A brake control method, device, vehicle and storage medium

Abstract

The application discloses a kind of brake control method, device, vehicle and storage medium.The method comprises: obtaining current brain wave data and radar point cloud data;According to current brain wave data, determine brain wave brake control information, and according to radar point cloud data, determine radar brake control information;According to brain wave brake control information and radar brake control information, determine vehicle brake control information, and control vehicle braking based on vehicle brake control information;Wherein, current brain wave data includes emotional brain wave information and control brain wave information.The technical scheme of the embodiment of the application limits control brain wave information by emotional brain wave information, so that the brain wave brake control information determined is more accurate, and the judgment of vehicle brake demand is realized by double standard, which improves the accuracy of vehicle brake information determination, and further improves the safety of vehicle driving.

Description

Technical Field

[0001] This invention relates to the field of vehicle control technology, and in particular to a braking control method, device, vehicle, and storage medium. Background Technology

[0002] With societal progress, the number of small cars on the road is increasing year by year, and the possibility of traffic accidents is also increasing. In order to reduce the possibility of collisions during driving, various control methods have been introduced into existing vehicles, such as voice, gestures, head movements and EEG signals, which combine multiple interactive technologies to increase the control of the vehicle.

[0003] A preliminary solution has been developed to control cars using brainwave signals, achieving "thought control" of driving behavior. Similar to the working principle of brain-computer interface systems, drivers wear special devices that capture brainwave signals, and the analysis of these signals determines the driver's control commands for the vehicle.

[0004] However, when a vehicle encounters a braking situation during driving, the situation is often urgent and unpredictable. Existing methods for vehicle braking control using EEG signals suffer from problems such as poor signal-to-noise ratio, low accuracy, and long delay, making it difficult to meet the needs of short-term accurate braking. Summary of the Invention

[0005] This invention provides a braking control method, device, vehicle, and storage medium that combines different types of information from electroencephalogram (EEG) data with information collected during vehicle operation to determine vehicle braking control information. This improves the accuracy and speed of vehicle braking information determination, thereby enhancing vehicle driving safety.

[0006] In a first aspect, embodiments of the present invention provide a braking control method, comprising:

[0007] Acquire current EEG data and radar point cloud data;

[0008] Brainwave braking control information is determined based on current brainwave data, and radar braking control information is determined based on radar point cloud data;

[0009] The vehicle braking control information is determined based on brainwave braking control information and radar braking control information, and the vehicle braking is controlled based on the vehicle braking control information.

[0010] The current EEG data includes emotional EEG information and control EEG information.

[0011] Secondly, embodiments of the present invention also provide a braking control device, comprising:

[0012] The data acquisition module is used to acquire current EEG data and radar point cloud data;

[0013] The control information determination module is used to determine brainwave braking control information based on the current brainwave data and to determine radar braking control information based on radar point cloud data.

[0014] The braking control module is used to determine vehicle braking control information based on EEG braking control information and radar braking control information, and to control vehicle braking based on the vehicle braking control information.

[0015] The current EEG data includes emotional EEG information and control EEG information.

[0016] Thirdly, embodiments of the present invention also provide a vehicle, the vehicle comprising:

[0017] Brain-computer interface is used to acquire current brainwave data;

[0018] LiDAR is used to acquire radar point cloud data;

[0019] One or more controllers;

[0020] Storage device for storing one or more programs;

[0021] When one or more programs are executed by one or more controllers, the one or more controllers implement the braking control method of any embodiment of the present invention.

[0022] Fourthly, embodiments of the present invention also provide a computer-readable storage medium storing computer instructions that, when executed by a processor, implement the braking control method of any embodiment of the present invention.

[0023] This invention provides a braking control method, device, vehicle, and storage medium. The method involves acquiring current brainwave data and radar point cloud data; determining brainwave braking control information based on the current brainwave data and radar braking control information based on the radar point cloud data; determining vehicle braking control information based on the brainwave and radar braking control information; and controlling vehicle braking based on the vehicle braking control information. The current brainwave data includes emotional and control-related brainwave information. By employing this technical solution, brainwave braking control information is determined based on emotional and control-related brainwave information in the current brainwave data. The emotional brainwave information restricts the control-related brainwave information, making the determined brainwave braking control information more accurate. Simultaneously, the radar braking control information determined from the radar point cloud data is combined with the brainwave braking control information to jointly determine the vehicle braking control information. This dual-standard approach improves the accuracy of vehicle braking information determination, thereby enhancing driving safety.

