Brake control method, device and equipment based on driver behaviors and medium

Abstract

The invention relates to the field of vehicle intelligent control, in particular to a brake control method, device and equipment based on driver behaviors and a medium. By detecting the driving emotion data and the operation data of the driver, the state of the driver can be known comprehensively. The extracted key emotional characteristics, such as anxiety or exhaustion perceived from sightedness and intonation change of a driver, are combined with driving operation characteristics and tension control reflected by fine shaking of a steering wheel, a driving scene is determined, an automatic braking decision is made to fit reality, misjudgment is reduced, and driving safety is improved. In order to solve the traditional problems, the mode that braking is triggered by sensing the environment purely through a sensor outside a vehicle is abandoned, and the defect that the sensor loses efficacy or misjudgment is caused by rain, snow, strong light and the like is overcome. Meanwhile, the recognition result is not interfered by noisy and accent in the vehicle, but focuses on the performance of the driver. The automatic braking function is accurately triggered, and driving stability and safety are practically guaranteed.

Description

Technical Field

[0001] The present invention relates to the field of vehicle intelligent control, and in particular to a driver behavior-based braking control method, device, equipment and medium. Background Art

[0002] At present, autonomous driving and intelligent assisted driving technologies have become the focus of the industry. Among them, environmental perception relies on various sensors, such as cameras and millimeter-wave radars, which are responsible for collecting key information such as road conditions and obstacles around the vehicle to provide a basis for driving decisions; at the same time, voice control functions are gradually becoming popular, aiming to provide drivers and passengers with a convenient operating experience. However, existing technologies face many challenges.

[0003] On the one hand, the sensor is seriously disturbed by the external environment. When it rains, raindrops may block the camera's line of sight and interfere with the reflection of the laser radar wave. When it snows, the flying snowflakes not only blur the field of vision, but also change the shape of objects due to accumulation, causing the sensor to misjudge. The glare caused by strong sunlight overexposes the camera and affects the image clarity. Moreover, most sensors are installed outside the car and are easily corroded by dust and water vapor due to long-term exposure. These factors combined can easily cause the sensor to fail to perceive the external environment or significantly reduce its judgment ability, and fail to trigger the brakes in time at critical moments, endangering driving safety. On the other hand, voice control has frequent problems in actual applications. Daily communication between people on the car and crying children will generate high-decibel noise. It is difficult for the voice recognition system to accurately distinguish between commands and noise. Changes in tone when happy and dialect accents also increase the difficulty of recognition, causing the brake command to be triggered incorrectly in scenes where the brake should not be braked, which brings great trouble to normal driving. Summary of the invention

[0004] In view of this, an embodiment of the present invention provides a brake control method, device, equipment and medium based on driver behavior to solve the problem that vehicle sensors are interfered by environmental factors and have inaccurate perception, and voice recognition is prone to misjudgment, which affects the inaccurate triggering of the automatic braking function.

[0005] In a first aspect, an embodiment of the present invention provides a brake control method based on driver behavior, the method comprising:

[0006] Detect the driver's driving emotion data and driving operation data during vehicle driving;

[0007] extracting key emotion features based on the driving emotion data, and extracting key operation features based on the driving operation data;

[0008] Analyzing the key emotional features and the key operating features to obtain the driver's emotional state and driving conditions;

[0009] The vehicle is controlled to automatically perform a braking operation according to the emotional state and / or driving situation.

[0010] Furthermore, the detection of the driver's driving emotion data and driving operation data during vehicle driving includes:

[0011] Detecting the voice data of the driver during the driving of the vehicle, and collecting the facial image data of the driver during the driving of the vehicle;

[0012] Detecting sensing data of the driver acting on a steering wheel in a vehicle, detecting a driving behavior video of the driver, and extracting driving behavior data from the driving behavior video;

[0013] The driving emotion data is constructed based on the voice data and the facial image data, and the driving operation data is constructed based on the sensing data and the driving behavior data.

[0014] Furthermore, the analyzing the key emotional features and the key operational features to obtain the driver's emotional state and driving conditions includes:

[0015] Extracting voice emotion features and facial expression features from the key emotion features;

[0016] Extracting hand sensing features and dangerous behavior features from the key operation features;

[0017] Obtaining weights corresponding to the voice emotion feature, the facial expression feature, the hand sensing feature, and the dangerous behavior feature, respectively;

[0018] Based on the weights corresponding to the voice emotion feature, the facial expression feature, the hand sensing feature and the dangerous behavior feature, and weighted calculation of the voice emotion feature, the facial expression feature, the hand sensing feature and the dangerous behavior feature, a weighted value is obtained;

[0019] Using the emotional state corresponding to the weighted value as the emotional state of the driver;

[0020] The driving safety factors corresponding to the hand sensing features and the dangerous behavior features are calculated, and the driving condition of the driver is determined based on the driving safety system.

[0021] Further, controlling the vehicle to automatically perform a braking operation according to the emotional state and / or driving condition includes:

[0022] If the emotional state is a loss of control state, and / or the driving condition is dangerous driving, then obtaining the current driving environment of the vehicle;

[0023] planning a safe parking route for the vehicle based on the driving environment;

[0024] The vehicle is controlled to travel along the safe parking route, and the vehicle is controlled to perform a braking operation when reaching an end point of the safe parking route.

[0025] Furthermore, the planning of a safe parking route for the vehicle based on the driving environment includes:

[0026] Acquiring driving data of other vehicles in the driving environment;

[0027] planning a driving trajectory of the other vehicles based on the driving data, and detecting a safe parking area in the driving environment;

[0028] A safe parking route for the vehicle is planned based on the driving trajectory and the safe parking area.

[0029] Furthermore, the method further comprises:

[0030] During the process of the vehicle safely driving the safe parking route, detecting environmental data inside the vehicle;

[0031] Analyzing the degree of influence of the environmental data on the driver's emotions;

[0032] If the degree of emotional impact is greater than a preset value, the target component of the vehicle is controlled to perform environmental adjustment according to a preset strategy.

[0033] Furthermore, the method further comprises:

[0034] When a safe parking route cannot be planned in the driving environment, searching for roadside equipment within a preset range where the vehicle is located;

[0035] If there is a road test device, a help signal is sent to the road test device, and the vehicle is controlled to perform a braking operation; wherein the help signal carries the vehicle identification of the vehicle and the emotional state and / or driving condition of the driver, and the roadside device is used to broadcast the vehicle identification according to the help signal, so that other vehicles in the driving environment where the vehicle is located can ask for help from the vehicle;

[0036] A response signal is received from the other vehicle, and the vehicle is controlled to be unlocked based on the response signal.

