus screens multiple preset emergency control strategies according to the severe fright level to determine a target emergency control strategy that matches the severe fright level. The VCU then controls the vehicle according to the target emergency control strategy to remind the driver to pay attention to the surrounding high-speed moving objects through the various hardware devices configured in the vehicle, and controls the vehicle to perform emergency braking operations when the driver fails to make correct operations.

[0129] In this way, when the vehicle detects that the driver is in a frightened state, it verifies the fright state level through the driver's corresponding biological change characteristic signal, thereby ensuring that the vehicle avoids performing emergency braking operations due to misjudging that the driver is in a frightened state.

[0130] In a feasible implementation manner, the above step B10 may specifically include steps B101 to B103:

[0131] Step B101: determining the signal change threshold corresponding to each biological change characteristic signal, and comparing each biological change characteristic signal with the corresponding signal change threshold to obtain a plurality of comparison results;

[0132] Step B102: when it is determined that there is a target comparison result among the plurality of comparison results, determining the verification result as verification failure, wherein the target comparison result is that the biological change characteristic signal does not reach the signal change threshold;

[0133] Step B103: When it is determined that the target comparison result does not exist in the plurality of comparison results, the verification result is determined to be verification passed.

[0134] In this embodiment, after determining the fright state level corresponding to the driver, the vehicle controller can also read the various biological change characteristic signals corresponding to the driver, and determine the signal change threshold corresponding to each biological change characteristic signal. The vehicle controller then compares each biological change characteristic signal with the corresponding signal change threshold to obtain multiple comparison results. Thereafter, when the vehicle controller determines that there is a target comparison result in which the biological change characteristic signal does not reach the signal change threshold among the multiple comparison results, the verification result is determined to be verification failure. Similarly, when the vehicle controller determines that there is no target comparison result in which the biological change characteristic signal does not reach the signal change threshold among the multiple comparison results, the verification result is determined to be verification passed.

[0135] Exemplarily, for example, after the CU determines that the fright state level corresponding to the driver is a severe fright level, it can also first read the actual pupil movement characteristics, actual voice tone change characteristics and actual heart rate variability characteristics corresponding to the driver, and the VCU further determines the facial signal change threshold corresponding to the actual pupil movement characteristics, and determines the sound signal change threshold corresponding to the actual voice tone change characteristics, and determines the heart rate sound signal change threshold corresponding to the actual heart rate variability characteristics. The VCU then compares the actual pupil movement characteristics with the facial signal change threshold, and compares the actual voice tone change characteristics with the sound signal change threshold, and compares the actual heart rate variability characteristics with the heart rate sound signal change threshold to obtain multiple comparison results, and then the VCU reads multiple comparison results and determines whether the multiple comparison results are all biological change feature signals that reach the signal change threshold. When the VCU determines that at least one comparison is that the biological change feature signal does not reach the signal change threshold, it determines that the driver's facial / voice / heart rate parameters are in a stable state, thereby determining that the above-mentioned fright state level is a misjudgment, and determining that the verification result is verification failure. Similarly, when the VCU determines that multiple comparison results are all biological change feature signals that reach the signal change threshold, it determines that the driver's facial, voice, and heart rate parameters are all in a fluctuating state, thereby determining that the above-mentioned fright state level is an accurate detection result, and determining that the verification result is verification success.

[0136] In this way, when the vehicle detects that the driver is in a frightened state, it verifies the fright state level through the driver's corresponding biological change characteristic signal, thereby ensuring that the vehicle avoids performing emergency braking operations due to misjudging that the driver is in a frightened state.

[0137] In addition, in this embodiment and another embodiment, after determining that the verification result is a passed verification, the vehicle controller can further obtain the historical emotion data corresponding to the driver when the emergency control strategy was triggered last time, and compare the obtained historical emotion data with the actual emotion data to obtain a comparison result. After that, when the vehicle controller determines that the comparison result is that the historical emotion data and the actual emotion data are inconsistent, it determines that the above-obtained fright state level is a misjudgment, and determines that the verification result is a failed verification. Similarly, when the vehicle controller determines that the comparison result is that the historical emotion data and the actual emotion data are consistent, it determines that the above-obtained fright state level is an accurate detection result, and determines that the verification result is a passed verification. In this way, the vehicle can verify the detected fright state level in many aspects, thereby ensuring that the vehicle avoids the situation where the driver is misjudged to be in a frightened state and performs an emergency braking operation.