[0024] It should be understood that the description in this section is not intended to identify key or essential features of the embodiments of the present invention, nor is it intended to limit the scope of the invention. Other features of the invention will become readily apparent from the following description. Attached Figure Description

[0025] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0026] Figure 1 A flowchart of a braking control method provided in Embodiment 1 of the present invention;

[0027] Figure 2 A flowchart of a braking control method provided in Embodiment 2 of the present invention;

[0028] Figure 3 This is a schematic diagram of a process for determining the current attention value and the current relaxation value based on current EEG data, provided in Embodiment 2 of the present invention.

[0029] Figure 4 This is a schematic diagram of a braking control device provided in Embodiment 3 of the present invention;

[0030] Figure 5 This is a structural schematic diagram of a vehicle provided in Embodiment 4 of the present invention. Detailed Implementation

[0031] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.

[0032] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this invention are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of the invention described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover a non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0033] Example 1

[0034] Figure 1 The flowchart below shows a braking control method provided in Embodiment 1 of the present invention. This embodiment of the present invention is applicable to situations where braking control of a vehicle is performed during vehicle driving. The method can be executed by a braking control device, which can be implemented by software and / or hardware. The braking control device can be configured on a computer device, such as an on-board computer in the vehicle. This embodiment of the present invention does not impose any limitations on this.

[0035] like Figure 1 As shown, the braking control method provided in Embodiment 1 of the present invention specifically includes the following steps:

[0036] S101. Obtain current EEG data and radar point cloud data.

[0037] The current EEG data includes emotional EEG information and control EEG information.

[0038] In this embodiment, the current EEG data can be specifically understood as the driver's EEG data information transmitted from the brain-computer interface to the vehicle's computer at the current moment, collected by EEG electrodes. The radar point cloud data can be specifically understood as the set of radar point data generated by the lidar installed on the vehicle to measure distances around the vehicle. Emotional EEG information can be specifically understood as information contained in the current EEG data that can indicate the human emotional state corresponding to the EEG. Control-related EEG information can be specifically understood as information contained in the current EEG data that can indicate the human's need to perform external control actions corresponding to the EEG.

[0039] Specifically, before the vehicle enters driving mode, a headgear with EEG electrodes is connected to the vehicle's onboard computer via a brain-computer interface. After the connection is established, the driver wears the headgear while driving, and the driver's brainwaves acquired at that moment are identified as the current EEG data. During driving, one or more lidar sensors installed on the vehicle simultaneously collect radar data to determine the distance between the vehicle and surrounding obstacles, thereby obtaining information about the vehicle's surrounding environment. The set of radar point data collected by the lidar at the current driving moment is identified as radar point cloud data.

[0040] S102. Determine brainwave braking control information based on current brainwave data, and determine radar braking control information based on radar point cloud data.

[0041] In this embodiment, brainwave braking control information can be specifically understood as information determined based on the driver's brainwaves, indicating that the driver possesses the information to control the vehicle's braking. Radar braking control information can be specifically understood as information determined based on radar point cloud information, indicating that the vehicle's surrounding environment requires braking control.

[0042] Specifically, the driver's emotional state is determined based on emotional EEG information from the current EEG data, and the driver's control needs are determined based on control-related EEG information from the same EEG data. Combining these control needs with the emotional state determines whether the driver currently requires braking control of the vehicle, thereby generating corresponding EEG braking control information. Simultaneously, radar point cloud data can be used to determine the vehicle's surrounding environment information at the current moment, and based on this information, it can be determined whether the vehicle currently requires braking control, generating radar braking control information corresponding to the radar point cloud data.

[0043] In this embodiment of the invention, the determination of brainwave braking control information is completed by combining emotional brainwave information and control brainwave information, which improves the accuracy of determining brainwave braking control information and better reflects the driver's driving will.

[0044] S103. Determine vehicle braking control information based on EEG braking control information and radar braking control information, and control vehicle braking based on vehicle braking control information.

[0045] In this embodiment, vehicle braking control information can be specifically understood as control commands that can be directly used to control the braking of the vehicle.

[0046] Specifically, when both brainwave braking control information and radar braking control information exist in the vehicle's computer, the braking control requirements contained in the two types of braking control information are combined to determine whether braking control of the vehicle is required at the current moment. If it is determined that braking control of the vehicle is required, vehicle braking control information is generated, and braking control is performed on the software and hardware in the vehicle used for braking based on the vehicle braking control information to control the vehicle to achieve braking.

