Description:Field of the Invention

The present invention pertains to vehicle safety systems, particularly to a system utilizing electroencephalogram (EEG) sensors for monitoring the cognitive state of a vehicle driver. Through the continuous analysis of brainwave data, the system is designed to detect signs of drowsiness, triggering alerts and providing feedback to enhance driver alertness and mitigate the risks associated with impaired driving.
Background
The background description includes information that may be useful in understanding the present invention. It is not an admission that any of the information provided herein is prior art or relevant to the presently claimed invention, or that any publication specifically or implicitly referenced is prior art.
The vehicle safety domain constantly strives to enhance driver awareness and mitigate potential hazards associated with impaired cognitive states while operating vehicles. State-of-the-art safety systems typically rely on various sensors and analysis techniques to monitor driver behavior and provide timely alerts in critical situations. However, conventional systems often face limitations in accurately detecting signs of drowsiness, posing challenges in ensuring optimal safety levels on the roads.
Such systems may incorporate electroencephalogram (EEG) sensors worn by drivers to capture brainwave data indicative of their cognitive state. The incoming EEG data is typically subjected to continuous analysis using machine learning algorithms, aimed at identifying patterns associated with drowsiness. Despite these efforts, existing systems encounter difficulties in reliably distinguishing between normal alertness and early signs of drowsiness, leading to potential delays in alerting the driver.
In light of the above discussion, there exists an urgent need for solutions that overcome the challenges associated with conventional systems for detecting driver drowsiness. Such solutions should offer improved accuracy in recognizing early signs of cognitive impairment, enabling timely alerts to mitigate risks and enhance overall safety on the roads.
Summary
The following presents a simplified summary of various aspects of this disclosure in order to provide a basic understanding of such aspects. This summary is not an extensive overview of all contemplated aspects, and is intended to neither identify key or critical elements nor delineate the scope of such aspects. Its purpose is to present some concepts of this disclosure in a simplified form as a prelude to the more detailed description that is presented later.
The following paragraphs provide additional support for the claims of the subject application.
In an aspect, the present disclosure provides a vehicle safety system equipped with an electroencephalogram (EEG) sensor designed to be worn by a vehicle driver for monitoring brainwave data indicative of the cognitive state of the driver. An analysis unit within the system employs a machine learning module to continuously analyze the incoming brainwave data for detecting signs of drowsiness. Upon detection, an alert mechanism is activated to notify the driver of the drowsiness condition. Additionally, a feedback unit provides auditory and/or tactile feedback to the driver once the alert mechanism is triggered, aiming to enhance driver alertness and mitigate the risks associated with impaired driving.
Further, the EEG sensor is integrated into a headband or a helmet, providing a convenient and effective means for monitoring the driver's cognitive state. Furthermore, the system includes a training module configured to instruct drivers on responses to feedback system alerts, enhancing the overall efficacy of the safety system. Moreover, a fatigue monitoring camera and an eye state monitoring camera are incorporated into the system to capture and analyze visual indicators of driver fatigue, such as yawning frequency, eye closure duration, head nodding, blink rate, and eye closure ratio. Additionally, an alcohol sensor measures the concentration of alcohol vapor in the cabin air, assessing the intoxication status of the driver.
The alert mechanism comprises a speaker system for auditory alerts through sound or spoken messages, an LED lamp for visual alerts via flashing lights of different colors and patterns, and electrodes for tactile alerts through the application of a mild electric current. Furthermore, a vehicle control unit (VCU) is included in the system to initiate a safety protocol that reduces the speed of the vehicle upon detection of drowsiness or intoxication, further enhancing the safety measures provided by the system.
Additionally, the disclosure presents a method for enhancing vehicle safety using EEG technology, involving the monitoring of a driver's brainwave data, the real-time analysis of this data employing machine learning algorithms to detect cognitive states indicative of drowsiness, and the triggering of an alert mechanism that provides feedback to the driver when drowsiness is detected. This comprehensive approach to vehicle safety aims to significantly reduce the risks associated with driving under the influence of drowsiness or intoxication, promoting a safer driving environment.