[0037] In a second aspect, an embodiment of the present invention provides a brake control device based on driver behavior, the device comprising:

[0038] A detection module, used to detect the driver's driving emotion data and driving operation data during vehicle driving;

[0039] An extraction module, configured to extract key emotion features based on the driving emotion data, and to extract key operation features based on the driving operation data;

[0040] An analysis module, used for analyzing the key emotional features and the key operating features to obtain the emotional state and driving conditions of the driver;

[0041] The control module is used to control the vehicle to automatically perform braking operations according to the emotional state and / or driving conditions.

[0042] In a third aspect, an embodiment of the present invention provides a computer device, comprising: a memory and a processor, the memory and the processor being communicatively connected to each other, the memory storing computer instructions, and the processor executing the method of the first aspect or any corresponding embodiment thereof by executing the computer instructions.

[0043] In a fourth aspect, an embodiment of the present invention provides a computer-readable storage medium having computer instructions stored thereon, the computer instructions being used to enable a computer to execute the method of the first aspect or any corresponding embodiment thereof.

[0044] This application can fully understand the driver's status by detecting the driver's driving emotion data and operation data. The extracted key emotional features, such as the driver's anxiety or fatigue detected from sighs and changes in tone, are combined with driving operation characteristics, such as tense control reflected by subtle shaking of the steering wheel, to determine the driving scenario, so that the automatic braking decision fits the reality, reduce misjudgment, and improve driving safety. In solving traditional problems, the method of simply relying on external sensors to sense the environment to trigger the brakes is abandoned, avoiding the drawbacks of sensor failure or misjudgment due to rain, snow, strong light, etc. At the same time, the recognition results will not be affected by the noise and accent in the car, but focus on the driver's own performance. The automatic braking function can be triggered accurately to ensure the stability and safety of driving. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] 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.

[0046] Figure 1 is a flow chart of a brake control method based on driver behavior according to some embodiments of the present invention;

[0047] Figure 2is a flow chart of another brake control method based on driver behavior according to some embodiments of the present invention;

[0048] Figure 3 is a structural block diagram of a brake control device based on driver behavior according to an embodiment of the present invention;

[0049] Figure 4 It is a schematic diagram of the hardware structure of a computer device according to an embodiment of the present invention. DETAILED DESCRIPTION

[0050] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative work are within the scope of protection of the present invention.

[0051] According to an embodiment of the present invention, a driver behavior-based braking control method, device, equipment and medium are provided. It should be noted that the steps shown in the flowchart of the accompanying drawings can be executed in a computer system such as a set of computer executable instructions, and although a logical order is shown in the flowchart, in some cases, the steps shown or described can be executed in an order different from that shown here.

[0052] In this embodiment, a braking control method based on driver behavior is provided. Figure 1 is a flow chart of a brake control method based on driver behavior according to an embodiment of the present invention, such as Figure 1 As shown, the process includes the following steps:

[0053] Step S101, detecting the driving emotion data and driving operation data of the driver during vehicle driving.

[0054] In an embodiment of the present application, the driving emotion data and driving operation data of the driver during the driving process of the vehicle are detected, including: detecting the voice data of the driver during the driving process of the vehicle, and collecting the facial image data of the driver during the driving process of the vehicle. Detecting the sensing data of the driver acting on the steering wheel in the vehicle, and detecting the driving behavior video of the driver, and extracting the driving behavior data from the driving behavior video. Constructing the driving emotion data based on the voice data and the facial image data, and constructing the driving operation data based on the sensing data and the driving behavior data.

[0055] Specifically, during the driving process of the vehicle, the professional audio acquisition equipment installed in the vehicle (such as a high-sensitivity microphone, etc.) is used to capture the driver's voice in real time, including various types of sound information such as speaking, sighing, coughing, etc., and these sound data will be continuously recorded. At the same time, the camera equipped in the car (generally installed in a suitable position, such as above the dashboard, near the rearview mirror, etc., to ensure that the driver's face can be clearly captured) is used to take real-time photos of the driver's face to obtain his facial image data. The image data covers all details of facial expressions, such as frowning, smiling, eye state, etc., and will be continuously collected at a certain frame rate (such as several frames per second) to form a coherent image sequence.

[0056] There are sensors installed on the steering wheel, which can accurately sense the force applied by the driver on the steering wheel, the direction of rotation, the angle of rotation, the speed of rotation and other aspects of sensor data. When the vehicle is driving, these sensors will always monitor and record relevant information. In addition, video recording equipment is installed at a suitable position in the car (such as the rear position of the top of the car, etc., which can cover a larger range of driving operation areas) to record the driver's entire driving behavior process and form a driving behavior video. Afterwards, with the help of image recognition and analysis technology, the video is analyzed frame by frame to extract driving behavior data such as the strength and frequency of stepping on the accelerator and brakes, gear shifting operations, and whether there are any illegal operations, so as to present these complex driving behaviors in a digitized form.

[0057] The collected sound data and facial image data are integrated, and specialized emotion analysis algorithms and models (these algorithms and models are usually trained based on a large amount of sample data and can identify the relationship between different sound features, facial expressions and emotions) are used to analyze the tone, timbre, volume changes and changes in facial expressions, and comprehensively judge the driver's current emotional state, such as calmness, anxiety, anger or fatigue, so as to construct driving emotion data. The collected steering wheel sensing data and extracted driving behavior data are sorted out according to certain logical rules and data association methods, such as combining the steering wheel rotation angle with the vehicle speed, brake operation, etc., in order to reflect the driver's actual operating characteristics and driving ability level, and then construct driving operation data, providing a comprehensive data basis for further analysis of the driver's status.

[0058] Step S102: extracting key emotion features based on the driving emotion data, and extracting key operation features based on the driving operation data.

[0059] In the embodiment of the present application, first, the collected and constructed driving emotion data is imported into a dedicated data analysis and processing module. This module usually uses relevant technologies such as machine learning and deep learning, for example, an emotion recognition model based on a convolutional neural network (CNN) or a recurrent neural network (RNN) can be used.