[0047] The technical solution of this embodiment acquires current EEG data and radar point cloud data; determines EEG braking control information based on the current EEG data and radar braking control information based on the radar point cloud data; determines vehicle braking control information based on the EEG braking control information and radar braking control information, and controls vehicle braking based on the vehicle braking control information; wherein, the current EEG data includes emotional EEG information and control EEG information. By adopting the above technical solution, EEG braking control information is determined based on emotional and control EEG information in the current EEG data. The control EEG information is restricted by the emotional EEG information, making the determined EEG braking control information more accurate. At the same time, the radar braking control information determined by the radar point cloud data is combined with the EEG braking control information to jointly determine the vehicle braking control information. This dual standard is used to judge the vehicle braking demand, improving the accuracy of vehicle braking information determination and thus improving vehicle driving safety.

[0048] Example 2

[0049] Figure 2This is a flowchart of a braking control method provided in Embodiment 2 of the present invention. The technical solution of this embodiment further optimizes the above-mentioned optional technical solutions. By comparing emotional EEG information with a preset set of emotional EEG information, the corresponding emotional type is determined. Then, based on different emotional types and control-type EEG information of different control types, compliant EEG braking control information that meets the driver's braking control needs is determined. Vehicle control braking information is only generated to brake the vehicle when the EEG braking control information is compliant and both compliant EEG braking control information and radar braking control information exist simultaneously. Furthermore, when the EEG braking control information is determined to be non-compliant based on the current EEG data, braking control of the vehicle is achieved solely through radar braking control information, ensuring that the vehicle can still perform correct braking control even when the driver's mental state is poor, thus improving vehicle driving safety.

[0050] like Figure 2 As shown, the braking control method provided in Embodiment 2 of the present invention specifically includes the following steps:

[0051] S201. Obtain current EEG data and radar point cloud data.

[0052] S202. Determine the current attention value and current relaxation value based on the current EEG data.

[0053] In this embodiment, the current attention value can be specifically understood as a numerical value used to measure the driver's level of concentration at the current moment. The current relaxation value can be specifically understood as a numerical value used to measure the driver's level of relaxation at the current moment.

[0054] Specifically, since EEG data contains rhythmic waves of various frequency ranges, the relaxation state and concentration of a person can be judged based on the proportion of each rhythmic wave in the EEG data. Furthermore, the power spectrum of different types of rhythmic wave signals in the current EEG data can be analyzed to obtain the driver's current attention value and current relaxation value at the current moment.

[0055] Optional, Figure 3 This is a schematic diagram of a process for determining the current attention value and the current relaxation value based on current electroencephalogram (EEG) data, as provided in Embodiment 2 of the present invention. Figure 3 As shown, the specific steps include the following:

[0056] S2021. Determine the alpha rhythm power spectrum, beta rhythm power spectrum, and theta rhythm power spectrum based on the current EEG data.

[0057] In this embodiment, alpha rhythm waves can be specifically understood as brain waves with a frequency of 8-12Hz in EEG data, used to represent a relaxed, stress-free state; beta rhythm waves can be specifically understood as brain waves with a frequency of 12-16Hz in EEG data, used to represent an alert, tense state; and theta rhythm waves can be specifically understood as brain waves with a frequency of 4-8Hz in EEG data, used to represent a state of reduced or even no sensation of bodily states.

[0058] Specifically, the current EEG data is separated to obtain the power spectrum of the alpha rhythm wave corresponding to the alpha rhythm wave, the power spectrum of the beta rhythm wave corresponding to the beta rhythm wave, and the power spectrum of the theta rhythm wave corresponding to the theta rhythm wave.

[0059] Furthermore, the characteristics of the various rhythmic components of EEG show that the prefrontal cortex rhythmic waves are most prominent when the body is under mental stress; while when the body is fatigued or drowsy, the β rhythmic waves are dominant in the temporal lobe. Since the intensity of EEG signals is extremely weak at the microvolt level and easily affected by 50Hz AC power and polarization levels below 0.5Hz, to ensure the accuracy of the results, this embodiment of the invention performs a secondary search on the acquired current EEG data. FIR digital filtering is used to filter out signals from 1-40Hz, and a moving window midpoint filtering method is used to correct baseline drift. Wavelet coefficient thresholding is used to remove artifacts such as electrooculography (EOG) and electromyography (EMG) in the signal, thereby extracting the β rhythmic waves in the prefrontal cortex and the θ rhythmic waves in the temporal lobe.

[0060] S2022. Determine the sum of the α-mode power spectrum and the β-mode power spectrum, and determine the ratio of the sum to the θ-mode power spectrum as the EEG power spectrum ratio.