Brief Description of the Drawings

The features and advantages of the present disclosure would be more clearly understood from the following description taken in conjunction with the accompanying drawings in which:
FIG. 1 illustrates a block diagram of a vehicle safety system (100), in accordance with the embodiments of the present disclosure.
FIG. 2 illustrates a method flow diagram (200) for vehicle safety using EEG technique, in accordance with the embodiments of the present disclosure.
FIG. 3 illustrates exemplary a flowchart detailing a driver safety system based on EEG technology, in accordance with embodiment of present disclosure.
FIG. 4 illustrates an experimental setup for a driver safety system, in accordance with embodiment of present disclosure.

Detailed Description
In the following detailed description of the invention, reference is made to the accompanying drawings that form a part hereof, and in which is shown, by way of illustration, specific embodiments in which the invention may be practiced. In the drawings, like numerals describe substantially similar components throughout the several views. These embodiments are described in sufficient detail to claim those skilled in the art to practice the invention. Other embodiments may be utilized and structural, logical, and electrical changes may be made without departing from the scope of the present invention. The following detailed description is, therefore, not to be taken in a limiting sense, and the scope of the present invention is defined only by the appended claims and equivalents thereof.
The use of the terms “a” and “an” and “the” and “at least one” and similar referents in the context of describing the invention (especially in the context of the following claims) are to be construed to cover both the singular and the plural, unless otherwise indicated herein or clearly contradicted by context. The use of the term “at least one” followed by a list of one or more items (for example, “at least one of A and B”) is to be construed to mean one item selected from the listed items (A or B) or any combination of two or more of the listed items (A and B), unless otherwise indicated herein or clearly contradicted by context. The terms “comprising,” “having,” “including,” and “containing” are to be construed as open-ended terms (i.e., meaning “including, but not limited to,”) unless otherwise noted. Recitation of ranges of values herein are merely intended to serve as a shorthand method of referring individually to each separate value falling within the range, unless otherwise indicated herein, and each separate value is incorporated into the specification as if it were individually recited herein. All methods described herein can be performed in any suitable order unless otherwise indicated herein or otherwise clearly contradicted by context. The use of any and all examples, or exemplary language (e.g., “such as”) provided herein, is intended merely to better illuminate the invention and does not pose a limitation on the scope of the invention unless otherwise claimed. No language in the specification should be construed as indicating any non-claimed element as essential to the practice of the invention.
Pursuant to the "Detailed Description" section herein, whenever an element is explicitly associated with a specific numeral for the first time, such association shall be deemed consistent and applicable throughout the entirety of the "Detailed Description" section, unless otherwise expressly stated or contradicted by the context.
The term "vehicle safety system" as used throughout the present disclosure relates to a comprehensive assembly designed to enhance the safety of a vehicle by monitoring the cognitive state of a driver to detect signs of drowsiness. The vehicle safety system comprises several components including an electroencephalogram (EEG) sensor, an analysis unit, an alert mechanism, and a feedback unit, each contributing to the system's overall function of improving driver alertness and mitigating the risks associated with impaired driving.
The term "electroencephalogram (EEG) sensor" as used throughout the present disclosure refers to a device configured to be worn by a vehicle driver to monitor brainwave data indicative of a cognitive state of the driver. The EEG sensor is designed for seamless integration with the driver's headwear, such as a headband or helmet, allowing for continuous monitoring of brainwave activity without causing discomfort or hindrance to the driver.
The term "analysis unit" as used throughout the present disclosure pertains to a component that employs a machine learning module for the continuous analysis of incoming brainwave data. The analysis unit is equipped with advanced algorithms capable of processing and interpreting EEG data to detect signs of drowsiness in real-time, thereby facilitating immediate response to changes in the driver's cognitive state.
The term "alert mechanism" as used throughout the present disclosure describes a system configured to be triggered by an alert upon detection of drowsiness by the analysis unit. The alert mechanism encompasses various modalities for notifying the driver, including auditory, visual, and tactile signals, to ensure that the alert effectively captures the driver's attention and prompts immediate corrective action.
