![References
[2] Aussat, Yerbol, Ansis Rosmanis, and Srinivasan Keshav. "A Power-Efficient
Self-Calibrating Smart Lighting System." Energy and Buildings 111, no. 874
(2022): 1–10. https://doi.org/10.1016/j.enbuild.2022.111874.
[1]Widartha, V.P., I. Ra, S.-Y. Lee, and C.-S. Kim. "Advancing Smart Lighting: A
Developmental Approach to Energy Efficiency through Brightness Adjustment
Strategies." Journal of Low Power Electronics and Applications 14, no. 1 (2024):
6. https://doi.org/10.3390/jlpea14010006.
[3]Mahmoud, M. "Automated Smart Utilization of Background Lights and
Daylight for Green Building Efficient and Economic Indoor Lighting Intensity
Control." Intelligent Control and Automation 12, no. 1 (2021): 1–15.
https://doi.org/10.4236/ica.2021.121001.
[4]Chiradeja, Pathomthat, and Suntiti Yoomak. "Development of Public Lighting
System with Smart Lighting Control Systems and Internet of Thing (IoT)
Technologies for Smart City." Energy Reports 9 (2023): 27–35.
https://doi.org/10.1016/j.egyr.2023.10.027.](https://image.slidesharecdn.com/pptteam4-250524054928-3ac8b357/85/ppt-team-4-pptx-system-management-with-auto-brightness-21-320.jpg)
![References
[5]Safaei, Danial, Ali Sobhani, and Ali Akbar Kiaei. "DeePLT: Personalized
Lighting Facilitated by Trajectory Prediction of Recognized Residents in the Smart
Home." arXiv (2023): 1–12. https://doi.org/10.48550/arXiv.2304.08027.
[6]Vashishtha, Kritika, Anas Saad, Reza Faieghi, and Fengfeng Xi. "Reinforcement
Learning Adaptive Fuzzy Controller for Lighting Systems: Application to Aircraft
Cabin." arXiv (2023): 1–15. https://doi.org/10.48550/arXiv.2310.00525.
[7]Obioma, Peace, Obinna Agbodike, Jenhui Chen, and Lei Wang. "ISLS: An IoT-
Based Smart Lighting System for Improving Energy Conservation in Office
Buildings." arXiv (2025): 1–14. https://doi.org/10.48550/arXiv.2503.13474.
[8]Tsesmelis, Theodore, Irtiza Hasan, Marco Cristani, Alessio Del Bue, and Fabio
Galasso. "Human-Centric Light Sensing and Estimation from RGBD Images: The
Invisible Light Switch." arXiv (2019): 1–10.
https://doi.org/10.48550/arXiv.1901.10772.](https://image.slidesharecdn.com/pptteam4-250524054928-3ac8b357/85/ppt-team-4-pptx-system-management-with-auto-brightness-22-320.jpg)
ppt team 4.pptx system management with auto brightness
- 1. OM SAKTHI ADHIPARASAKTHI ENGINEERING COLLEGE MELMARUVATHUR - 603319 DEPARTMENT OF COMPUTER SCIENCE AND ENGINEERING CS3811 Project Work /Internship Anna University Viva Voce Examination 24-05-2025 Semester 8 SMART LIGHT MANAGEMENT SYSTEM WITH AUTO BRIGHTNESS ADJUSTMENT Presented By, GANESH N 420421104019 GUGANESH R 420421104025 JAYAPRAKASHV 420421104031 TAMILSELVAN A 420421104083 Guided By, Mr. G. SRINIVASAN, M.E., Assistant Professor Department of Computer Science and Engineering
- 2. Introduction The auto brightness lamp is a smart lighting solution that automatically adjusts its illumination based on the ambient light conditions in your surroundings. This innovative lamp ensures your space is always perfectly lit, providing both convenience and energy efficiency This helps to reduce energy consumption during the day and minimizes glare at night, thereby improving safety and sustainability.
