Driver fatigue detection and warning system

Abstract

An AI-supported driver fatigue detection and warning system, consisting of: a vision system comprising a camera and an image preprocessor, wherein the vision system is configured to capture facial images of the driver in real time via the camera and to preprocess the captured images via an image preprocessor; a server that is operationally connected to the vision system and configured to analyze the captured image using artificial intelligence and to generate fatigue values, wherein the server includes a core processing module that has been trained using an artificial intelligence model to detect driver fatigue and to generate fatigue values; a controller embedded in the steering wheel cover, which is operationally connected to the server and configured to process fatigue values ​​and activate tactile feedback; an actuator system that is operationally connected to the controller and consists of a multi-row arrangement of button motors controlled by addressable chips to provide the driver with localized tactile feedback based on commands from the controller, with the actuator system seamlessly integrated into the steering wheel cover; a user interface consisting of an instrument panel display and a configuration panel, configured to display fatigue levels and system warnings in real time and to allow the driver to configure system parameters; and a data storage module comprising one or more storage devices and configured to log fatigue events, tactile feedback patterns, and driver reactions.

Description

FIELD OF THE INVENTION

[0001] The present disclosure relates to an AI-based driver drowsiness detection and warning system. More specifically, the present invention relates to an AI-based driver drowsiness detection and warning system for preventing accidents caused by fatigue-induced drowsiness. The proposed system integrates AI models for drowsiness detection and an actuator system for providing tactile feedback through button motors arranged in a multi-row array in the steering wheel. BACKGROUND OF THE INVENTION

[0002] Fatigue is one of the leading causes of traffic accidents worldwide and often leads to reduced reaction times and poor driver judgment. Conventional warning systems with acoustic or visual signals are only partially effective because they rely on sensory access, which is already impaired under fatigue conditions.

[0003] Existing tactile systems lack precision, adaptability, and scalability, limiting their adaptability to different vehicle types. This invention overcomes these limitations with a comprehensive, closed-loop system that integrates AI, advanced tactile feedback using addressable chips, and real-time driver monitoring.

[0004] In view of the above explanation, the present invention provides an AI-based driver fatigue detection and warning system for preventing accidents caused by fatigue-induced drowsiness. SUMMARY OF THE INVENTION

[0005] This disclosure relates to an AI-based driver fatigue detection and warning system. The proposed system is designed to improve driver safety and, more importantly, prevent accidents caused by fatigue-related drowsiness. The system integrates an AI-based fatigue detection mechanism, a visual monitoring system, and a tactile feedback module embedded in the steering wheel. Upon detecting drowsiness, the system activates precise vibration patterns to alert the driver, thus ensuring road safety. It also includes a user interface for customization, data logging for analysis, and escalation protocols for unresponsive drivers.

[0006] The present disclosure aims to provide an AI-assisted driver fatigue detection and warning system. The system comprises: a vision system having a camera and an image pre-processor, wherein the vision system is configured to capture real-time facial images of the driver via the camera and pre-process the captured images via an image pre-processor; a server operatively connected to the vision system and configured to analyze the captured image using artificial intelligence and generate fatigue scores, wherein the server includes a core processing module trained using an artificial intelligence model to detect driver fatigue and generate fatigue scores; a controller embedded in the steering wheel cover and operatively connected to the server and configured to process fatigue scores and activate tactile feedback;an actuator system operatively connected to the controller and comprising a multi-row array of button motors controlled by addressable chips to provide localized tactile feedback to the driver based on commands from the controller, the actuator system being seamlessly integrated into the steering wheel cover; a user interface including a dashboard display and a configuration panel, configured to display real-time fatigue levels and system warnings and to allow the driver to configure the system parameters; and a data storage module including one or more storage means and configured to log fatigue events, tactile feedback patterns, and driver responses.

[0007] One objective of this disclosure is to provide an AI-based driver fatigue detection and warning system.

[0008] Another objective of this disclosure is to detect drowsiness caused by fatigue using artificial intelligence and visual analysis in real time, thus minimizing the risk of accidents.