[0061] For example, the EEG power spectrum ratio can be determined by the following formula:

[0062] R = (α + β) / θ;

[0063] Where α represents the power spectrum of the alpha rhythm, β represents the power spectrum of the beta rhythm, θ represents the power spectrum of the theta rhythm, and R represents the ratio of the brainwave power spectrum. R can be used as an attention feature value to characterize human attention.

[0064] S2023. Determine the current attention value based on the ratio of the brainwave power spectrum and the current relaxation value based on the alpha rhythm wave power spectrum.

[0065] Specifically, the ratio of the brainwave power spectrum is substituted into the pre-constructed formula for determining the attention value, and the attention value corresponding to the ratio of the brainwave power spectrum of the current brainwave data is determined as the current attention value; the alpha rhythm power spectrum is substituted into the pre-constructed formula for determining the relaxation value, and the current relaxation value corresponding to the current brainwave data is determined.

[0066] For example, the formula for determining the attention value can be:

[0067] Attention=100-(R-0.95)*100;

[0068] Where Attention is the attention score and R is the EEG power spectrum ratio. An attention score between 60 and 80 indicates high concentration; between 40 and 60 indicates relatively high concentration; between 20 and 40 indicates relatively scattered attention; and below 20 indicates severely scattered attention.

[0069] For example, the formula for determining the degree of relaxation can be:

[0070] Meditation = (α / 10) * 100;

[0071] Meditation represents the relaxation level, and α represents the alpha rhythm power spectrum. Generally, a relaxation index of 40-60 indicates a high level of relaxation, while 60-80 indicates a very high level of relaxation and a state of deep relaxation.

[0072] S203. Determine whether the current attention value is less than the preset attention threshold and the current relaxation value is greater than the preset relaxation threshold. If yes, proceed to step S204; otherwise, proceed to step S205.

[0073] In this embodiment, the preset attention threshold can be understood as a threshold determined statistically based on actual conditions, used to determine whether the driver's attention meets the braking judgment requirements. The preset relaxation threshold can be understood as a threshold determined statistically based on actual conditions, used to determine whether the driver's relaxation meets the braking judgment requirements.

[0074] S204. Determine the brainwave braking control information as non-compliant brainwave braking control information and execute step S210.

[0075] Specifically, when the current attention value is less than the preset attention threshold and the current relaxation value is greater than the preset relaxation threshold, it can be considered that the driver is too relaxed or inattentive at the current driving moment and cannot achieve accurate and effective braking control of the vehicle. If the vehicle is still braked based on the brainwave braking control information generated by the driver's brainwaves at this time, it may lead to a collision accident or unnecessary braking that affects the driving experience. In this case, the brainwave braking control information is identified as non-compliant brainwave braking control information.

[0076] S205. Determine the control type of the control-type brainwave information. When the control type is braking control, compare the emotion-type brainwave information with the preset emotion-type brainwave information set to determine the emotion type of the emotion-type brainwave information.

[0077] In this embodiment, the preset set of emotion-related brainwave information can be specifically understood as a set of brainwave information extracted and stored in advance from the human body under different emotions. In the preset set of emotion-related brainwave information, each emotion-related brainwave information will be labeled with an emotion type.

[0078] Specifically, based on the study of brainwave information, the portion of brainwave data belonging to the control category can be classified into different types. That is, control-type brainwave information has its corresponding different control types. When the control type is determined to be braking control, it can be assumed that the driver has a need to brake the vehicle after analyzing the driver's current brainwave data. At this time, the emotional brainwave information is compared with the preset emotional brainwave information set to determine the emotional type of the emotional brainwave information at the current moment based on the comparison results.

[0079] Furthermore, if it is determined that the control type of the control-type brainwave information is not the braking control type, it can be assumed that the driver has no braking control need at the current moment. In this case, there is no need to determine the emotion type of the emotion-type brainwave information, and therefore no brainwave braking control information will be generated to brake the vehicle.

[0080] S206. If the emotion type belongs to the preset emotion type, then the brainwave braking control information is determined to be compliant brainwave braking control information, and step S209 is executed.

[0081] In this embodiment, the preset emotion type can be understood as a set of emotion types that the driver may experience when performing vehicle braking tasks, which are predetermined based on the actual situation.

[0082] Specifically, when the control-type brainwave information is braking control type brainwave information, and the emotion type of the emotion-type brainwave information belongs to the preset emotion type, it can be considered that the driver has a clear emotion to brake the vehicle at the current moment, and the control intention in the control-type brainwave information is real and effective. At this time, brainwave braking control information for braking control of the vehicle will be generated, and the brainwave braking control information will be identified as compliant brainwave braking control information.

