| Outcomes |
- **Course Learning Outcomes: Implementation of Biometric Authentication System on Automobile** ### Overall Course Outcome By the end of this course, participants will be able to **design, develop, integrate, and validate a secure, reliable, and production-ready biometric authentication system for modern automobiles**, meeting automotive industry standards for safety, cybersecurity, and user experience. ### Specific Learning Outcomes #### 1. Knowledge Outcomes (What you will understand) - Explain the principles, strengths, limitations, and suitability of various biometric modalities (fingerprint, facial, iris, voice, ECG, vein pattern, gait, etc.) in automotive environments. - Describe automotive electronic architectures and how biometric systems integrate with ECUs, CAN/LIN/Ethernet networks, immobilizers, and ADAS. - Understand key standards: **ISO/SAE 21434** (Cybersecurity), **ISO 26262** (Functional Safety), **UNECE WP.29**, and data privacy regulations (GDPR, CCPA). - Analyze real-world challenges such as environmental variations, spoofing attacks, sensor degradation, and user diversity. #### 2. Technical & Practical Skill Outcomes (What you will be able to do) - Select, interface, and calibrate appropriate biometric sensors for vehicle integration (door handles, steering wheel, dashboard, etc.). - Develop embedded software (C/C++) for biometric data acquisition, feature extraction, template matching, and liveness detection. - Implement secure biometric template storage and matching using encryption, TPM/HSM, and anti-tampering techniques. - Build multi-modal and multi-factor authentication systems with fallback mechanisms. - Integrate the biometric system with vehicle functions: door access, engine start, driver personalization (seat, mirrors, climate, infotainment), and safety features. - Use automotive tools (CANoe, Vector, oscilloscopes) to test and debug communication between biometric modules and vehicle networks. - Perform security testing: spoofing attacks, side-channel analysis, and penetration testing of biometric systems. - Apply machine learning / deep learning (TensorFlow Lite, OpenCV) for improved accuracy and adaptation in real driving conditions. #### 3. Professional & Soft Skill Outcomes - Design user-centric systems while addressing privacy, ethical, and inclusivity concerns. - Conduct risk assessments and develop mitigation strategies for biometric system failures. - Work effectively in cross-functional teams (hardware, software, cybersecurity, design). - Document and present technical designs compliant with automotive development processes (V-model, ASPICE). - Evaluate the business and societal impact of biometric authentication in future mobility (autonomous vehicles, car-sharing, fleet management). ### Measurable / Demonstrable Outcomes (Capstone Project) Upon successful completion, you will have: - A fully functional prototype of a multi-modal biometric vehicle access and start system. - Complete technical documentation (requirements, architecture, test reports, security analysis). - A professional presentation and demonstration ready for industry review or academic defense. - Portfolio piece showcasing embedded development, AI integration, and automotive cybersecurity skills. ### Career & Industry Outcomes Graduates of this course will be equipped to: - Join leading automotive OEMs (BMW, Mercedes, Tesla, Toyota, etc.) as Biometric Systems Engineers. - Work with Tier-1 suppliers (Bosch, Continental, Denso, Harman) on next-generation security solutions. - Contribute to autonomous vehicle and connected car projects. - Pursue roles in automotive cybersecurity consultancies or research institutions. - Add significant value in the growing “Software-Defined Vehicle” (SDV) and biometric mobility market. **Certification Statement** Participants who successfully complete all assessments and the capstone project will receive a formal certificate stating they have achieve
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| Requirements |
- **Course Requirements: Implementation of Biometric Authentication System on Automobile** ### 1. Academic & Educational Requirements - **Minimum Qualification**: Bachelor’s degree (or equivalent) in one of the following fields: - Automotive Engineering - Electrical/Electronics Engineering - Mechanical/Mechatronics Engineering - Computer Science or Computer Engineering - Embedded Systems / Cybersecurity - **Alternative Entry**: Diploma holders with 3+ years of relevant industry experience in automotive electronics or embedded systems may be considered (subject to interview/portfolio review). ### 2. Technical Prerequisites (Must-Have Knowledge) | Area | Required Knowledge | |-------------------------------|------------------------------------------------------------------------------------| | Embedded Systems | Microcontrollers (ARM, STM32, ESP32), GPIO, interrupts, PWM | | Programming | Strong C/C++ skills; Python (intermediate) | | Automotive Networks | CAN bus, LIN, Automotive Ethernet basics | | Electronics | Basic analog & digital circuits, sensors & actuators | | Operating Systems | Real-Time Operating Systems (RTOS) fundamentals | | Security | Basic cryptography, encryption concepts | **Nice-to-Have (Advantageous)**: - Machine Learning / Deep Learning basics - Computer Vision (OpenCV) - Signal Processing - Automotive Functional Safety (ISO 26262) - Automotive Cybersecurity (ISO/SAE 21434) ### 3. Hardware Requirements (For Hands-on Labs & Projects) **Minimum Setup (Student must have or arrange access):** - Laptop/Desktop: Windows 11 / Linux (Ubuntu 22.04+), 16 GB RAM minimum, 500 GB SSD - Development Board: STM32 Nucleo / ESP32 DevKit / Arduino Due (or equivalent) - Biometric Sensors: - Fingerprint module (e.g., AS608 or R307) - Camera module (OV2640 or higher for facial recognition) - Microphone for voice recognition - CAN Bus Tools: USB-CAN adapter (e.g., PCAN-USB or cheap MCP2515 module) - Power Supply, Breadboards, Jumper wires, Multimeter **Recommended (for advanced projects):** - Automotive-grade ECU simulator or real vehicle test bench (provided in lab/hybrid mode) - High-quality biometric modules (e.g., Bosch or Goodix fingerprint sensors) - Raspberry Pi 5 (for edge AI processing) ### 4. Software Requirements - **Development Tools**: - STM32CubeIDE or Keil MDK - VS Code + PlatformIO - Python 3.10+ with libraries: OpenCV, TensorFlow Lite, NumPy, SciPy - **Automotive Tools**: - CANoe / CANalyzer (student license) - Vector tools or open-source alternatives - **Simulation Software**: - MATLAB & Simulink (Automotive Toolbox) - Proteus or Tinkercad for circuit simulation - **Version Control**: Git & GitHub ### 5. Time & Commitment Requirements - **Weekly Time Commitment**: 8–12 hours (4–6 hours theory + 4–6 hours practical) - **Attendance**: Minimum 75% for live/online sessions - **Language Proficiency**: Good command of English (all materials and instruction in English) ### 6. Additional Requirements - **Operating System**: Dual boot or virtual machine with Linux strongly recommended - **Internet**: Stable high-speed internet (for online mode and cloud-based ML training) - **Safety & Ethics**: Students must agree to follow ethical guidelines regarding biometric data handling - **Legal**: Students are responsible for complying with local data protection laws when working with real biometric data ### 7. For Online / Hybrid Participants - Webcam and microphone required for interactive sessions and proctored assessments - Access to cloud lab environments will be provided for those without full hardware
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