[0009] Another objective of the present disclosure is to provide immediate, localized tactile warnings via ergonomic vibration motors embedded in the vehicle surfaces.

[0010] Another objective of the present disclosure is to log fatigue events for diagnostic purposes and to allow drivers to configure warning sensitivity and feedback patterns.

[0011] To further clarify the advantages and features of the present disclosure, a more detailed description of the invention will be given with reference to specific embodiments thereof illustrated in the accompanying drawings. It should be noted that these drawings represent only typical embodiments of the invention and are therefore not to be considered limiting its scope. The invention will be described and explained in additional detail and in greater detail with reference to the accompanying drawings. BRIEF DESCRIPTION OF THE CHARACTERS

[0012] These and other features, aspects, and advantages of the present disclosure will be better understood when the following detailed description is read with reference to the accompanying drawings, in which like characters represent like parts throughout. Fig. 1 shows a block diagram of an AI-based driver drowsiness detection and warning system according to an embodiment of the present disclosure; Fig. 2 shows a block diagram illustrating the operation of the proposed system according to an embodiment of the present disclosure; and Fig. 3 shows various views of an actuator system according to an embodiment of the present disclosure.

[0013] Furthermore, those skilled in the art will appreciate that elements in the drawings are shown for convenience and may not necessarily be drawn to scale. For example, the flowcharts illustrate the method by key steps to enhance understanding of aspects of the present disclosure. Moreover, with respect to device construction, one or more components of the device may have been represented in the drawings by conventional symbols, and the drawings may show only those specific details relevant to understanding embodiments of the present disclosure in order not to clutter the drawings with details that would be readily apparent to those skilled in the art who would benefit from the description herein. DETAILED DESCRIPTION:

[0014] To facilitate an understanding of the principles of the invention, reference will now be made to the embodiment illustrated in the drawings and described in specific language. It is to be understood, however, that this is not intended to limit the scope of the invention, as such changes and further modifications to the illustrated system and such further applications of the principles of the invention as illustrated therein are contemplated as would normally occur to one skilled in the art to which the invention pertains.

[0015] It will be understood by one skilled in the art that the foregoing general description and the following detailed description are exemplary and explanatory of the invention and are not intended to be limiting thereof.

[0016] References in this specification to "one aspect," "another aspect," or similar language mean that a particular feature, structure, or characteristic described in connection with the embodiment is included in at least one embodiment of the present disclosure. Therefore, occurrences of the phrase "in one embodiment," "in another embodiment," and similar language throughout this specification may or may not all refer to the same embodiment.

[0017] The terms "comprises," "comprising," or other variations thereof are intended to cover non-exclusive inclusion, such that a process or method comprising a list of steps not only includes those steps, but may also include other steps not expressly listed or inherent in such process or method. Likewise, one or more devices or subsystems or elements or structures or components preceded by "comprises...a" does not preclude, without further limitation, the existence of other devices or other subsystems or other elements or other structures or other components or additional devices or additional subsystems or additional elements or additional structures or additional components.

[0018] Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs. The system, methods, and examples provided herein are for illustrative purposes only and should not be considered limiting.

[0019] Embodiments of the present disclosure will be described in detail below with reference to the accompanying drawings.

[0020] The functional units described in this specification have been referred to as devices. A device may be implemented in programmable hardware devices such as processors, digital signal processors, central processing units, field-programmable gate arrays, programmable array logic, programmable logic devices, cloud processing systems, or the like. The devices may also be implemented in software for execution by various types of processors. An identified device may contain executable code and may, for example, comprise one or more physical or logical blocks of computer instructions, which may be organized, for example, as an object, procedure, function, or other construct.However, the executable file of an identified device need not be physically located together, but may comprise different instructions stored in different locations which, when logically linked together, constitute the device and fulfill the stated purpose of the device.

[0021] Indeed, executable code of a device or module may be a single instruction or multiple instructions, and may even be distributed across several different code segments, among different applications, and across multiple storage devices. Similarly, operational data may be identified and represented within the device and embodied in any suitable form and organized in any suitable type of data structure. The operational data may be captured as a single set of data or distributed across different locations, including different storage devices, and may exist, at least in part, as electronic signals in a system or network.