- 3. Problem Statement • As per the AHO (Automatic Headlamp On) rule enforced from April 2017, two-wheeler headlights must remain ON at all times to enhance road safety. • Constant headlight brightness leads to unnecessary battery power consumption, especially during well-lit areas or during mornings headlights are often not required. • At night, excessive brightness can cause glare, potentially disturbing other drivers and increasing the risk of accidents.
- 4. Objectives • The objective of this project is to develop a Smart Lighting System for Vehicles that automatically adjusts the brightness of the headlights based on surrounding ambient light conditions. • This system aims to Enhance road Safety, Reduce Energy Consumption, and Improve Driving Comfort by optimizing light intensity according to real-time environmental changes.
- 5. Literature Survey Title &Year Tool/Technology Comparison with Proposed Solution Gaps in Existing System Automatic Brightness Control of LED Lights Based on Ambient Light and Human Presence (2021) LDR, PIR sensors, Arduino Focuses on ambient light and human presence; your project targets vehicle lights and web-based control No dedicated manual override or full brightness mode for vehicles A Smart LED Control System for Vehicular Applications (2022) ESP32, sensors Designed for vehicles like yours, includes manual override No customizable brightness level via user interface
- 6. Literature Survey Intelligent Lighting System for Smart Infrastructure (2023) Microcontroller, Sensors Uses multiple lighting modes, similar to your system Not focused on vehicle use case; lacks web-based UI Design of an Automatic Light Control System Using ESP32 andWeb Interface (2023) ESP32,Web interface Closely resembles your system with web UI and mode switching No mention of vehicle adaptation or voltage compatibility IoT-Based Smart Lamp with User- Defined Brightness Profiles (2024) Wi-Fi, Microcontroller Offers brightness profiles based on user input, aligns with manual mode in your system Too focused on static lamp use, not real-time vehicle adjustment
- 7. Proposed System • Auto Brightness Lamp for Vehicle. • The Auto Brightness Lamp automatically adjusts headlamp brightness based on surrounding light using an LDR sensor and ESP32 microcontroller. It also allows users to control brightness remotely via the Website, providing better visibility and energy efficiency. • Advantages: Better Safety, Energy Saving, Remote Control, Supports AHO Rule, Eco-Friendly
- 9. System Modules Module 1 : Sensor Data Collection Module Gathers real-time light intensity data using an LDR sensor to detect surrounding brightness conditions. Module 2 : Control Logic & Decision Module Processes the sensor input and decides whether to turn the LED ON or OFF, or adjust brightness, based on predefined thresholds or user settings. Module 3 : Simulation & Validation Module Simulates circuit behavior using software tools to validate sensor readings, control logic, and relay actions before physical implementation.
- 10. System Modules Module 4 : Hardware Integration Module Integrates all components — LDR sensor, ESP32, Relay module, and LED — into a working circuit and ensures real-time interaction with the environment. Module 5 : Wi-Fi Communication & Manual Override Module Enables remote access via WiFi for the user to manually override or customize brightness settings through a web or mobile interface. Module 6 : Deployment & Testing Module Installs the system onto the vehicle and tests in real-world conditions like day, night, tunnels, and varying speeds to ensure reliable performance.
- 11. Methodology / Technology Used Technologies & Tools Used: Micro Python, ESP32, LDR Sensor, HTML, CSS, JS, Wi-Fi module (inbuilt in ESP32), ADC and PWM modules. Methodology Followed: SDLC (Software Development Life Cycle) – Waterfall Model – step by step development : Requirement, Design, Implementation, Testing, Deployment. Frameworks / Platforms: Thonny IDE, Local Web Server (ESP32), Socket Programming.
- 12. Implementation • Start the System Power on ESP32 and initialize LDR, LED, and Wi-Fi. Start the web server for user access. • Mode Selection User selects a mode via the web: Auto, Manual, or Full Brightness. i. Auto Brightness Mode Reads ambient light using LDR. Adjusts LED brightness automatically via PWM. ii. Manual Brightness Mode User sets brightness with a slider. ESP32 applies it directly using PWM.