The term "feedback unit" as used throughout the present disclosure refers to a component providing auditory and/or tactile feedback to the driver when the alert mechanism is triggered. The feedback unit plays a crucial role in enhancing the efficacy of the alert mechanism by reinforcing the alert with additional sensory inputs, thereby maximizing the likelihood of the driver's response to the detected drowsiness.
FIG. 1 illustrates a block diagram of a vehicle safety system (100), in accordance with the embodiments of the present disclosure. Said vehicle safety system (100) comprises EEG sensor (102) configured to be worn by a vehicle driver to monitor brainwave data indicative of the cognitive state of said driver. An analysis unit (104) is provided and employs a machine learning module to continuously analyze the incoming brainwave data to detect signs of drowsiness. An alert mechanism (106) is configured to be triggered to provide an alert upon detection of drowsiness by said analysis unit (104). A feedback unit (108) is comprised to provide auditory and/or tactile feedback to said driver when said alert mechanism (106) is triggered. Said components are illustrated as discrete modules within said vehicle safety system (100), demonstrating the system's modular architecture, which facilitates both implementation and potential future enhancements.
Optionally, the EEG sensor (102) may be integrated into various types of headwear, offering flexibility in application and use. Additionally, the analysis unit (104) may incorporate varying machine learning algorithms to cater to different operational requirements and environments. The alert mechanism (106) and the feedback unit (108) may also be customized to include a broader range of feedback options, accommodating driver preferences and sensitivities.
In an embodiment, the vehicle safety system (100) further enhances the practicality and ease of use by integrating the electroencephalogram (EEG) sensor (102) into a headband or helmet. This design choice significantly improves the usability and comfort for drivers, facilitating continuous monitoring of brainwave data without hindering the driving experience. The integration into wearable accessories like a headband or helmet ensures that the EEG sensor remains securely in place, providing accurate and reliable data on the cognitive state of the driver. This arrangement not only simplifies the process of wearing the sensor for prolonged periods but also enhances the system's effectiveness in detecting signs of drowsiness early and accurately. By embedding the sensor into commonly used headwear, the system promotes driver compliance with safety protocols, thereby enhancing the overall safety and responsiveness of the vehicle safety system.
In another embodiment, the system (100) comprises a training module specifically configured to instruct drivers on appropriate responses to the feedback system alerts. This module is designed to educate drivers on the significance of the alerts and the recommended corrective actions to ensure safety. The inclusion of a training module addresses the challenge of ensuring that drivers understand and appropriately react to alerts, thereby maximizing the effectiveness of the safety system. Through interactive sessions, drivers become familiar with the system's functionality and the criticality of responding promptly to drowsiness alerts. This educational component plays a crucial role in enhancing driver preparedness and response, significantly contributing to the reduction of risks associated with drowsy driving.
In yet another embodiment, the system (100) incorporates a fatigue monitoring camera as part of its comprehensive approach to detecting driver drowsiness. This camera is configured to continuously capture and analyze visual indicators of driver fatigue, such as yawning frequency, eye closure duration, and head nodding. The inclusion of this camera allows for an additional layer of monitoring, complementing the data collected from the EEG sensor. By focusing on observable physical behaviors that indicate fatigue, the system ensures a more holistic assessment of the driver's alertness levels. The use of visual indicators to detect signs of drowsiness enhances the system's accuracy and reliability, providing a robust mechanism for early detection and intervention to prevent drowsy driving incidents.
In an embodiment, the fatigue monitoring camera, the system (100) is further equipped with an eye state monitoring camera. This camera is dedicated to monitoring the open or closed state of the eyelids, calculating the blink rate and eye closure ratio to detect signs of drowsiness. The eye state monitoring camera offers a precise and non-intrusive method to assess the driver's alertness by analyzing eye movement patterns, which are critical indicators of fatigue. This technology allows for continuous observation of the driver's eye behavior, providing real-time data that enhances the system's capability to promptly detect and alert drivers to the onset of drowsiness. The integration of this camera into the vehicle safety system underscores the system's commitment to leveraging advanced technology to ensure driver safety and prevent accidents caused by fatigue.