[0022] References in this specification to "a selected embodiment," "an embodiment," or "an embodiment" mean that a particular feature, structure, or characteristic described in connection with the embodiment is included in at least one embodiment of the disclosed subject matter. Therefore, the appearances of the phrases "a selected embodiment," "in an embodiment," or "in an embodiment" in various places in this specification do not necessarily refer to the same embodiment.

[0023] Furthermore, the described features, structures, or characteristics may be combined in any suitable manner in one or more embodiments. In the following description, numerous specific details are provided to provide a thorough understanding of embodiments of the disclosed subject matter. However, one of ordinary skill in the art will recognize that the disclosed subject matter may be practiced without one or more of the specific details, or with different methods, components, materials, etc. In other instances, well-known structures, materials, or operations are not shown or described in detail to avoid obscuring aspects of the disclosed subject matter.

[0024] According to the example embodiments, the disclosed computer programs or modules may be executed in many example ways, for example, as an application stored in the memory of a device or as a hosted application running on a server and communicating with the device application or browser using a variety of standard protocols such as TCP / IP, HTTP, XML, SOAP, REST, JSON, and other suitable protocols. The disclosed computer programs may be written in example programming languages ​​that execute from memory on the device or from a hosted server, for example, BASIC, COBOL, C, C++, Java, Pascal, or scripting languages ​​such as JavaScript, Python, Ruby, PHP, Perl, or other suitable programming languages.

[0025] Some of the disclosed embodiments comprise or otherwise involve data transmission over a network, such as communicating various inputs or files over the network. The network may comprise, for example, one or more of the following: the Internet, wide area networks (WANs), local area networks (LANs), analog or digital wired and wireless telephone networks (e.g., a PSTN, Integrated Services Digital Network (ISDN), a cellular network, and Digital Subscriber Line (xDSL)), radio, television, cable, satellite, and / or any other delivery or tunneling mechanism for transmitting data. The network may comprise multiple networks or subnetworks, each of which may comprise, for example, a wired or wireless data path. The network may comprise a circuit-switched voice network, a packet-switched data network, or any other network capable of transmitting electronic communications.For example, the network may include networks based on Internet Protocol (IP) or Asynchronous Transfer Mode (ATM), and may support voice using, for example, VoIP, Voice over ATM, or other comparable protocols used for voice data communications. In one implementation, the network includes a cellular network configured for the exchange of text or SMS messages.

[0026] Examples of the network include, but are not limited to, a Personal Area Network (PAN), a Storage Area Network (SAN), a Home Area Network (HAN), a Campus Area Network (CAN), a Local Area Network (LAN), a Wide Area Network (WAN), a Metropolitan Area Network (MAN), a Virtual Private Network (VPN), an Enterprise Private Network (EPN), the Internet, a Global Area Network (GAN), and so on.

[0027] Fig. 1 shows a block diagram of an AI-based driver drowsiness detection and warning system (100) according to an embodiment of the present disclosure.

[0028] With reference to Fig. 1, the system (10) comprises a vision system (12) comprising a camera (14) and an image pre-processor (16), wherein the vision system (12) is configured to capture facial images of the driver in real time via the camera (14) and pre-process the captured images via an image pre-processor (16); a server (18) operatively connected to the vision system (12) and configured to analyze the captured image using artificial intelligence and generate fatigue values, wherein the server (18) comprises a core processing module (20) trained using an artificial intelligence model to detect driver fatigue and generate fatigue values; a controller (22) embedded in the steering wheel cover and operatively connected to the server (18) and configured to process fatigue values ​​and activate tactile feedback;an actuator system (24) operatively connected to the controller (22) and comprising a multi-row array of button motors (26) controlled by addressable chips (27) to provide localized tactile feedback to the driver based on commands from the controller (22), the actuator system (24) being seamlessly integrated into the steering wheel cover; a user interface (28) comprising a dashboard display (30) along with a configuration panel (32) configured to display fatigue levels and system warnings in real time and to allow the driver to configure the system parameters; and a data storage module (34) comprising one or more storage means and configured to log fatigue events, tactile feedback patterns, and driver responses.