[0138] For example, in order to help understand the implementation process of the method for determining the vehicle control strategy obtained by combining this embodiment with the above embodiments, please refer to Figure 4 , Figure 4 A brief flow chart of a method for determining a vehicle control strategy is provided, specifically:

[0139] In this embodiment, when the vehicle is driving, the vehicle controller configured in the vehicle first calls the environment detection module configured on the vehicle to detect the surrounding environment of the vehicle to obtain a detection result. After that, when the vehicle controller determines that the detection result is that there is a high-speed moving object approaching the vehicle in the surrounding environment, it determines that the vehicle is in an emergency state requiring emergency braking. At this time, the vehicle controller calls the emotion detection module configured in the vehicle to detect the driver of the vehicle, thereby obtaining the driver's facial signal data, sound signal data and physiological signal data. The vehicle controller then inputs the acquired facial signal data, sound signal data and physiological signal data and other biological signal data into the high-precision emotion detection module configured in the vehicle. The high-precision emotion detection module processes the facial signal data, sound signal data and physiological signal data to extract the actual pupil movement characteristics, actual voice tone change characteristics and actual heart rate variability characteristics corresponding to the driver. The high-precision emotion detection module then fuses the actual pupil movement characteristics, actual voice tone change characteristics and actual heart rate variability characteristics to obtain a fused feature vector, and determines the actual emotion data corresponding to the driver according to the fused feature vector, and then uploads the actual emotion value to the vehicle controller;

[0140] Afterwards, the vehicle controller determines the corresponding fright state level of the driver according to the actual emotion data, and determines the signal change thresholds corresponding to the actual pupil movement characteristics, the actual voice tone change characteristics and the actual heart rate variability characteristics. The vehicle controller then compares the actual pupil movement characteristics, the actual voice tone change characteristics and the actual heart rate variability characteristics with their corresponding signal change thresholds to obtain multiple comparison results. Then, when the vehicle controller determines that the multiple comparison results are all biological change feature signals reaching the signal change threshold, it screens multiple preset emergency control strategies based on the fright state level to determine the target emergency control strategy that matches the fright state level. The vehicle controller screens the various hardware devices in the vehicle according to the target emergency control strategy to determine that when the target emergency control strategy is running, the target hardware device that the vehicle controller needs to take over is the brake pedal. The vehicle controller then detects the brake pedal to determine whether the driver has triggered the brake pedal. Finally, if the vehicle controller determines that the driver has not triggered the brake pedal, it activates the early warning mechanism according to the target emergency control strategy to remind the driver to pay attention to the surrounding high-speed moving objects through the various hardware devices configured in the vehicle, and controls the operation of the brake pedal to perform emergency braking operations.

[0141] It should be noted that the above examples are only used to understand the present application and do not constitute a limitation on the method for determining the vehicle control strategy of the present application. More simple transformations based on this technical concept are all within the scope of protection of the present application.

[0142] The present application provides a vehicle, comprising: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute the method for determining the vehicle control strategy in the above-mentioned embodiment one.

[0143] Reference below Figure 5 , which shows a schematic diagram of the structure of a vehicle suitable for implementing the embodiments of the present application. The vehicle in the embodiments of the present application may include, but is not limited to, a vehicle with an emotion detection module, an environment detection module, a VCU, a high-precision emotion detection module, or a mobile terminal, a data storage control terminal, a PC, etc. connected to an electronic control unit of the vehicle. Figure 5 The vehicle shown is merely an example and should not bring any limitation to the functions and scope of use of the embodiments of the present application.

[0144] like Figure 5As shown, the vehicle may include a processing device 1001 (e.g., a central processing unit, a graphics processor, etc.), which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM: Read Only Memory) 1002 or a program loaded from a storage device 1003 to a random access memory (RAM: Random Access Memory) 1004. In RAM1004, various programs and data required for vehicle operation are also stored. The processing device 1001, ROM1002, and RAM1004 are connected to each other via a bus 1005. An input / output (I / O) interface 1006 is also connected to the bus. Generally, the following systems can be connected to the I / O interface 1006: an input device 1007 including, for example, a touch screen, a touch pad, a keyboard, a mouse, an image sensor, a microphone, an accelerometer, a gyroscope, etc.; an output device 1008 including, for example, a liquid crystal display (LCD: Liquid Crystal Display), a speaker, a vibrator, etc.; a storage device 1003 including, for example, a magnetic tape, a hard disk, etc.; and a communication device 1009. The communication device 1009 can allow the vehicle to communicate with other devices wirelessly or by wire to exchange data. Although the figure shows a vehicle with various systems, it should be understood that it is not required to implement or have all the systems shown. More or fewer systems can be implemented or have alternatively.

[0145] In particular, according to the embodiments disclosed in the present application, the process described above with reference to the flowchart can be implemented as a computer software program. For example, the embodiments disclosed in the present application include a computer program product, which includes a computer program carried on a computer-readable medium, and the computer program includes a program code for executing the method shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from a network through a communication device, or installed from a storage device 1003, or installed from a ROM 1002. When the computer program is executed by the processing device 1001, the above-mentioned functions defined in the method of the embodiment disclosed in the present application are executed.