[0060] For the sound data, we analyze its audio features, such as the pitch changes, volume fluctuations, and speech speed, which can reflect the driver's emotional excitement, nervousness, etc. For example, when the pitch suddenly rises, the speech speed increases, and the volume becomes louder, it may mean that the driver is becoming impatient or excited.

[0061] For facial image data, we will focus on the characteristic changes of key parts of the face, such as the degree of eye opening and closing, the raising or wrinkling of eyebrows, the raising or lowering of the corners of the mouth, and other expression features. Through the image recognition algorithm, these key parts are accurately located, and their morphological change data at different times are extracted to determine whether they are happy, angry, sad, or tired. Then, with the help of the trained emotion classification model, the various audio features and facial expression features extracted above are used as input. After complex calculations and feature mapping of the model, the key emotional features that can accurately represent the driver's emotional state are finally screened and refined, such as "emotional excitement level" and "fatigue index". These key features can highly summarize the driver's current emotional core situation and provide strong support for subsequent analysis.

[0062] Similarly, the constructed driving operation data is passed to the corresponding data analysis module, which will work based on professional driving behavior analysis theory and related mathematical statistical methods.

[0063] For steering wheel sensing data, focus on analyzing features such as the frequency of steering wheel rotation, the maximum angle of a single rotation, the average rotation angle, and the stability of the rotation angle change. For example, if the steering wheel rotation frequency is too high and the single rotation angle changes greatly, it may mean that the driver is in a panic or the road conditions are complex and the direction needs to be adjusted frequently. From the driving behavior data, pay attention to key aspects such as the force change curve of braking and accelerator, the timeliness of operation, and the smoothness of gear shifting. For example, the braking force fluctuates and the operation is not timely, which reflects the driver's unstable operation or lack of concentration. Then, use data mining and feature extraction algorithms to conduct a comprehensive analysis of these scattered operation-related features, remove some redundant information, and extract the key operation features that best reflect the driver's driving operation style, proficiency, and current operation status.

[0064] Step S103, analyzing key emotional features and key operating features to obtain the driver's emotional state and driving conditions.

[0065] In the embodiment of the present application, analyzing the key emotional features and the key operating features to obtain the driver's emotional state and driving conditions includes the following steps A1-A6:

[0066] Step A1, extracting voice emotion features and facial expression features from key emotion features.

[0067] Specifically, for voice emotion feature extraction, we will focus on acoustic parameters in voice data that are closely related to emotional expression, such as the trend of pitch change (gradually rising or falling), the dynamic change amplitude of volume (stable or fluctuating), the rhythm change of speech speed (rapid or slow), and the characteristics of timbre (such as hoarseness, sharpness, etc.). Through speech emotion analysis technology, these acoustic parameters are quantitatively analyzed, combined with the classification model trained by a large number of existing speech samples with labeled emotions, to identify specific emotional categories such as happiness, sadness, anger, calmness, etc. contained in the voice, and then extract the corresponding voice emotion features.

[0068] In terms of facial expression feature extraction, image recognition and analysis algorithms are used to accurately locate key facial expression areas, such as eyes, eyebrows, mouth, etc. Then, the morphological changes of these parts are monitored, such as changes in pupil size, blinking frequency, eyebrow upward angle, frowning degree, mouth opening range, upward or downward angle of mouth corners, etc. Based on professional models for facial expression recognition, these quantified morphological change data are further converted into corresponding expression features, such as smiling, frowning, surprise and other facial expression features, so as to complete the operation of extracting sound emotional features and facial expression features from key emotional features.

[0069] Step A2: extracting hand sensing features and dangerous behavior features from key operation features.

[0070] Specifically, the extraction of hand sensing features is mainly based on the data collected by the sensing devices on the steering wheel and other control components. The force change curve when the hand acts on the steering wheel is analyzed, including the peak value, average value and frequency of force change (whether it is frequent and strong operation or smooth and moderate operation); at the same time, the angle change of the hand turning the steering wheel is examined, such as the size of the single turning angle, the continuity of the continuous turning angle, the smoothness of the steering operation, etc. Through in-depth mining and analysis of these data, hand sensing features that can reflect the hand operation characteristics and force control are extracted.

[0071] For the extraction of dangerous behavior features, we will focus on checking the driving behavior data for operational behaviors that violate traffic rules or are likely to cause safety accidents. For example, whether the frequency of sudden braking and acceleration is too high (judged by analyzing the sudden changes in the braking and accelerator pedaling force and the corresponding time intervals), whether there are behaviors such as changing lanes without turning on the turn signal for a long time, frequently crossing lane lines, etc. (using video analysis to identify lane lines and turn signal status). Based on these judgment bases, dangerous behavior features are screened and extracted from key operational features to clarify potential dangerous situations in the driving process.

[0072] Step A3, obtaining weights corresponding to voice emotion features, facial expression features, hand sensing features, and dangerous behavior features, respectively.

[0073] Specifically, first, a large amount of past driving case data is collected, which covers the actual performance of different drivers' voice emotion characteristics, facial expression characteristics, hand sensing characteristics and dangerous behavior characteristics under various road conditions, and also records the corresponding actual driving results (such as whether an accident occurred, driving stability, etc.). Then, data analysis algorithms (such as regression analysis, correlation analysis, etc.) are used to analyze the degree of correlation between these characteristics and driving results.

[0074] At the same time, combined with the judgment of the importance of each feature on driving given by experts in the field of transportation based on their professional knowledge and long-term practical experience, for example, experts believe that dangerous behavior features have a relatively high impact on driving safety, so their weight will be set relatively high; while voice emotion features have a relatively small impact on direct driving safety, so their weight may be set lower.

[0075] Based on the degree of correlation obtained from big data analysis and expert experience, we ultimately determine the weight values ​​corresponding to voice emotion features, facial expression features, hand sensing features, and dangerous behavior features, preparing for subsequent weighted calculations.

[0076] Step A4, based on the weights corresponding to the voice emotion features, facial expression features, hand sensing features and dangerous behavior features, and weighted calculation of the voice emotion features, facial expression features, hand sensing features and dangerous behavior features, a weighted value is obtained.

[0077] Specifically, the acquired voice emotion features, facial expression features, hand sensing features, dangerous behavior features and their corresponding weight values ​​are input into the weighted calculation module.