[0029] In one embodiment, the image preprocessor (16) of the vision system (12) is configured to adapt to different lighting conditions, optimize image contrast and brightness, stabilize image acquisition during vehicle movement, and filter out noise and artifacts from the acquired images.

[0030] In one embodiment, the core processing module (20) trained by an artificial intelligence model to detect driver fatigue is configured to detect: duration of eye closure, blink rate, variations in head position, yawn frequency, and changes in facial expression indicative of drowsiness.

[0031] In one embodiment, the controller (22) comprises: a microcontroller unit (MCU) (22a) configured to process fatigue data and generate motor control signals; a communications interface (22b) configured to receive data from the vision system (12) and server (18), transmit commands to the actuator system (24), and communicate with the user interface (28); and a mapping module (22c) configured to translate fatigue values ​​into specific vibration patterns.

[0032] In one embodiment, the actuator system (24) is configured to provide tactile feedback patterns via the button motors (26) based on commands from the controller (22), the actuator system (24) further comprising: vibration intensity control mechanisms; pattern sequencing modules; feedback verification sensors; and motor health monitoring systems. The tactile feedback patterns include: graduated intensity levels based on fatigue level; directional patterns indicating alarm urgency; variable frequency vibrations; and customizable pattern sequences depending on the driver's needs, wherein the configuration panel of the user interface allows the driver to customize the tactile feedback patterns.

[0033] In one embodiment, the dashboard display (30) of the user interface (28) is configured to display the following information: real-time fatigue levels, system status indicators, and warning history, and wherein the configuration panel (32) of the user interface (28) is configured to allow the driver to customize the following information: vibration intensity, warning thresholds, and feedback pattern preferences.

[0034] In one embodiment, the data storage module (34) comprises: local storage (34a) configured to maintain the following data: immediate access to current fatigue events; system performance logs; and driver response data; and cloud storage (34b) configured to synchronize data across multiple vehicles; enable fleet-wide analysis; and back up critical system data.

[0035] In one embodiment, the system (10) further comprises security mechanisms configured to monitor the integrity of the system components, detect malfunctions, initiate backup procedures, and maintain critical functions during a partial system failure.

[0036] In one embodiment, the system further comprises an emergency module (36) configured to implement emergency protocols, including: detecting persistent unresponsiveness; escalating alerts based on severity; notifying predetermined emergency contacts; and logging emergency events for analysis.

[0037] The present invention relates to a driver safety system that prevents accidents caused by fatigue. The system uses an AI-assisted vision module to monitor driver fatigue indicators such as eye closure and yawning. When fatigue is detected, a tactile feedback module integrated into the steering wheel provides precise vibration warnings. The system also includes a customizable user interface, data logging functions for performance analysis, and escalation mechanisms for unresponsive drivers to ensure comprehensive road safety.

[0038] Fig. 2 illustrates a block diagram showing the operation of the proposed system according to an embodiment of the present disclosure.

[0039] With reference to Fig. The functionality of the system is explained in detail below in Section 2. The diagram shows the core components and their connections in the driver drowsiness detection and warning system.

[0040] The vision system (100) acts as the primary input module for detecting driver fatigue. It uses a strategically placed camera (101), which can be mounted on the dashboard or rearview mirror, to capture real-time facial images of the driver. This placement ensures an unobstructed view of the driver's face and enables accurate data collection. To improve system reliability, an embedded image preprocessor (102) is integrated. This component improves image quality and ensures effective performance under varying lighting conditions, such as low light or strong glare. The vision system (100) analyzes key fatigue-related parameters, including eye closure duration, blink rate, head position, and yawn frequency. Together, these indicators enable the system to accurately detect signs of drowsiness in real time.