[0146] The vehicle provided by the present application adopts the method for determining the vehicle control strategy in the above embodiment, which can solve the technical problem of the vehicle having a large safety hazard in an emergency state in the related technology. Compared with the prior art, the beneficial effects of the vehicle provided by the present application are the same as the beneficial effects of the method for determining the vehicle control strategy provided by the above embodiment, and the other technical features of the vehicle are the same as the features disclosed in the method of the previous embodiment, which will not be repeated here.

[0147] It should be understood that the various parts disclosed in this application can be implemented by hardware, software, firmware or a combination thereof. In the description of the above embodiments, specific features, structures, materials or characteristics can be combined in any one or more embodiments or examples in a suitable manner.

[0148] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

[0149] The present application provides a computer-readable storage medium having computer-readable program instructions (ie, computer programs) stored thereon, the computer-readable program instructions being used to execute the method for determining the vehicle control strategy in the above-mentioned embodiment.

[0150] The computer-readable storage medium provided in the present application may be, for example, a USB flash drive, but is not limited to electrical, magnetic, optical, electromagnetic, infrared, or semiconductor systems or devices, or any combination of the above. More specific examples of computer-readable storage media may include, but are not limited to: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM: Random Access Memory), a read-only memory (ROM: Read Only Memory), an erasable programmable read-only memory (EPROM: Erasable Programmable Read Only Memory or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM: CD-Read Only Memory), an optical storage device, a magnetic storage device, or any suitable combination of the above. In this embodiment, the computer-readable storage medium may be any tangible medium containing or storing a program that can be used by or in combination with an instruction execution system or device. The program code contained on the computer-readable storage medium may be transmitted using any appropriate medium, including but not limited to: wires, optical cables, RF (Radio Frequency: Radio Frequency), etc., or any suitable combination of the above.

[0151] The computer-readable storage medium may be included in the vehicle, or may exist independently without being installed in the vehicle.

[0152] The above-mentioned computer-readable storage medium carries one or more programs. When the above-mentioned one or more programs are executed by the vehicle, the vehicle: detects the surrounding environment of the vehicle through the environmental detection module to obtain a detection result; when it is determined that the vehicle is in an emergency state according to the detection result, obtains the actual emotional data of the driver of the vehicle; determines the corresponding fright state level of the driver based on the actual emotional data, and screens multiple preset emergency control strategies according to the fright state level to determine the target emergency control strategy.

[0153] Computer program code for performing the operations of the present application 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., via the Internet using an Internet service provider).

[0154] The flow chart and block diagram in the accompanying drawings illustrate the possible architecture, function and operation of the system, method and computer program product according to various embodiments of the present application. In this regard, each square box in the flow chart or block diagram can represent a module, a program segment or a part of a code, and the module, the program segment or a part of the code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the square box can also occur in a sequence different from that marked in the accompanying drawings. For example, two square boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each square box in the block diagram and / or flow chart, and the combination of the square boxes in the block diagram and / or flow chart can be implemented with a dedicated hardware-based system that performs a specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

[0155] The modules involved in the embodiments described in this application may be implemented by software or hardware, wherein the name of the module does not constitute a limitation on the unit itself in some cases.

[0156] The readable storage medium provided by the present application is a computer-readable storage medium, which stores computer-readable program instructions (i.e., computer programs) for executing the above-mentioned method for determining the vehicle control strategy, and can solve the technical problem of the vehicle having a large safety hazard in an emergency state in the related art. Compared with the prior art, the beneficial effects of the computer-readable storage medium provided by the present application are the same as the beneficial effects of the method for determining the vehicle control strategy provided by the above-mentioned embodiment, and will not be repeated here.

[0157] The present application also provides a computer program product, including a computer program, which implements the steps of the method for determining the vehicle control strategy as described above when the computer program is executed by a processor.

[0158] The computer program product provided by this application can solve the technical problem in the related art that there are great safety hazards in vehicles under emergency conditions. Compared with the prior art, the beneficial effects of the computer program product provided by this application are the same as the beneficial effects of the method for determining the vehicle control strategy provided by the above embodiment, and will not be repeated here.

[0159] The above descriptions are only some embodiments of the present application, and are not intended to limit the patent scope of the present application. All equivalent structural changes made using the contents of the present application specification and drawings under the technical concept of the present application, or direct / indirect applications in other related technical fields are included in the patent protection scope of the present application.