[0078] The weighted calculation module will perform calculations according to the established weighted calculation formula. For example, assuming that the voice emotion feature value is A, its corresponding weight is a; the facial expression feature value is B, the weight is b; the hand sensing feature value is C, the weight is c; the dangerous behavior feature value is D, the weight is d. Then the weighted calculation formula is: weighted value = A×a+B×b+C×c+D×d.

[0079] By strictly following such mathematical operation rules, each characteristic value is multiplied by the corresponding weight and then added up, so as to accurately calculate the weighted value. This weighted value comprehensively considers each characteristic and its importance, and can more comprehensively reflect the overall status of the driver.

[0080] Step A5: taking the emotional state corresponding to the weighted value as the emotional state of the driver.

[0081] Specifically, when the weighted value is in a certain interval, it corresponds to the emotional state of "relaxed and happy"; when it is in another interval, it may correspond to the emotional state of "tension and anxiety", etc.

[0082] By comparing the calculated weighted value with these pre-set intervals, we can find out which corresponding emotional state interval the weighted value falls within, and then determine the emotional state corresponding to this interval as the driver's current emotional state, thereby achieving the purpose of accurately judging the driver's emotional state through weighted values.

[0083] Step A6, calculating the driving safety factor corresponding to the hand sensing characteristics and the dangerous behavior characteristics, and determining the driver's driving condition based on the driving safety system.

[0084] Specifically, according to the factors involved in the hand sensing characteristics, such as the operational stability and rationality of force control, as well as the number of illegal operations and the frequency of dangerous driving behaviors contained in the dangerous behavior characteristics, quantitative processing is performed according to a certain mathematical algorithm. For example, the more stable the hand operation and the fewer dangerous behaviors, the higher the corresponding value; vice versa. By combining the quantitative results of these factors, according to a specific calculation formula (for example, safety factor = operational stability score × a certain coefficient + dangerous behavior suppression score × another coefficient, etc.), the driving safety factor is calculated.

[0085] Next, the driver's driving condition is determined based on this driving safety factor. Different safety factor ranges are generally set to correspond to different driving condition descriptions. For example, when the driving safety factor is in a higher range (such as 0.8-1), the driver's driving condition is judged to be "good", indicating that the operation is standardized and the risk is low; when the coefficient is in a medium range (such as 0.4-0.8), the driving condition is determined to be "general", which means that there may be some unstable operations or occasional dangerous behaviors; and when the coefficient is in a lower range (such as 0-0.4), the driving condition is judged to be "poor", indicating that the driver's driving has a major safety hazard and requires timely reminders or intervention measures to complete the accurate judgment of the driver's driving condition.

[0086] Step S104, controlling the vehicle to automatically perform a braking operation according to the emotional state and / or driving conditions.

[0087] In the embodiment of the present application, controlling the vehicle to automatically perform a braking operation according to the emotional state and / or driving conditions includes the following steps B1-B3:

[0088] Step B1, if the emotional state is out of control, and / or the driving condition is dangerous driving, then the current driving environment of the vehicle is obtained.

[0089] First, a corresponding state judgment mechanism will be set up in the system, which will monitor and compare the driver's emotional state and the judgment results of the driving situation in real time. When the driver's emotional state determined by the previous steps is out of control, such as extreme anger, panic and other emotions have obviously affected normal driving operations, or the driving situation is determined to be dangerous driving, such as frequent sudden braking, sudden acceleration, random lane changes and other dangerous behaviors, the operation of obtaining the current driving environment of the vehicle is triggered.

[0090] At this time, various environmental perception devices equipped on the vehicle start to work, such as using the on-board camera to capture the road, traffic signs, other vehicles and pedestrians around the vehicle; using sensors such as millimeter-wave radar and lidar to accurately detect the distance, relative speed and direction between the vehicle and surrounding objects; at the same time, the on-board communication unit (OBU) will also receive relevant information about road conditions, traffic control, etc. sent by the roadside unit (RSU) or other intelligent transportation systems. Through the collection and integration of these multi-source information, the current driving environment of the vehicle can be fully obtained, providing basic data support for the subsequent planning of safe parking routes.

[0091] Step B2, planning a safe parking route for the vehicle based on the driving environment.

[0092] Specifically, planning a safe parking route for a vehicle based on a driving environment includes: obtaining driving data of other vehicles in the driving environment. Planning driving trajectories of other vehicles based on the driving data, and detecting safe parking areas in the driving environment. Planning a safe parking route for the vehicle based on the driving trajectory and the safe parking area.

[0093] The sensors and communication units on the vehicle continuously collect relevant information about other vehicles around it. These driving data cover other vehicles' real-time speed, driving direction, acceleration, position coordinates, etc. For example, the on-board radar can accurately measure the distance to the front, rear and side vehicles and the change in relative speed. The camera, combined with image recognition technology, can determine the state of the turn signals of other vehicles to infer their turning intentions, etc. Through these methods, detailed driving data of other vehicles around can be obtained in all directions.

[0094] The collected driving data of other vehicles is input into the trajectory prediction module, which usually uses a trajectory prediction algorithm based on machine learning or dynamic models. For example, based on the current speed, acceleration, and historical driving path of other vehicles, combined with traffic rules and common driving behavior patterns, the possible driving trajectories of other vehicles in the next period of time are predicted through algorithm analysis. For example, if a vehicle is driving in a straight line at a constant speed and no turn signal is on, it is predicted that it will most likely continue to maintain the current direction and speed; if the vehicle turns on the turn signal and the speed is reduced, it is predicted that it is about to turn in the corresponding direction, etc., so as to draw the approximate driving trajectory of other vehicles.

[0095] The on-board camera is used to perform image recognition on the areas on both sides of the road and in front of the road to determine whether there are areas that meet the parking conditions, such as emergency parking strips on the roadside, empty parking lot entrances, and roadside spaces that are not prohibited and have enough space for vehicles to park safely. At the same time, combined with map data and real-time traffic information, areas that are under construction, have obstacles, or where parking is not allowed due to traffic control are excluded. In addition, the dynamic relationship between the parking area and other vehicles and pedestrians must be considered to ensure that there is no risk of collision with other traffic participants during parking, so as to accurately detect areas where vehicles can be safely parked.