[0041] The server (200) serves as the central processing unit of the driver fatigue detection system. It uses AI-driven algorithms, including convolutional neural networks (CNNs), to analyze facial data captured by the vision system (100) and detect signs of fatigue. This analysis results in the assignment of a "drowsiness score" representing the driver's current fatigue level. The server also generates actionable data that is transmitted to the controller (300) to initiate appropriate responses within the system. The server includes essential components to ensure its functionality. The AI ​​model (201) is a critical element, trained on various datasets to enable real-time fatigue detection with high accuracy. This extensive training ensures that the model can adapt to different driver behaviors and environmental conditions.Furthermore, the real-time processing module (202) is integrated into the server to minimize latency in analysis and response times. This module ensures that fatigue detection and subsequent actions are performed quickly, thus maintaining the safety and reliability of the system under real-time driving conditions.

[0042] The controller (300) serves as the operational hub of the driver fatigue detection system. Embedded in the steering wheel cover, it encompasses all essential communication systems and orchestrates the functionality of the entire system. Through seamless interfacing with all components, including the vision system (100), the server (200), the actuator system (400), and the user interface (500), the controller (300) ensures consistent operation and precise response to fatigue data. The controller (300) includes a microcontroller unit (MCU) (301), which plays a critical role in interfacing with the actuator system (400). This connection enables the activation and control of tactile feedback mechanisms based on fatigue levels detected and processed by other system components.In addition, the controller (300) has a communication interface (302) that enables real-time data exchange with the vision system (100), the server (200), and the user interface (500). This interface ensures that information flows efficiently and safely through the system and supports accurate detection and timely feedback. One of the most important functions of the controller (300) is its ability to associate fatigue levels with specific vibration patterns. After receiving actionable fatigue values ​​and corresponding feedback commands from the server (200), the controller (300) processes this data and generates precise instructions for the actuator system (400). These instructions determine the vibration patterns that are activated to provide tactile feedback to the driver, ensuring an effective response to detected fatigue and contributing to increased road safety.

[0043] Fig. 3 shows various views of an actuator system according to an embodiment of the present disclosure.

[0044] In Fig. Figure 3 shows (a) the isometric view, (b) the top view, (c) the front view, (d) the bottom view, (e) the left view, and (f) the right view of the actuator system.

[0045] The actuator system (400), as in Fig.3, is responsible for delivering tactile feedback to the driver and utilizes a multi-row array of button motors (401), each connected to addressable chips (402). This design enables precise, localized vibrations to effectively alert the driver in real time. The actuator system is seamlessly integrated into the steering wheel cover, ensuring an ergonomic design that provides immediate feedback. The motor array within the actuator system is embedded into ergonomic surfaces such as the steering wheel or seat cover, allowing for optimal placement and usability. This integration ensures that the tactile feedback is both effective and non-intrusive, contributing to the overall functionality of the driver safety system. Furthermore, the feedback patterns generated by the actuator system are highly customizable.These patterns include gentle pulsing, rolling vibrations, or escalating warnings tailored based on the severity of driver fatigue detected. This capability ensures that the system delivers an appropriate response to varying levels of driver fatigue, increasing its effectiveness in preventing accidents and promoting road safety.

[0046] The user interface (500) serves as a driver interaction portal, allowing drivers to monitor the system, adjust its settings, and review performance data. This interface is designed to enhance user engagement and provide real-time insights into the system's operation. The dashboard display (501) is a critical component of the interface, displaying fatigue levels and system warnings in an intuitive and accessible format. Furthermore, the configuration panel (502) provides drivers with the ability to adjust vibration intensity and customize feedback patterns to their personal preferences. By integrating these features, the user interface ensures that the system remains user-friendly, adaptable, and effective in counteracting driver fatigue and maintaining road safety.