[0083] S207. Generate a map of the surrounding environment based on radar point cloud data.

[0084] Specifically, based on the distance information contained in each radar point cloud in the radar point cloud data, the relative positions of different objects around the vehicle at the current moment can be determined, thereby generating a surrounding environment map containing information about the vehicle's surrounding environment.

[0085] S208. If the surrounding environment map contains preset braking information, then radar braking control information is generated; otherwise, radar braking control information is not generated.

[0086] In this embodiment, the preset braking information can be understood as a set of scenario information that includes the need for braking, which is preset according to the actual situation.

[0087] Specifically, if a scene matching the preset braking information is detected in the generated surrounding environment map, it can be assumed that the vehicle has a braking requirement under the current driving situation detected by the radar; otherwise, a collision may occur, and radar braking control information is generated. Otherwise, it can be assumed that the vehicle will not collide under the current driving situation detected by the radar and has no braking requirement, and radar braking control information is not generated. If radar braking control information is generated, step S209 or step S210 is executed.

[0088] It should be clarified that there is no obvious sequential relationship between S202-S206 and S207-S208. The two sets of steps can be executed simultaneously or in different orders as needed. This embodiment of the invention does not impose any restrictions on this.

[0089] S209. If the brainwave braking control information is compliant brainwave braking control information, and both compliant brainwave braking control information and radar braking control information exist simultaneously, then vehicle braking control information for controlling vehicle braking is generated.

[0090] Specifically, when the brainwave braking control information is compliant brainwave braking control information, and both compliant brainwave braking control information and radar braking control information exist simultaneously, it can be considered that the vehicle has a braking demand based on radar judgment at the current moment, and the driver also has a braking demand. At this time, the reliability of the vehicle braking is high, and vehicle braking control information for controlling vehicle braking can be generated accordingly.

[0091] Furthermore, if the brainwave braking control information is compliant brainwave braking control information, but the compliant brainwave braking control information and radar braking control information do not exist simultaneously, since the driver's brainwaves are more likely to be interfered with, it is impossible to accurately determine whether the vehicle needs to brake at the current moment, and therefore vehicle braking control information for controlling vehicle braking will not be generated.

[0092] In this embodiment of the invention, the vehicle's braking demand is determined by combining compliant EEG braking control information and radar braking control information. This avoids ineffective braking when relying solely on EEG information for braking control, ensuring the accuracy of braking control and improving driving safety and comfort.

[0093] S210. Generate vehicle braking control information for controlling vehicle braking based on radar braking control information.

[0094] Specifically, when the brainwave braking control information is non-compliant, it can be assumed that the driver does not have the ability to make braking judgments at the current moment, or that the driver's braking judgments at the current moment are inaccurate. In this case, the vehicle can be braked based solely on the collected radar point cloud data, that is, vehicle braking control information is generated based on the radar braking control information to control the vehicle to brake.

[0095] Furthermore, after determining vehicle braking control information based on EEG braking control information and radar braking control information, and controlling vehicle braking based on vehicle braking control information, the process also includes:

[0096] The current EEG data, EEG braking control information, and vehicle braking control information are combined into an EEG control optimization information set.

[0097] The information set for optimizing brainwave control is analyzed, and the preset set of emotion-related brainwave information is updated based on the analysis results.

[0098] In this embodiment, the brainwave control optimization information set can be specifically understood as a set constructed to achieve accurate identification of information in brainwaves, which associates and stores brainwave information and vehicle control information from multiple historical moments.

[0099] Specifically, after determining the vehicle braking control information based on EEG braking control information and radar braking control information, the braking control for the vehicle at the current moment can be considered complete. At this point, the vehicle braking operation can be considered to have occurred. The braking operation and acquired parameter information at this moment can be used as historical information for subsequent analysis and judgment. At this point, the current EEG data, EEG braking control information, and vehicle braking control information can be combined based on the time of generation to generate an optimized EEG control information set. It can be understood that after each current moment ends, a combination can be written into the optimized EEG control information set. This optimized EEG control information set can be uploaded to the cloud for staff to perform EEG analysis to determine whether the vehicle braking control information generated at different times is appropriate, i.e., whether there is erroneous EEG braking control information. This allows for the determination of the type of EEG information required when the vehicle needs to brake. Based on the staff's analysis results, the preset emotional EEG information set can be updated, making the updated preset emotional EEG set closer to the driver's actual driving situation, further improving the accuracy of the EEG braking control information.