[0096] After obtaining the driving trajectories of other vehicles and determining the safe parking area, the path planning module comes into play. This module will comprehensively consider many factors such as the current vehicle's position, speed, the dynamic situation of the surrounding traffic flow, and the location of each safe parking area, and use path planning algorithms (such as A* algorithms, etc.) to plan an optimal driving route from the current vehicle position to a suitable safe parking area while avoiding the predicted driving trajectories of other vehicles and ensuring no conflict with other traffic participants. This route is the vehicle's safe parking route, which must ensure that the vehicle can drive smoothly and safely to the parking point.

[0097] Step B3, controlling the vehicle to travel along the safe parking route, and controlling the vehicle to perform a braking operation when reaching the end of the safe parking route.

[0098] Specifically, after receiving the planned safe parking route information, the vehicle's automatic driving control system will adjust the vehicle's driving direction and speed by controlling the vehicle's steering system, power system, and brake system according to the various path nodes in the route (such as turning points, lane change points, etc.) and the corresponding speed requirements. For example, when turning, the steering wheel rotation angle is accurately controlled to make the vehicle turn accurately according to the planned route; in sections where deceleration is required, the throttle and brake are reasonably adjusted to allow the vehicle to decelerate smoothly and ensure that the vehicle always moves along the safe parking route.

[0099] During the driving process, the system will monitor the distance between the vehicle and the end of the safe parking route in real time. When the positioning system and other means determine that the vehicle is about to reach the end, the automatic driving control system will issue a command to drive the braking system to increase the braking force, so that the vehicle will gradually slow down until it stops completely in the safe parking area. At the same time, during the braking process, it will continue to pay attention to changes in the environment around the vehicle. If there is an emergency (such as other vehicles suddenly approaching, etc.), the braking force and parking strategy will be adjusted in time to ensure the safety of parking operations and avoid accidents such as collisions.

[0100] This application can fully understand the driver's status by detecting the driver's driving emotion data and operation data. The extracted key emotional features, such as the driver's anxiety or fatigue detected from sighs and changes in tone, are combined with driving operation characteristics, such as tense control reflected by subtle shaking of the steering wheel, to determine the driving scenario, so that the automatic braking decision fits the reality, reduce misjudgment, and improve driving safety. In solving traditional problems, the method of simply relying on external sensors to sense the environment to trigger the brakes is abandoned, avoiding the drawbacks of sensor failure or misjudgment due to rain, snow, strong light, etc. At the same time, the recognition results will not be affected by the noise and accent in the car, but focus on the driver's own performance. The automatic braking function can be triggered accurately to ensure the stability and safety of driving.

[0101] As an example, Figure 2 As shown, suppose the driver is driving on the road, and suddenly a car drives out quickly from the fork road next to him and almost collides with his car.

[0102] Sound emotional feature (sensor): The driver let out a short cry due to being frightened, and the sound sensor captured this feature.

[0103] Facial expression features (camera sensor): The camera sensor inside the car detects that the driver's facial expression suddenly becomes terrified, his eyes widen, and his eyebrows rise.

[0104] Hand sensing on the steering wheel (torque sensor): Due to fear, the driver's hands unconsciously clench on the steering wheel, and the torque sensor detects the sudden increase in hand strength.

[0105] Gesture signature (camera sensor): The camera also captures a brief nervous shake of the driver's hands on the steering wheel.

[0106] Voice emotion recognition: Based on the sound characteristics transmitted by the sound sensor, the algorithm determines that the driver is in a state of panic.

[0107] Facial emotion recognition: By analyzing the facial expressions captured by the camera, the algorithm also recognizes that the driver is losing control of his emotions (panic).

[0108] Steering wheel recognition: Data from the torque sensor shows that the driver's hands are tightly gripping the steering wheel.

[0109] Gesture recognition: The shaking gesture features captured by the camera are judged by the algorithm as the driver being in a nervous and panic state.

[0110] Based on all the above judgments, the system determines that the driver's emotions are out of control. Therefore, the system triggers the automatic braking function and turns on the double flash lights to send warning signals to surrounding vehicles to avoid possible collision accidents.

[0111] In an embodiment of the present application, the method further includes: detecting environmental data in the vehicle while the vehicle is driving along the safe parking route. Analyzing the degree of emotional impact of the environmental data on the driver. If the emotional impact is greater than a preset value, controlling the target component of the vehicle to adjust the environment according to a preset strategy.

[0112] When the vehicle starts driving along the planned safe parking route, various environmental sensors in the vehicle begin to work to collect relevant environmental data.

[0113] ① Temperature sensors are distributed in different locations in the car, such as near the driver's seat and the rear seat area, to monitor the temperature of various parts of the car in real time. They can accurately sense temperature changes and form corresponding temperature data records.

[0114] ②The humidity sensor is also installed at a suitable location in the car to detect the humidity level of the air in the car, measure the amount of water contained in the air, and obtain humidity-related data information.

[0115] ③Light sensors are generally located near the windows or on the top of the car, and are used to detect the intensity of light inside the car. Whether it is natural light entering the car or the light status when the lights are turned on, they can be accurately sensed and converted into corresponding data.

[0116] ④ Noise sensors can be installed around the car to collect noise information inside the car, such as tire noise, wind noise generated during the vehicle's driving, and the sound made by the operation of equipment inside the car, and quantify the decibel size of the noise and other data.

[0117] ⑤ The air quality sensor is responsible for detecting the content of various pollutants in the air inside the car, such as the concentration of carbon monoxide, carbon dioxide, volatile organic compounds (VOCs), etc., so as to reflect the quality of the air inside the car and generate corresponding air quality data.

[0118] ⑥ Through the coordinated work of these sensors, the environmental data inside the vehicle can be obtained in an all-round and real-time manner, providing a basic basis for subsequent analysis of the impact on the driver's emotions.

[0119] The collected in-vehicle environment data will be transmitted to a dedicated data analysis module, which uses an analysis model based on psychology, physiology and a large amount of experimental data to carry out its work.

[0120] ① Temperature: Refer to existing research results. For example, when the temperature in the car is too high, the human body will feel hot and stuffy, which can easily make the driver feel irritable; while when the temperature is too low, people may feel cold and uncomfortable, making it difficult to concentrate and become depressed. The analysis module will compare the currently detected temperature value with the corresponding emotional impact range to determine the degree of its impact on the driver's emotions.