[0047] The system includes a data storage module (600) designed to log and analyze fatigue events, tactile feedback patterns, and driver responses. This capability is critical for diagnostic purposes and fleet management operations. The module offers two primary storage options: local storage (601), which provides instant access to the data in the vehicle, and cloud storage (602), an optional feature for centralized data management in fleet operations. The data storage module (600) performs several important functions. It logs all driver fatigue events detected by the system, records motor actuation patterns used for tactile feedback, and maintains a detailed record of system responses.This comprehensive data logging allows fleet managers or individual drivers to review historical data, gain insights into driver behavior, or conduct post-incident investigations. The module ensures that all data is accessible for analysis, contributing to improved system reliability and operational safety.

[0048] The system described consists of 100 (vision system) to 600 (data storage) interconnected components that ensure smooth operation, detect driver fatigue, and provide appropriate tactile feedback. The system works as follows: The vision system (100) captures facial images of the driver in real time using a camera (101) positioned to ensure unobstructed visibility of the driver's face. The images are preprocessed by the image preprocessor (102), which improves their quality for analysis. This preprocessed data is then transmitted to the server (200) for further analysis. The data is sent via a high-speed wired or wireless connection to ensure minimal latency.

[0049] The server (200) uses an AI model (201) and a real-time processing module (202) to analyze the image data for fatigue indicators such as eye closure, yawning, and head tilting and calculates a fatigue score. This fatigue score, along with recommended feedback patterns, is then transmitted to the controller (300) for further processing. Data transmission can use a Controller Area Network (CAN) bus or a similar protocol to ensure efficient and secure data exchange.

[0050] The controller (300) embedded in the steering wheel cover receives the fatigue values ​​from the server (200) and assigns these values ​​to specific tactile feedback patterns. The controller then transmits the corresponding actuation commands to the actuator system (400), which includes button motors (401) and addressable chips (402). These commands are transmitted via a dedicated control signal, ensuring precise activation of specific motors in the array to provide tactile feedback.

[0051] The controller (300) also communicates with the user interface (500) to provide real-time updates on system status, drowsiness warnings, and the ability to customize feedback options. The information, including current drowsiness levels, engine activation patterns, and system logs, is displayed on the dashboard display (501) or can be accessed via the configuration panel (502), allowing the driver to adjust settings as needed.

[0052] The controller (300) logs all fatigue events, engine activations, and driver responses in the data logger (600). This data is stored in local storage (601) for immediate in-vehicle access, while the cloud storage (602) (optional) provides fleet operators with centralized data management. The logged data can be transferred regularly or in real time, depending on the system configuration, allowing fleet managers to monitor driver fatigue and system performance.

[0053] The user interface (500) can retrieve historical data from the data store (600) for analysis and display. This data includes past fatigue events and system performance logs. The bidirectional data exchange between the user interface and the data store allows users to access and update system logs or settings, providing greater flexibility and control.

[0054] The actuator system (400) provides tactile feedback to the driver based on commands from the controller (300). The system may include sensors embedded in the steering wheel cover to monitor the driver's response to the feedback. This data is sent back to the controller (300) for further adjustment, ensuring a closed-loop communication system that allows for real-time feedback adjustments for optimal effectiveness. This feedback loop helps ensure the driver receives appropriate and responsive drowsy warnings.

[0055] The driver fatigue detection system is designed to improve vehicle safety by monitoring driver fatigue levels and providing immediate feedback to prevent accidents. It consists of several key components, beginning with a vision system that captures real-time facial images of the driver to detect signs of fatigue. This data is then processed by a server that uses an AI model to analyze the images and generate actionable fatigue scores that help determine the driver's alertness level. The system includes a controller embedded in the steering wheel cover, which processes the fatigue scores and triggers the actuator system. The actuator system provides localized tactile feedback via a motor array also embedded in the steering wheel cover, providing subtle physical warnings to the driver.Additionally, a user interface for real-time monitoring and adjustment is integrated, allowing the driver to adjust settings as needed. The data storage module logs fatigue events and system responses for future analysis, ensuring that system performance can be reviewed and optimized over time.