In another embodiment, the system (100) extends its safety features by including an alcohol sensor designed to measure the concentration of alcohol vapor in the cabin air. This sensor assesses the intoxication status of the driver, adding a crucial layer of safety by identifying potential impairment due to alcohol consumption. The ability to detect alcohol vapor within the vehicle enables the system to alert drivers and suggest corrective actions, such as pulling over and resting, before continuing to drive. This proactive approach to monitoring and addressing the risks associated with alcohol-impaired driving demonstrates the system's comprehensive strategy to enhance vehicle safety, ensuring a safer driving environment for all road users.
In an embodiment, the alert mechanism (106) of the vehicle safety system (100) comprises a multifaceted approach to notify the driver upon the detection of drowsiness. This includes a speaker system for auditory alerts, an LED lamp for visual alerts, and electrodes for tactile alerts. The speaker system employs sound or spoken messages to immediately capture the driver's attention, ensuring the alert is heard even in a noisy environment. The LED lamp enhances the alert mechanism by providing visual alerts via flashing lights of different colors and patterns, which can be particularly effective during conditions of reduced visibility or to augment the auditory alert. Additionally, the electrodes offer a unique means of alert through the application of a mild electric current, providing a direct tactile stimulus to the driver. This combination of auditory, visual, and tactile alerts ensures a comprehensive notification system that is highly effective in alerting and re-engaging drivers, thereby significantly reducing the risk of accidents due to drowsiness.
In another embodiment, the system (100) includes a vehicle control unit (VCU) that activates a safety protocol reducing the vehicle's speed upon the detection of drowsiness or intoxication by the driver. This integration of a VCU into the safety system represents a proactive measure to enhance road safety by directly controlling the vehicle's operational parameters in critical situations. Upon detecting signs of impaired driving, the VCU initiates a controlled deceleration process, thereby minimizing the risk of collisions or accidents. This feature is particularly beneficial in scenarios where the driver does not respond to the initial alerts, ensuring that the vehicle is automatically brought to a safer speed, which can provide additional time for the driver to regain full alertness or for the vehicle to be safely stopped.
FIG. 2 illustrates a method flow diagram (200) for vehicle safety using EEG technique, in accordance with the embodiments of the present disclosure. Method flow step (202), a driver with an EEG sensor (102) comprise to continuously monitor brainwave data. In step (204) machine learning algorithms are employed to analyze said monitored brainwave data. Step (206) comprises detecting cognitive states indicative of drowsiness through said analysis. Upon detection of such cognitive states, an alert mechanism is triggered to provide feedback to said driver in step (208). The said steps are depicted sequentially, indicating the method flow from the equipping of the driver with the EEG sensor to the step of triggering the alert mechanism, embodying a systematic process designed to enhance driver safety by monitoring and responding to signs of drowsiness.
FIG. 3 illustrates exemplary a flowchart detailing a driver safety system based on EEG technology, in accordance with embodiment of present disclosure. The system operates by monitoring the driver's cognitive state through EEG and employs machine learning to analyze this data for signs of fatigue. Upon detection of drowsiness, an integrated alert mechanism activates, providing auditory and tactile feedback without necessitating significant modifications to the vehicle, thereby maintaining ease of use. Wireless data transmission ensures continuous monitoring and the ability to deliver instant alerts. These alerts prompt the driver to take corrective action, leading to enhanced safety outcomes. The system's design focuses on seamless integration with the vehicle's existing safety features, user-friendly interaction, and proactive safety measures to mitigate the risks associated with driving while fatigued. The driver alertness monitoring system uses EEG technology to detect driver fatigue and prevent drowsy driving. The system records brain activity to assess alertness levels and employs machine learning algorithms to differentiate patterns related to being awake and tired. Wireless transmission of EEG data from a cap worn by the driver to an in-vehicle processing module allows for real-time signal analysis. When drowsiness is detected, the system triggers alerts to prompt immediate driver response. EEG-based drowsiness monitoring system of present disclosure improves efficiency over known drowsiness detection. The proposed drowsiness detection solution enables early detection and rapid intervention to enhance road safety and reduce accidents related to driver tiredness. The proposed system of present disclosure improves road safety by detecting driver drowsiness early