[0100] The technical solution of this embodiment compares emotional EEG information with a preset set of emotional EEG information to determine the corresponding emotion type. Then, based on different emotion types and control-type EEG information of different control types, it determines compliant EEG braking control information that the driver needs for braking control. Only when the EEG braking control information is compliant, and both compliant EEG braking control information and radar braking control information are present, is vehicle control braking information generated to brake the vehicle. Furthermore, when the current EEG data determines that the EEG braking control information is non-compliant, only radar braking control information is used to achieve vehicle braking control, ensuring that the vehicle can still perform correct braking control even when the driver's mental state is poor, thus improving vehicle driving safety.

[0101] Example 3

[0102] Figure 4 This is a schematic diagram of a braking control device provided in Embodiment 3 of the present invention. The braking control device includes: a data acquisition module 31, a control information determination module 32, and a braking control module 33.

[0103] The data acquisition module 31 is used to acquire current EEG data and radar point cloud data; the control information determination module 32 is used to determine EEG braking control information based on the current EEG data and radar braking control information based on the radar point cloud data; the braking control module 33 is used to determine vehicle braking control information based on the EEG braking control information and radar braking control information, and control vehicle braking based on the vehicle braking control information; wherein, the current EEG data includes emotional EEG information and control EEG information.

[0104] The technical solution of this embodiment determines brainwave braking control information based on emotional and control-related brainwave information in the current brainwave data. By restricting the control-related brainwave information through emotional brainwave information, the determined brainwave braking control information becomes more accurate. At the same time, the radar braking control information determined based on radar point cloud data is combined with the brainwave braking control information to jointly determine the vehicle braking control information. By adopting a dual standard to judge the vehicle braking demand, the accuracy of vehicle braking information determination is improved, thereby enhancing the safety of vehicle driving.

[0105] Optionally, the control information determination module 32 includes:

[0106] The emotion type determination unit is used to determine the control type of control-type brainwave information. When the control type is braking control, the emotion-type brainwave information is compared with a preset set of emotion-type brainwave information to determine the emotion type of the emotion-type brainwave information.

[0107] The first control information determination unit is used to determine the brainwave braking control information as compliant brainwave braking control information if the emotion type belongs to a preset emotion type.

[0108] The environment map generation unit is used to generate a map of the surrounding environment based on radar point cloud data.

[0109] The second control information determination unit is used to generate radar braking control information if the surrounding environment map contains preset braking information; otherwise, it does not generate radar braking control information.

[0110] Optionally, the control information determination module 32 further includes:

[0111] Attention relaxation parameter determination unit, used to determine the current attention value and current relaxation value based on the current EEG data before determining the control type of control-type EEG information;

[0112] The third control information determination unit is used to determine the EEG braking control information as non-compliant EEG braking control information if the current attention value is less than the preset attention threshold and the current relaxation value is greater than the preset relaxation threshold.

[0113] Optionally, note the relaxation parameter determination unit, specifically used for:

[0114] Determine the alpha rhythm power spectrum, beta rhythm power spectrum, and theta rhythm power spectrum based on current EEG data;

[0115] The sum of the power spectrum of α rhythm wave and β rhythm wave is determined, and the ratio of the sum to the power spectrum of θ rhythm wave is determined as the EEG power spectrum ratio.

[0116] The current attention level is determined based on the ratio of the brainwave power spectrum, and the current relaxation level is determined based on the alpha rhythm wave power spectrum.

[0117] Optional, the braking control module 33 is specifically used for:

[0118] If the brainwave braking control information is compliant brainwave braking control information, and both compliant brainwave braking control information and radar braking control information exist simultaneously, then vehicle braking control information for controlling vehicle braking is generated.

[0119] If the brainwave braking control information is non-compliant, then vehicle braking control information for controlling vehicle braking is generated based on the radar braking control information.

[0120] Optionally, the braking control device may also include an information set update module.

[0121] The information set update module is used to determine vehicle braking control information based on EEG braking control information and radar braking control information, and control vehicle braking based on vehicle braking control information. Then, it combines the current EEG data, EEG braking control information and vehicle braking control information into an EEG control optimization information set; analyzes the EEG control optimization information set; and updates the preset emotion-related EEG information set based on the analysis results.

[0122] The braking control device of this invention can execute the braking control method provided in any embodiment of this invention, and has the corresponding functional modules and beneficial effects of the method.