[0056] The vision system includes a camera and image preprocessor, ensuring high-quality image capture for accurate drowsiness detection even in low-light environments. The AI ​​model in the server is specifically trained to detect drowsiness indicators such as eye closure, yawning, and head tilting, which are common signs of sleepiness. The tactile feedback mechanism is carefully designed and uses a multi-row array of button motors controlled by addressable chips for precise, localized actuation. The controller unit generates customizable vibration patterns based on the severity of detected drowsiness, enabling different warning intensity levels. The motor array is ergonomically embedded into strategic vehicle surfaces such as the steering wheel and seat upholstery to ensure that feedback is effectively delivered to the driver without causing distraction or discomfort.

[0057] The system also features a user interface that allows the driver to configure alarm sensitivity and vibration patterns, allowing the system to be tailored to individual preferences and comfort levels. This versatility makes it suitable for a wide range of vehicles, from passenger cars to commercial vehicles, and provides an effective fatigue monitoring solution in various industrial applications, including logistics and ride-hailing.

[0058] The proposed system incorporates a variety of security mechanisms, including fail-safe operation, data protection compliance, and emergency alerts. In fail-safe mode, the system is configured redundantly to ensure continuous operation. In the event of a malfunction, the data storage module logs the event for later analysis. The system is also equipped with redundant power sources to ensure it remains operational even during power outages. The system ensures data protection compliance by performing all image processing locally in the vehicle. This design prevents the leakage of confidential data and ensures that the driver's personal data and facial images remain protected. If the system detects an unresponsive driver, it triggers emergency alerts to notify specific contacts, such as emergency contacts or fleet managers.This mechanism ensures an immediate response in the event of the driver being unable to act due to fatigue.

[0059] The proposed system offers several advantages, including improved safety, non-intrusive warnings, customizable features, scalability and integration, as well as privacy and autonomy. The system addresses the critical issue of driver drowsiness and contributes to reducing traffic accidents and fatalities. By detecting signs of fatigue early and alerting the driver, it significantly contributes to improving road safety. The system provides non-intrusive, effective warnings through the tactile feedback mechanism. These warnings ensure that the driver is immediately informed of drowsiness without distracting or startling them, making the system both efficient and driver-friendly. The system offers customizable settings that allow drivers to adjust the warning sensitivity and vibration patterns to their individual preferences and comfort.This adaptation ensures that the system meets different driver needs and improves the user experience and effectiveness. The system's modular design allows for easy integration into a wide range of vehicle models, from passenger cars to commercial vehicles. Its scalability ensures that it can be adapted for use in different vehicle types, making it suitable for a wide variety of applications. The system operates independently and ensures that no data is transmitted outside the vehicle, helping to protect driver privacy. This design allows the driver to retain control over their data and autonomy while benefiting from the system's safety features.

[0060] The driver fatigue detection and warning system is versatile and can be used in various industries. It is suitable for use in the automotive sector, including passenger cars, commercial vehicles, and public transport systems. It is also highly applicable in the logistics and freight industry, especially for trucks and delivery vehicles, where drivers often work long hours. Furthermore, the system can be integrated into ride-sharing services to ensure the safety of drivers and passengers in ride-sharing vehicles.

[0061] The drawings and the foregoing description provide examples of embodiments. Those skilled in the art will recognize that one or more of the described elements may well be combined into a single functional element. Alternatively, certain elements may be separated into multiple functional elements. Elements from one embodiment may be added to another embodiment. For example, orders of the processes described herein may be changed and are not limited to the manner described herein. Furthermore, the actions of any flowchart need not be implemented in the order shown; nor do all actions necessarily need to be performed. Also, those actions that are not dependent on other actions may be performed in parallel with the other actions. The scope of the embodiments is in no way limited by these specific examples.Numerous variations, whether explicitly stated in the specification or not, such as differences in structure, dimensions, and material use, are possible. The scope of the embodiments is at least as broad as indicated in the following claims.