through EEG technology and machine learning. The EED headset monitors brainwave patterns to identify fatigue, providing real-time alerts to the driver using auditory or tactile feedback. The proactive approach ensures early intervention, reducing the risk of accidents caused by drowsy driving. The system seamlessly integrates with existing vehicle safety features, offering continuous monitoring without interfering with the driver's comfort or vehicle operations. The system enhances road safety by lowering the chance of fatigue-related accidents and proactive accident prevention through real-time cognitive state monitoring. Further, the system raises driver awareness, prompting to take timely breaks or other measures. The non-intrusive, wireless technology maintains driver comfort, and the customizable alerts cater to individual preferences, enabling a swift reaction.
FIG. 4 illustrates an experimental setup for a driver safety system, in accordance with embodiment of present disclosure. The experimental setup comprises an Arduino Uno connected to a BioAmp EXG Pill through jumper cables. The BioAmp EXG Pill is further connected to a person via a BioAmp cable using proper electrical connections such as “5V to VCC", "GND to GND", and "A0 to OUT", etc. The Arduino Uno is also connected to a device, likely a computer or laptop, via a USB cable, allowing for data transmission and possibly programming of the Arduino for the experiment. The setup is designed to monitor physiological signals from the person, presumably to assess states such as alertness or drowsiness for the driver safety system.
Example embodiments herein have been described above with reference to block diagrams and flowchart illustrations of methods and apparatuses. It will be understood that each block of the block diagrams and flowchart illustrations, and combinations of blocks in the block diagrams and flowchart illustrations, respectively, can be implemented by various means including hardware, software, firmware, and a combination thereof. For example, in one embodiment, each block of the block diagrams and flowchart illustrations, and combinations of blocks in the block diagrams and flowchart illustrations can be implemented by computer program instructions. These computer program instructions may be loaded onto a general purpose computer, special purpose computer, or other programmable data processing apparatus to produce a machine, such that the instructions which execute on the computer or other programmable data processing apparatus create means for implementing the functions specified in the flowchart block or blocks.
Throughout the present disclosure, the term ‘processing means’ or ‘microprocessor’ or ‘processor’ or ‘processors’ includes, but is not limited to, a general purpose processor (such as, for example, a complex instruction set computing (CISC) microprocessor, a reduced instruction set computing (RISC) microprocessor, a very long instruction word (VLIW) microprocessor, a microprocessor implementing other types of instruction sets, or a microprocessor implementing a combination of types of instruction sets) or a specialized processor (such as, for example, an application specific integrated circuit (ASIC), a field programmable gate array (FPGA), a digital signal processor (DSP), or a network processor).
The term “non-transitory storage device” or “storage” or “memory,” as used herein relates to a random access memory, read only memory and variants thereof, in which a computer can store data or software for any duration.
Operations in accordance with a variety of aspects of the disclosure is described above would not have to be performed in the precise order described. Rather, various steps can be handled in reverse order or simultaneously or not at all.
While several implementations have been described and illustrated herein, a variety of other means and/or structures for performing the function and/or obtaining the results and/or one or more of the advantages described herein may be utilized, and each of such variations and/or modifications is deemed to be within the scope of the implementations described herein. More generally, all parameters, dimensions, materials, and configurations described herein are meant to be exemplary and that the actual parameters, dimensions, materials, and/or configurations will depend upon the specific application or applications for which the teachings is/are used. Those skilled in the art will recognize, or be able to ascertain using no more than routine experimentation, many equivalents to the specific implementations described herein. It is, therefore, to be understood that the foregoing implementations are presented by way of example only and that, within the scope of the appended claims and equivalents thereto, implementations may be practiced otherwise than as specifically described and claimed. Implementations of the present disclosure are directed to each individual feature, system, article, material, kit, and/or method described herein. In addition, any combination of two or more such features, systems, articles, materials, kits, and/or methods, if such features, systems, articles, materials, kits, and/or methods are not mutually inconsistent, is included within the scope of the present disclosure.