[0121] ② Humidity: If the humidity in the car is too high, the air will appear dull and depressing, making people feel sleepy and lazy, affecting the driver's mental state and mood; if the humidity is too low, the air will be dry, which may cause dry skin, sore throat and other problems, leading to emotional unhappiness for the driver. By analyzing the range of humidity data and combining it with the established humidity-emotional impact relationship model, the degree of impact on emotions can be determined.

[0122] ③ Light: Too strong light may dazzle the eyes, causing visual fatigue and even irritability; too dark light will affect the vision and make the driver feel nervous and insecure. The data collected by the light sensor will be used by the analysis module to determine whether the current light intensity is in an appropriate visual and emotional comfort range, so as to measure the degree of impact on emotions.

[0123] ④ Noise: High-decibel noise can disturb the driver's attention, making him or her anxious and irritable, and making it difficult for him or her to maintain a calm state of mind while driving. The analysis module evaluates the impact of noise on the driver's emotions based on the decibel value detected by the noise sensor and the corresponding relationship between different noise levels and the degree of emotional interference.

[0124] ⑤ Air quality: If the air quality in the car is poor and the concentration of pollutants is high, people will feel shortness of breath, dizziness, and headache, which will cause the driver to be negative and sleepy. By analyzing the concentration data of various pollutants detected by the air quality sensor and combining the standard of the impact of air quality on human emotions, the degree of impact on the driver's emotions can be judged.

[0125] By integrating the analysis results of various aspects of environmental data, using specific weight allocation (for example, assigning different weights according to the importance of the impact of different environmental factors on emotions) and comprehensive calculation methods, the numerical value of the impact of the overall environmental data on the driver's emotions is obtained.

[0126] If the degree of emotional impact is greater than the preset value, the target components of the vehicle will be controlled according to the preset strategy to adjust the environment. After obtaining the numerical value of the degree of impact of environmental data on the driver's emotions, it will be compared with the preset value. The preset value is set based on a large number of driving experiments, psychological research and practical application experience, representing a critical degree that can have a significant negative effect on the driver's emotions.

[0127] If the impact is greater than the preset value, it indicates that the current in-car environment has had a serious adverse impact on the driver's emotions and needs to be adjusted and improved. At this time, the system will control the corresponding target components of the vehicle to carry out environmental adjustment according to the preset strategy.

[0128] If the temperature is too high, the car's air-conditioning system will receive instructions to start the cooling mode, and gradually lower the temperature inside the car to a suitable comfort range by adjusting the operating frequency of the compressor, the air volume at the air outlet, and the wind direction. If the temperature is too low, the heating mode will be turned on to heat the air and reasonably distribute it to various areas in the car to increase the overall temperature.

[0129] When the humidity is too high, the dehumidification function of the air conditioner is activated, removing excess moisture in the air through the principle of condensation to reduce the humidity; when the humidity is too low, the humidification device equipped in some vehicles will work, releasing an appropriate amount of water vapor into the air, increasing the air humidity to a comfortable range.

[0130] If the light is too strong, the sunshade inside the car (electric sunshade can be automatically controlled) will automatically adjust its position to block part of the sunlight, and the brightness of the lights inside the car will be dimmed appropriately; when the light is too dark, the lighting system inside the car will automatically increase the brightness, or adjust parameters such as the color temperature of the light to create a comfortable visual environment and improve the driver's emotional state.

[0131] For the problem of noise inside the car, the active noise reduction system equipped in some high-end vehicles will come into play. The noise waves are detected through the microphone inside the car, and then the audio system emits sound waves with the opposite phase to the noise waves. The two cancel each other out, reducing the noise level inside the car and allowing the driver to be in a relatively quiet environment.

[0132] When poor air quality is detected, the on-board air purification system starts up, using technical means such as filter filtration and negative ion generators to absorb and decompose pollutants in the air, improve air quality, allow the driver to breathe more smoothly, and relieve negative emotions.

[0133] Through precise control of these target components and in accordance with preset adjustment strategies, the in-vehicle environment can be improved in a targeted manner, reducing the adverse effects of environmental factors on the driver's emotions, and assisting the driver in maintaining a relatively stable emotional state during the safe parking process.

[0134] In an embodiment of the present application, the method further includes: when a safe parking route cannot be planned in a driving environment, searching whether there is roadside equipment within a preset range where the vehicle is located. If there is a road test device, a help signal is sent to the road test device, and the vehicle is controlled to perform a braking operation. The help signal carries the vehicle identification of the vehicle and the emotional state and / or driving conditions of the driver. The roadside equipment is used to broadcast the vehicle identification according to the help signal so that other vehicles in the driving environment where the vehicle is located can ask for help from the vehicle. Response signals from other vehicles are received, and the vehicle is unlocked based on the response signals.

[0135] Specifically, when a safe parking route cannot be planned in a driving environment, a search is performed to determine whether there is roadside equipment within a preset range where the vehicle is located.

[0136] The vehicle system will pre-set a reasonable search range based on the current driving environment and actual conditions. This range usually takes into account factors such as the vehicle's driving speed, surrounding road conditions, and signal transmission distance. It is generally centered on the vehicle, with a radius ranging from several hundred meters to several thousand meters.

[0137] The vehicle sends a search signal to the surrounding area through its own communication module, such as the V2X communication module in the vehicle networking system. The signal contains a query request for roadside equipment. At the same time, the vehicle will also receive signals sent by other devices in the surrounding environment, and determine whether there is roadside equipment through information such as signal strength and signal characteristics. The vehicle's positioning system will continuously update the vehicle's location information and compare it with the map data within a preset range to find out whether there is a known roadside equipment installation location that matches the vehicle's current location.

[0138] If there is a road test device, a help signal is sent to the roadside device and the vehicle is controlled to perform braking operations. A stable communication connection needs to be established between the vehicle and the roadside device in order to transmit the help signal. Generally, wireless communication technologies such as DSRC (dedicated short-range communication) or C-V2X (cellular vehicle-to-everything) are used. After the vehicle confirms that the communication connection with the roadside device is successful, it will generate a help signal containing detailed information such as vehicle identification, driver emotional state and / or driving conditions.

[0139] The help signal is encoded and packaged according to the established communication protocol, and then sent to the roadside equipment through the established communication connection. At the same time, the vehicle's control system will automatically control the vehicle to perform braking operations according to the current driving status and road conditions, gradually reducing the speed until the vehicle stops completely. During the braking process, the vehicle will monitor the vehicle's speed, acceleration, braking distance and other parameters in real time through various sensors, and adjust the braking force according to these parameters to ensure that the vehicle can stop smoothly and safely.