Claims

1. A method for determining a vehicle control strategy, characterized in that: The method for determining the vehicle control strategy includes: Detecting the surrounding environment of the vehicle to obtain a detection result; When it is determined according to the detection result that the vehicle is in an emergency state, obtaining actual emotion data of the driver of the vehicle; The startle state level corresponding to the driver is determined based on the actual emotion data, and a plurality of preset emergency control strategies are screened according to the startle state level to determine a target emergency control strategy.

2. The method for determining a vehicle control strategy according to claim 1, characterized in that: The step of obtaining actual emotion data of the driver of the vehicle comprises: Detecting the driver of the vehicle to obtain various biosignal data corresponding to the driver; The actual emotion data of the driver is determined according to each of the bio-signal data.

3. The method for determining a vehicle control strategy according to claim 2, characterized in that: The step of determining the actual emotion data of the driver according to each of the bio-signal data comprises: Determining facial change features corresponding to the driver according to facial signal data included in each of the bio-signal data, and determining actual emotion data of the driver based on the facial change features; or, Determining the sound change characteristics corresponding to the driver according to the sound signal data included in each of the bio-signal data, and determining the actual emotion data of the driver based on the sound change characteristics; or, The physiological signal data included in each of the biological signal data determines the physiological change characteristics corresponding to the driver, and the actual emotion data of the driver is determined based on the physiological change characteristics.

4. The method for determining a vehicle control strategy according to claim 2, characterized in that: The step of determining the actual emotion data of the driver according to each of the bio-signal data further includes: Determine the biological change characteristic signal corresponding to each of the biological signal data, and fuse the biological change characteristic signals to obtain a fused change characteristic signal; The actual emotion data of the driver is determined according to the fused change characteristic signal.

5. The method for determining a vehicle control strategy according to claim 1, characterized in that: The step of determining the fright state level corresponding to the driver based on the actual emotion data comprises: Determine a plurality of preset emotion data ranges and preset fright state levels corresponding to the plurality of preset emotion data ranges; Screening the plurality of preset emotion data ranges based on the actual emotion data to determine a target emotion data range that matches the actual emotion data; The preset fright state level corresponding to the target emotion data range is determined as the fright state level corresponding to the driver.

6. The method for determining a vehicle control strategy according to claim 1, characterized in that: The step of screening a plurality of preset emergency control strategies according to the fright state level to determine a target emergency control strategy comprises: Determining a plurality of preset emergency control strategies and a plurality of preset fright state levels corresponding to each of the plurality of preset emergency control strategies; screening the plurality of preset fright state levels according to the fright state level to determine a target fright state level that matches the fright state level; The preset emergency control strategy corresponding to the target fright state level is determined as the target emergency control strategy.

7. The method for determining a vehicle control strategy according to claim 1, characterized in that: After the step of screening a plurality of preset emergency control strategies according to the fright state level to determine a target emergency control strategy, the method further includes: Determine a target hardware device corresponding to the target emergency control strategy, wherein the target hardware device is a hardware device that needs to take over operation when the target emergency control strategy is running; Detecting the target hardware device to determine whether the driver operates the target hardware device; If it is determined that the driver has not operated the target hardware device, the target hardware device is controlled based on the target emergency control strategy to cause the vehicle to perform an emergency braking operation.

8. The method for determining a vehicle control strategy according to claim 1, characterized in that: After the step of determining the fright state level corresponding to the driver based on the actual emotion data, the method further includes: Performing a verification operation on the fright state level according to each biological change characteristic signal of the driver to obtain a verification result; When it is determined that the verification result is a passed verification, the step of screening a plurality of preset emergency control strategies according to the fright state level to determine a target emergency control strategy is performed.

9. The method for determining a vehicle control strategy according to claim 8, characterized in that: The step of performing a verification operation on the fright state level according to each biological change characteristic signal of the driver to obtain a verification result comprises: Determining the signal change threshold corresponding to each biological change characteristic signal, and comparing each biological change characteristic signal with the corresponding signal change threshold to obtain a plurality of comparison results; When it is determined that there is a target comparison result among the plurality of comparison results, determining the verification result as verification failure, wherein the target comparison result is that the biological change characteristic signal does not reach the signal change threshold; When it is determined that the target comparison result does not exist in the plurality of comparison results, the verification result is determined to be verification passed.

10. A vehicle, characterized in that: The vehicle comprises: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the computer program is configured to implement the steps of the method for determining a vehicle control strategy according to any one of claims 1 to 9.

11. A storage medium, characterized in that: The storage medium is a computer-readable storage medium, and a computer program is stored on the storage medium. When the computer program is executed by a processor, the steps of the method for determining the vehicle control strategy as described in any one of claims 1 to 9 are implemented.

12. A computer program product, characterized in that The computer program product comprises a computer program, and when the computer program is executed by a processor, the steps of the method for determining a vehicle control strategy according to any one of claims 1 to 9 are implemented.

Images