Claims

I/We claims:

A vehicle safety system (100) comprising:
an electroencephalogram (EEG) sensor (102) configured to be worn by a vehicle driver to monitor brainwave data indicative of a cognitive state of a driver;
an analysis unit (104) that employs a machine learning module to continuously analyze the incoming brainwave data to detect signs of drowsiness;
an alert mechanism (106) configured to be triggered an alert upon detection of drowsiness; and
a feedback unit (108) providing auditory and/or tactile feedback to the driver when the alert mechanism (106) is triggered.
The system (100) of claim 1, wherein the EEG sensor (102) is integrated into a headband or a helmet.
The system (100) of claim 1, further comprising a training module configured to instruct drivers on responses to the feedback system alerts.
The system (100) of claim 1, further comprising a fatigue monitoring camera configured to continuously capture and analyze visual indicators of driver fatigue to detect signs of drowsiness, wherein the visual indicators selected from yawning frequency, eye closure duration, and head nodding.
The system (100) of claim 1, further comprising an eye state monitoring camera monitors eyes to monitor the open or closed state of the eyelids to calculate the blink rate and eye closure ratio to detect signs of drowsiness.
The system (100) of claim 1, further comprising an alcohol sensor to measure the concentration of alcohol vapor in the cabin air to assess the intoxication status of the driver.
The system of claim 1, wherein the alert mechanism (106) comprises:
a speaker system for auditory alerts through sound or spoken messages;
an LED lamp for visual alerts via flashing lights of different colors and patterns; and
electrodes for tactile alerts through the application of a mild electric current.
The system (100) of claim 1, comprises a vehicle control unit (VCU) initiates a safety protocol that reduces a speed of the vehicle upon detection of drowsiness or intoxication.
A method for enhancing vehicle safety using EEG technology, the method comprising:
monitoring a driver's brainwave data using an EEG sensor (102) worn by the driver;
analyzing the brainwave data, employing machine learning algorithms, in real time to detect cognitive states indicative of drowsiness; and
triggering an alert mechanism (106) that provides feedback to the driver when drowsiness is detected.

VEHICLE SAFETY SYSTEM WITH EEG MONITORING

This invention presents a vehicle safety system designed to enhance driver alertness and mitigate the risks associated with drowsiness while driving. The system incorporates an EEG sensor worn by the driver to monitor brainwave data, which is continuously analyzed by a machine learning module to detect signs of drowsiness. Upon detection, an alert mechanism is triggered to notify the driver, accompanied by auditory and/or tactile feedback through a feedback unit. By proactively identifying and addressing drowsiness, the system aims to improve driver vigilance and reduce the likelihood of accidents.

Drawings
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FIG. 1

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FIG. 2
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FIG. 3
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FIG. 4
, Claims:I/We claims:

A vehicle safety system (100) comprising:
an electroencephalogram (EEG) sensor (102) configured to be worn by a vehicle driver to monitor brainwave data indicative of a cognitive state of a driver;
an analysis unit (104) that employs a machine learning module to continuously analyze the incoming brainwave data to detect signs of drowsiness;
an alert mechanism (106) configured to be triggered an alert upon detection of drowsiness; and
a feedback unit (108) providing auditory and/or tactile feedback to the driver when the alert mechanism (106) is triggered.
The system (100) of claim 1, wherein the EEG sensor (102) is integrated into a headband or a helmet.
The system (100) of claim 1, further comprising a training module configured to instruct drivers on responses to the feedback system alerts.
The system (100) of claim 1, further comprising a fatigue monitoring camera configured to continuously capture and analyze visual indicators of driver fatigue to detect signs of drowsiness, wherein the visual indicators selected from yawning frequency, eye closure duration, and head nodding.
The system (100) of claim 1, further comprising an eye state monitoring camera monitors eyes to monitor the open or closed state of the eyelids to calculate the blink rate and eye closure ratio to detect signs of drowsiness.
The system (100) of claim 1, further comprising an alcohol sensor to measure the concentration of alcohol vapor in the cabin air to assess the intoxication status of the driver.
The system of claim 1, wherein the alert mechanism (106) comprises:
a speaker system for auditory alerts through sound or spoken messages;
an LED lamp for visual alerts via flashing lights of different colors and patterns; and
electrodes for tactile alerts through the application of a mild electric current.
The system (100) of claim 1, comprises a vehicle control unit (VCU) initiates a safety protocol that reduces a speed of the vehicle upon detection of drowsiness or intoxication.
A method for enhancing vehicle safety using EEG technology, the method comprising:
monitoring a driver's brainwave data using an EEG sensor (102) worn by the driver;
analyzing the brainwave data, employing machine learning algorithms, in real time to detect cognitive states indicative of drowsiness; and
triggering an alert mechanism (106) that provides feedback to the driver when drowsiness is detected.

VEHICLE SAFETY SYSTEM WITH EEG MONITORING