[0140] After receiving the help signal sent by the vehicle, the roadside equipment will decode and analyze the signal to extract key information such as the vehicle identification. Then, the roadside equipment will use its own broadcast function to broadcast the vehicle identification and brief help information to other vehicles within a certain range through wireless communication. The broadcast range is usually determined by the power of the roadside equipment and the obstruction of the surrounding environment, and can generally cover an area of ​​several hundred meters to several thousand meters.

[0141] While broadcasting the help message, the roadside equipment can also use some special signals or prompts to guide the drivers of other vehicles to pay attention to the help message.

[0142] After sending a distress signal and stopping, the vehicle will continue to listen to response signals sent by other vehicles around it. When other vehicles receive the distress message broadcast by the roadside equipment and decide to provide assistance, these vehicles will send a response signal to the vehicle in need of help. After receiving the response signal, the vehicle will verify the signal to ensure that the source of the signal is legal, valid, and matches the distress message. The verification process may include operations such as encryption and decryption of the signal, identity authentication, and information integrity check. After verifying that the response signal is correct, the vehicle's control system will send an unlock command to the vehicle's door lock system according to the preset program to control the vehicle to unlock so that drivers of other vehicles or rescue personnel can enter the vehicle for assistance or rescue.

[0143] In this embodiment, a brake control device based on driver behavior is also provided, which is used to implement the above-mentioned embodiments and preferred implementation modes, and the descriptions that have been made will not be repeated. As used below, the term "module" can be a combination of software and / or hardware that implements a predetermined function. Although the devices described in the following embodiments are preferably implemented in software, the implementation of hardware, or a combination of software and hardware, is also possible and conceivable.

[0144] This embodiment provides a brake control device based on driver behavior, such as Figure 3 As shown, including:

[0145] The detection module 301 is used to detect the driving emotion data and driving operation data of the driver during the driving process of the vehicle;

[0146] An extraction module 302, configured to extract key emotion features based on the driving emotion data, and to extract key operation features based on the driving operation data;

[0147] An analysis module 304 is used to analyze key emotional features and key operational features to obtain the driver's emotional state and driving conditions;

[0148] The control module 304 is used to control the vehicle to automatically perform braking operations according to the emotional state and / or driving conditions.

[0149] In an embodiment of the present application, the detection module 301 is used to detect the sound data of the driver during the driving of the vehicle, and collect the facial image data of the driver during the driving of the vehicle; detect the sensing data of the driver acting on the steering wheel in the vehicle, and detect the driving behavior video of the driver, and extract driving behavior data from the driving behavior video; construct driving emotion data based on the sound data and facial image data, and construct driving operation data based on the sensing data and driving behavior data.

[0150] In an embodiment of the present application, an analysis module is used to extract voice emotion features and facial expression features from key emotion features; extract hand sensing features and dangerous behavior features from key operation features; obtain weights corresponding to voice emotion features, facial expression features, hand sensing features and dangerous behavior features, respectively; perform weighted calculation based on the weights corresponding to voice emotion features, facial expression features, hand sensing features and dangerous behavior features, respectively, and voice emotion features, facial expression features, hand sensing features and dangerous behavior features, to obtain a weighted value; use the emotional state corresponding to the weighted value as the emotional state of the driver; calculate the driving safety factor corresponding to the hand sensing features and the dangerous behavior features, and determine the driver's driving condition based on the driving safety system.

[0151] In an embodiment of the present application, the control module 304 is used to obtain the current driving environment of the vehicle if the emotional state is out of control and / or the driving condition is dangerous driving; plan a safe parking route for the vehicle based on the driving environment; control the vehicle to drive along the safe parking route, and control the vehicle to perform a braking operation when reaching the end of the safe parking route.

[0152] In an embodiment of the present application, the control module 304 is used to obtain driving data of other vehicles in the driving environment; plan the driving trajectory of other vehicles based on the driving data, and detect the safe parking area in the driving environment; and plan the safe parking route of the vehicle based on the driving trajectory and the safe parking area.

[0153] In an embodiment of the present application, the device also includes: an adjustment module, which is used to detect environmental data in the vehicle while the vehicle is driving on a safe parking route; analyze the degree of impact of the environmental data on the driver's emotions; if the degree of emotional impact is greater than a preset value, control the target components of the vehicle for environmental adjustment according to a preset strategy.

[0154] In an embodiment of the present application, the device also includes: a communication module, which is used to search for roadside equipment within a preset range where the vehicle is located when a safe parking route cannot be planned in the driving environment; if there is road test equipment, a help signal is sent to the road test equipment, and the vehicle is controlled to perform a braking operation; wherein the help signal carries the vehicle identification of the vehicle and the emotional state and / or driving conditions of the driver, and the roadside equipment is used to broadcast the vehicle identification according to the help signal so that other vehicles in the driving environment where the vehicle is located can ask for help from the vehicle; receive response signals from other vehicles, and control the vehicle to unlock based on the response signal.

[0155] See also Figure 4 , Figure 4 is a schematic diagram of the structure of a computer device provided by an optional embodiment of the present invention, such as Figure 4 As shown, the computer device includes: one or more processors 10, a memory 20, and interfaces for connecting various components, including high-speed interfaces and low-speed interfaces. Various components are connected to each other using different buses for communication, and can be installed on a common mainboard or installed in other ways as needed. The processor can process the instructions executed in the computer device, including instructions stored in or on the memory to display the graphical information of the GUI on an external input / output device (such as, a display device coupled to the interface). In some optional embodiments, if necessary, multiple processors and / or multiple buses can be used together with multiple memories and multiple memories. Similarly, multiple computer devices can be connected, and each device provides some necessary operations (for example, as a server array, a group of blade servers, or a multi-processor system). Figure 4 A processor 10 is taken as an example.

[0156] The processor 10 may be a central processing unit, a network processor or a combination thereof. The processor 10 may further include a hardware chip. The hardware chip may be a dedicated integrated circuit, a programmable logic device or a combination thereof. The programmable logic device may be a complex programmable logic device, a field programmable gate array, a general purpose array logic or any combination thereof.

[0157] The memory 20 stores instructions executable by at least one processor 10, so that the at least one processor 10 executes the method shown in the above embodiment.