[0123] Example 4

[0124] Figure 5 This is a structural schematic diagram of a vehicle provided in Embodiment 4 of the present invention, as shown below. Figure 5 As shown, the vehicle includes a brain-computer interface 40, a lidar 41, a controller 42, a storage device 43, an input device 44, and an output device 45; the number of controllers 42 in the vehicle can be one or more. Figure 5Taking a controller 42 as an example; the lidar 41, controller 42, storage device 43, input device 44, and output device 45 in the vehicle can be connected via bus or other means. Figure 5 Taking the example of a connection between China and Israel via a bus.

[0125] Brain-computer interface 40 is used to acquire current brainwave data.

[0126] LiDAR 41 is used to acquire radar point cloud data.

[0127] Storage device 43, as a computer-readable storage medium, can be used to store software programs, computer-executable programs, and modules, such as the program instructions / modules corresponding to the braking control method in this embodiment of the invention (e.g., data acquisition module 31, control information determination module 32, and braking control module 33). Controller 42 executes various vehicle functions and data processing by running the software programs, instructions, and modules stored in storage device 43, thereby realizing the aforementioned braking control method.

[0128] Storage device 43 may primarily include a program storage area and a data storage area. The program storage area may store the operating system and at least one application program required for a given function; the data storage area may store data created based on terminal usage. Furthermore, storage device 43 may include high-speed random access memory and non-volatile memory, such as at least one disk storage device, flash memory, or other non-volatile solid-state storage device. In some instances, storage device 43 may further include memory remotely configured relative to controller 42, which can be connected to the vehicle via a network. Examples of such networks include, but are not limited to, the Internet, intranets, local area networks, mobile communication networks, and combinations thereof.

[0129] Input device 44 can be used to receive input digital or character information, and to generate key signal inputs related to user settings and function control of the vehicle. Output device 45 may include display devices such as a display screen.

[0130] In some embodiments, the braking control method may be implemented as a computer program tangibly contained in a computer-readable storage medium, such as a storage unit. In some embodiments, part or all of the computer program may be loaded and / or installed on the braking control device via ROM and / or a communication unit. When the computer program is loaded into RAM and executed by a processor, one or more steps of the braking control method described above may be performed. Alternatively, in other embodiments, the processor may be configured to perform the braking control method by any other suitable means (e.g., by means of firmware).

[0131] Various embodiments of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field-programmable gate arrays (FPGAs), application-specific integrated circuits (ASICs), application-specific standard products (ASSPs), systems-on-a-chip (SoCs), payload-programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments may include implementations in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which may be a dedicated or general-purpose programmable processor, capable of receiving data and instructions from a storage system, at least one input device, and at least one output device, and transmitting data and instructions to the storage system, the at least one input device, and the at least one output device.

[0132] Computer programs used to implement the methods of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, such that when executed by the processor, the computer programs cause the functions / operations specified in the flowcharts and / or block diagrams to be performed. The computer programs may be executed entirely on a machine, partially on a machine, or as a standalone software package, partially on a machine and partially on a remote machine, or entirely on a remote machine or server.

[0133] In the context of this invention, a computer-readable storage medium can be a tangible medium that may contain or store a computer program for use by or in conjunction with an instruction execution system, apparatus, or device. A computer-readable storage medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination thereof. Alternatively, a computer-readable storage medium may be a machine-readable signal medium. More specific examples of machine-readable storage media include electrical connections based on one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fibers, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof.

[0134] To provide interaction with a user, the systems and techniques described herein can be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and pointing device (e.g., a mouse or trackball) through which the user provides input to the electronic device. Other types of devices can also be used to provide interaction with the user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including sound input, voice input, or tactile input).

[0135] The systems and technologies described herein can be implemented in computing systems that include backend components (e.g., as data servers), or computing systems that include middleware components (e.g., application servers), or computing systems that include frontend components (e.g., user computers with graphical user interfaces or web browsers through which users can interact with implementations of the systems and technologies described herein), or any combination of such backend, middleware, or frontend components. The components of the system can be interconnected via digital data communication of any form or medium (e.g., communication networks). Examples of communication networks include local area networks (LANs), wide area networks (WANs), blockchain networks, and the Internet.

[0136] A computing system can include clients and servers. Clients and servers are generally located far apart and typically interact through a communication network. The client-server relationship is created by computer programs running on the respective computers and having a client-server relationship with each other. The server can be a cloud server, also known as a cloud computing server or cloud host, which is a hosting product within the cloud computing service system to address the shortcomings of traditional physical hosts and VPS services, such as high management difficulty and weak business scalability.

[0137] It should be understood that the various forms of processes shown above can be used, with steps reordered, added, or deleted. For example, the steps described in this invention can be executed in parallel, sequentially, or in different orders, as long as the desired result of the technical solution of this invention can be achieved, and this is not limited herein.