- 13. iii. Auto Brightness Off Mode Ignores sensor and sets LED to full brightness. PWM is fixed at maximum value. • Real-Time Monitoring System checks sensor/user input continuously. Updates LED brightness instantly. • Local Web Interface User connects via Wi-Fi to control modes. Interface runs directly on ESP32. Implementation
- 14. Results and Testing Auto Mode:
- 15. Results and Testing Manual Mode:
- 16. Results and Testing Full Brightness Mode(Auto off):
- 18. Challenges Faced Sensor Calibration (LDR) LDR values were inconsistent in different environments (e.g., indoor, outdoor, night, tunnel). Web Interface Communication with ESP3 Difficulty in syncing the website controls with the ESP32 over Wi-Fi. Voltage Regulation Direct battery connection caused voltage instability for ESP32. Wi-Fi Connectivity Issues ESP32 failed to connect reliably to the Wi-Fi network.
- 19. Future Enhancements • Integrate more sensors like rain and motion detectors. • Add automatic high/low beam switching for oncoming traffic. • Develop a mobile app for remote control and monitoring. • Implement energy-saving algorithms and solar power support.
- 20. Conclusion The Auto Brightness Lamp for Vehicles successfully adjusts headlamp brightness based on ambient light, improving safety and energy efficiency. The system also allows manual control via Wi-Fi for user flexibility. It ensures better visibility in varying conditions. Overall, it’s a smart, reliable, and user-friendly solution.
- 21. References [2] Aussat, Yerbol, Ansis Rosmanis, and Srinivasan Keshav. "A Power-Efficient Self-Calibrating Smart Lighting System." Energy and Buildings 111, no. 874 (2022): 1–10. https://doi.org/10.1016/j.enbuild.2022.111874. [1]Widartha, V.P., I. Ra, S.-Y. Lee, and C.-S. Kim. "Advancing Smart Lighting: A Developmental Approach to Energy Efficiency through Brightness Adjustment Strategies." Journal of Low Power Electronics and Applications 14, no. 1 (2024): 6. https://doi.org/10.3390/jlpea14010006. [3]Mahmoud, M. "Automated Smart Utilization of Background Lights and Daylight for Green Building Efficient and Economic Indoor Lighting Intensity Control." Intelligent Control and Automation 12, no. 1 (2021): 1–15. https://doi.org/10.4236/ica.2021.121001. [4]Chiradeja, Pathomthat, and Suntiti Yoomak. "Development of Public Lighting System with Smart Lighting Control Systems and Internet of Thing (IoT) Technologies for Smart City." Energy Reports 9 (2023): 27–35. https://doi.org/10.1016/j.egyr.2023.10.027.
- 22. References [5]Safaei, Danial, Ali Sobhani, and Ali Akbar Kiaei. "DeePLT: Personalized Lighting Facilitated by Trajectory Prediction of Recognized Residents in the Smart Home." arXiv (2023): 1–12. https://doi.org/10.48550/arXiv.2304.08027. [6]Vashishtha, Kritika, Anas Saad, Reza Faieghi, and Fengfeng Xi. "Reinforcement Learning Adaptive Fuzzy Controller for Lighting Systems: Application to Aircraft Cabin." arXiv (2023): 1–15. https://doi.org/10.48550/arXiv.2310.00525. [7]Obioma, Peace, Obinna Agbodike, Jenhui Chen, and Lei Wang. "ISLS: An IoT- Based Smart Lighting System for Improving Energy Conservation in Office Buildings." arXiv (2025): 1–14. https://doi.org/10.48550/arXiv.2503.13474. [8]Tsesmelis, Theodore, Irtiza Hasan, Marco Cristani, Alessio Del Bue, and Fabio Galasso. "Human-Centric Light Sensing and Estimation from RGBD Images: The Invisible Light Switch." arXiv (2019): 1–10. https://doi.org/10.48550/arXiv.1901.10772.
- 23. Q & A