[0062] Advantages, other benefits, and solutions to problems have been described above with reference to specific embodiments. However, the advantages, benefits, solutions to problems, and any components that may cause an advantage or solution to occur or become more apparent are not to be construed as a critical, required, or essential feature or component of any or all of the claims. REFERENCES 100 image processing system 101 Camera 102 Image processor 12 Vision system 14 Camera 16 Image preprocessor 18 servers 20 Core processing module 22 controllers 22a Microcontroller unit (MCU) 22b Communication interface 22c Mapping Module 24 Actuators 26 Multi-row arrangement of button motors 27 Addressable Chips 28 User interface 30 Dashboard display 32 Configuration panel 34 Data storage module 34a Local storage 34b Cloud storage 200 servers 201 AI model 202 Real-time processing module 300 controllers 301 MCU 302 Communication interface 400 actuator system 401 button motors 402 addressable chips 500 user interface 501 dashboard display 502 Configuration panel 600 data storage 601 Local Storage 602 cloud storage

Claims

[1] An AI-based driver fatigue detection and warning system consisting of: a vision system comprising a camera and an image pre-processor, the vision system configured to capture facial images of the driver in real time via the camera and pre-process the captured images via an image pre-processor; a server operatively connected to the vision system and configured to analyze the captured image using artificial intelligence and generate fatigue scores, the server comprising a core processing module trained using an artificial intelligence model to detect driver fatigue and generate fatigue scores; a controller embedded in the steering wheel cover, operatively connected to the server and configured to process fatigue values ​​and activate tactile feedback; an actuator system operatively connected to the controller and consisting of a multi-row array of button motors controlled by addressable chips to provide localized tactile feedback to the driver based on commands from the controller, the actuator system being seamlessly integrated into the steering wheel cover; a user interface consisting of a dashboard display and a configuration panel, configured to display fatigue levels and system warnings in real time and to allow the driver to configure the system parameters; and a data storage module comprising one or more storage means and configured to log fatigue events, tactile feedback patterns, and driver responses. [2] The system of claim 1, wherein the image preprocessor of the vision system is configured to adapt to different lighting conditions, optimize image contrast and brightness, stabilize image acquisition during vehicle movement, and filter out noise and artifacts from acquired images. [3] The system of claim 1, wherein the core processing module trained by an artificial intelligence model, an artificial intelligence model for detecting driver fatigue, is configured to detect the following data: duration of eye closure, blink rate, variations in head position, yawn frequency, and changes in facial expression indicative of drowsiness. [4] The system of claim 1, wherein the controller comprises: a microcontroller unit (MCU) configured to process fatigue data and generate motor control signals; a communications interface configured to receive data from the vision system and server, transmit commands to the actuator system, and communicate with the user interface; and a mapping module configured to translate fatigue values ​​into specific vibration patterns. [5] The system of claim 1, wherein the actuator system is configured to provide tactile feedback patterns via the button motors based on commands from the controller, the actuator system further comprising: vibration intensity control mechanisms, pattern sequencing modules, feedback verification sensors, and motor integrity monitoring systems. [6] The system of claim 5, wherein the tactile feedback patterns include: graduated intensity levels based on the severity of fatigue; directional patterns indicating the urgency of alerting; variable frequency vibrations; and customizable pattern sequences depending on the driver's needs, wherein the configuration panel of the user interface allows the driver to customize the tactile feedback patterns. [7] The system of claim 1, wherein the dashboard display of the user interface is configured to display the following information: real-time fatigue levels, system status indicators, and warning history, and wherein the configuration panel of the user interface is configured to allow the driver to customize the following information: vibration intensity, warning thresholds, and feedback pattern preferences. [8] The system of claim 1, wherein the data storage module comprises: local storage configured to maintain immediate access to current fatigue events, system performance logs, and driver response data; and cloud storage configured to synchronize data across multiple vehicles, enable fleet-wide analysis, and back up critical system data. [9] The system of claim 1, further comprising safety mechanisms configured to monitor the state of the system components, detect malfunctions, initiate backup procedures, and maintain critical functions during a partial system failure. [10] The system of claim 1, wherein the system further comprises an emergency module configured to implement emergency protocols, including: detecting persistent unresponsiveness; escalating alerts based on severity; notifying predetermined emergency contacts; and logging emergency events for analysis.

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