[0138] The specific embodiments described above do not constitute a limitation on the scope of protection of this invention. Those skilled in the art should understand that various modifications, combinations, sub-combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this invention should be included within the scope of protection of this invention.

Claims

1. A braking control method, characterized in that, include: Acquire current EEG data and radar point cloud data; Brainwave braking control information is determined based on the current brainwave data, and radar braking control information is determined based on the radar point cloud data; Vehicle braking control information is determined based on the brainwave braking control information and the radar braking control information, and vehicle braking is controlled based on the vehicle braking control information. The current EEG data includes emotional EEG information and control EEG information. The step of determining brainwave braking control information based on the current brainwave data includes: The control type of the control-type brainwave information is determined. When the control type is braking control, the emotion-type brainwave information is compared with a preset set of emotion-type brainwave information to determine the emotion type of the emotion-type brainwave information. If the emotion type belongs to a preset emotion type, then the brainwave braking control information is determined to be compliant brainwave braking control information; The preset emotion type is a set of emotion types that a driver may experience when performing vehicle braking tasks.

2. The method according to claim 1, characterized in that, Before determining the control type of the control-type EEG information, the method further includes: The current attention level and current relaxation level are determined based on the current EEG data. If the current attention value is less than the preset attention threshold and the current relaxation value is greater than the preset relaxation threshold, then the EEG braking control information is determined to be non-compliant EEG braking control information.

3. The method according to claim 2, characterized in that, The step of determining vehicle braking control information based on the electroencephalogram (EEG) braking control information and the radar braking control information includes: If the brainwave braking control information is compliant brainwave braking control information, and the compliant brainwave braking control information and the radar braking control information exist simultaneously, then vehicle braking control information for controlling vehicle braking is generated. If the brainwave braking control information is non-compliant brainwave braking control information, then vehicle braking control information for controlling vehicle braking is generated based on the radar braking control information.

4. The method according to claim 2, characterized in that, The step of determining the current attention value and current relaxation value based on the current EEG data includes: The power spectra of alpha, beta, and theta rhythms are determined based on the current EEG data. The sum of the α-cycle power spectrum and the β-cycle power spectrum is determined, and the ratio of the sum to the θ-cycle power spectrum is determined as the EEG power spectrum ratio. The current attention value is determined based on the ratio of the EEG power spectrum, and the current relaxation value is determined based on the alpha rhythm wave power spectrum.

5. The method according to claim 1, characterized in that, The step of determining radar braking control information based on the radar point cloud data includes: A map of the surrounding environment is generated based on the radar point cloud data; If the surrounding environment map contains preset braking information, radar braking control information is generated; otherwise, radar braking control information is not generated.

6. The method according to any one of claims 1-4, characterized in that, After determining vehicle braking control information based on the electroencephalogram (EEG) braking control information and the radar braking control information, and controlling vehicle braking based on the vehicle braking control information, the method further includes: The current EEG data, the EEG braking control information, and the vehicle braking control information are combined into an EEG control optimization information set. The brainwave control optimization information set is analyzed, and the preset emotion-related brainwave information set is updated based on the analysis results.

7. A braking control device, characterized in that, include: The data acquisition module is used to acquire current EEG data and radar point cloud data; The control information determination module is used to determine brainwave braking control information based on the current brainwave data and to determine radar braking control information based on the radar point cloud data. A braking control module is used to determine vehicle braking control information based on the brainwave braking control information and the radar braking control information, and to control vehicle braking based on the vehicle braking control information. The current EEG data includes emotional EEG information and control EEG information. The control information determination module includes: An emotion type determination unit is used to determine the control type of the control-type brainwave information. When the control type is a braking control type, the emotion-type brainwave information is compared with a preset set of emotion-type brainwave information to determine the emotion type of the emotion-type brainwave information. The first control information determining unit is used to determine the brainwave braking control information as compliant brainwave braking control information if the emotion type belongs to a preset emotion type. The preset emotion type is a set of emotion types that a driver may experience when performing vehicle braking tasks.

8. A vehicle, characterized in that, The vehicles include: Brain-computer interface is used to acquire current brainwave data; LiDAR is used to acquire radar point cloud data; One or more controllers; Storage device for storing one or more programs; When the one or more programs are executed by the one or more controllers, the one or more controllers implement the braking control method as described in any one of claims 1-6.

9. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer instructions that, when executed by a processor, are used to implement the braking control method as described in any one of claims 1-6.

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