[0158] The memory 20 may include a program storage area and a data storage area, wherein the program storage area may store an operating system, an application required for at least one function; the data storage area may store data created by the use of a computer device based on the presentation of a small program landing page, etc. In addition, the memory 20 may include a high-speed random access memory, and may also include a non-transient memory, such as at least one disk storage device, a flash memory device, or other non-transient solid-state storage device. In some optional embodiments, the memory 20 may optionally include a memory remotely arranged relative to the processor 10, and these remote memories may be connected to the computer 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.

[0159] The memory 20 may include a volatile memory, such as a random access memory; the memory may also include a non-volatile memory, such as a flash memory, a hard disk or a solid state drive; the memory 20 may also include a combination of the above types of memory.

[0160] The computer device further comprises a communication interface 30 for the computer device to communicate with other devices or a communication network.

[0161] The embodiment of the present invention also provides a computer-readable storage medium. The method according to the embodiment of the present invention can be implemented in hardware, firmware, or can be implemented as a computer code that can be recorded in a storage medium, or can be implemented as a computer code that is originally stored in a remote storage medium or a non-temporary machine-readable storage medium and will be stored in a local storage medium through a network download, so that the method described herein can be stored in such software processing on a storage medium using a general-purpose computer, a dedicated processor, or programmable or dedicated hardware. Among them, the storage medium can be a magnetic disk, an optical disk, a read-only storage memory, a random access memory, a flash memory, a hard disk or a solid-state hard disk, etc.; further, the storage medium can also include a combination of the above types of memories. It can be understood that a computer, a processor, a microprocessor controller, or programmable hardware includes a storage component that can store or receive software or computer code. When the software or computer code is accessed and executed by a computer, a processor, or hardware, the method shown in the above embodiment is implemented.

[0162] Although the embodiments of the present invention have been described in conjunction with the accompanying drawings, those skilled in the art may make various modifications and variations without departing from the spirit and scope of the present invention, and such modifications and variations are all within the scope defined by the appended claims.

Claims

1. A brake control method based on driver behavior, characterized in that: The method comprises: Detect the driver's driving emotion data and driving operation data during vehicle driving; extracting key emotion features based on the driving emotion data, and extracting key operation features based on the driving operation data; Analyzing the key emotional features and the key operating features to obtain the driver's emotional state and driving conditions; The vehicle is controlled to automatically perform a braking operation according to the emotional state and / or driving situation.

2. The method according to claim 1, characterized in that The detection of the driving emotion data and driving operation data of the driver during the driving process of the vehicle includes: Detecting the voice data of the driver during the driving of the vehicle, and collecting the facial image data of the driver during the driving of the vehicle; Detecting sensing data of the driver acting on a steering wheel in a vehicle, detecting a driving behavior video of the driver, and extracting driving behavior data from the driving behavior video; The driving emotion data is constructed based on the voice data and the facial image data, and the driving operation data is constructed based on the sensing data and the driving behavior data.

3. The method according to claim 1, characterized in that The analyzing the key emotional features and the key operating features to obtain the driver's emotional state and driving conditions includes: Extracting voice emotion features and facial expression features from the key emotion features; Extracting hand sensing features and dangerous behavior features from the key operation features; Obtaining weights corresponding to the voice emotion feature, the facial expression feature, the hand sensing feature, and the dangerous behavior feature, respectively; Based on the weights corresponding to the voice emotion feature, the facial expression feature, the hand sensing feature and the dangerous behavior feature, and weighted calculation of the voice emotion feature, the facial expression feature, the hand sensing feature and the dangerous behavior feature, a weighted value is obtained; Using the emotional state corresponding to the weighted value as the emotional state of the driver; The driving safety factors corresponding to the hand sensing features and the dangerous behavior features are calculated, and the driving condition of the driver is determined based on the driving safety system.

4. The method according to claim 1, characterized in that: The step of controlling the vehicle to automatically perform a braking operation according to the emotional state and / or driving condition includes: If the emotional state is a loss of control state, and / or the driving condition is dangerous driving, then obtaining the current driving environment of the vehicle; planning a safe parking route for the vehicle based on the driving environment; The vehicle is controlled to travel along the safe parking route, and the vehicle is controlled to perform a braking operation when reaching an end point of the safe parking route.

5. The method according to claim 4, characterized in that The planning of a safe parking route for the vehicle based on the driving environment includes: Acquiring driving data of other vehicles in the driving environment; planning a driving trajectory of the other vehicles based on the driving data, and detecting a safe parking area in the driving environment; A safe parking route for the vehicle is planned based on the driving trajectory and the safe parking area.

6. The method according to claim 4, characterized in that The method further comprises: During the process of the vehicle safely driving the safe parking route, detecting environmental data inside the vehicle; Analyzing the degree of influence of the environmental data on the driver's emotions; If the degree of emotional impact is greater than a preset value, the target component of the vehicle is controlled to perform environmental adjustment according to a preset strategy.

7. The method according to claim 4, characterized in that The method further comprises: When a safe parking route cannot be planned in the driving environment, searching whether there is roadside equipment within a preset range where the vehicle is located; If there is a road test device, a help signal is sent to the road test device, and the vehicle is controlled to perform a braking operation; wherein the help signal carries the vehicle identification of the vehicle and the emotional state and / or driving condition of the driver, and the roadside device is used to broadcast the vehicle identification according to the help signal, so that other vehicles in the driving environment where the vehicle is located can ask for help from the vehicle; A response signal is received from the other vehicle, and the vehicle is controlled to be unlocked based on the response signal.

8. A brake control device based on driver behavior, characterized in that: The device comprises: A detection module, used to detect the driver's driving emotion data and driving operation data during vehicle driving; An extraction module, configured to extract key emotion features based on the driving emotion data, and to extract key operation features based on the driving operation data; An analysis module, used for analyzing the key emotional features and the key operating features to obtain the emotional state and driving conditions of the driver; The control module is used to control the vehicle to automatically perform braking operations according to the emotional state and / or driving conditions.

9. A computer device, characterized in that: include: A memory and a processor, wherein the memory and the processor are communicatively connected to each other, the memory stores computer instructions, and the processor executes the method according to any one of claims 1 to 7 by executing the computer instructions.

10. A computer-readable storage medium, characterized in that: The computer-readable storage 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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