Advanced Driver Assistance System (ADAS) Implementation

🎥 Demo Videos

• 🎬 Introduction

• 🔄 Round Movement

• ⬆️ Front Movement

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❓ What is ADAS?

Advanced Driver Assistance Systems (ADAS) are intelligent systems that help drivers by providing enhanced safety and driving experience. These systems utilize sensors, cameras, and control algorithms to:

• Detect potential hazards
• Automate certain driving functions
• Provide critical information to the driver
• Take preventive actions when necessary

🤔 Why ADAS?

Key reasons for ADAS development:

• Safety: Reduces human error (cause of 94% of accidents - NHTSA)
• Efficiency: Optimizes driving patterns for fuel/energy savings
• Comfort: Reduces driver fatigue in traffic/long drives
• Regulation: Meets evolving automotive safety standards
• Foundation: Building block for autonomous vehicles

💡 Use Cases of ADAS

1. Collision Avoidance (Implemented)
2. Lane Keeping Assist (Implemented)
3. Automatic Parking (Implemented)
4. Adaptive Cruise Control
5. Blind Spot Detection
6. Driver Drowsiness Detection

⚖️ Manual Driving vs ADAS

Feature	Manual Driving	ADAS Assisted Driving
Obstacle Detection	Human reaction time	Real-time ultrasonic sensing
Decision Making	Driver-only	Sensor data + microcontroller logic
Parking Assistance	Manual estimation	Automated distance-based logic
Speed Control	Driver input	Controlled by predefined safety logic
Alerts & Warnings	Horns / lights	Automated buzzer + LED alerts

🎯 Project Objectives

This project demonstrates a basic ADAS implementation using an Arduino-compatible microcontroller and sensor suite to simulate:

• Obstacle detection and avoidance
• Emergency braking
• Lane-following using IR sensors
• Automatic parking mode
• Speed control and mode switching (Auto, Manual, Parking)
• Bluetooth support for command handling

🛠️ System Capabilities

• Autonomous Mode: Line following with obstacle avoidance
• Manual Mode: Future support for remote/Bluetooth control
• Parking Mode: Forward parking with obstacle-based halting
• Obstacle Avoidance: Left/right scanning and intelligent redirection
• Emergency Stop: Immediate halt when obstacle is too close
• Speed Control: Adjustable auto speed via commands
• Alerts: Buzzer and LED for event signaling

⚙️ Implementation Requirements

🔌 Hardware:

• Arduino Uno or compatible microcontroller
• L298N Motor Driver x2
• Ultrasonic Sensor (HC-SR04)
• IR Sensors x2
• Servo Motor (for directional scanning)
• Buzzer
• LED
• DC Motors (Left & Right wheels)
• Bluetooth Module (e.g., HC-05)
• Power Supply (Battery or USB)
• Chassis & Wheels

💻 Software

• Arduino IDE

• Libraries:

  • Servo.h
  • L298N.h (external motor driver library)
  • NewPing.h
  • SoftwareSerial.h (for Bluetooth)

🧩 Software Architecture

Key Functional Blocks:

1. Sensor Input Layer

   • Continuous distance monitoring
   • Line sensor polling

2. Decision Layer

   • State machine for mode handling
   • Collision risk assessment

3. Actuation Layer

   • Motor control
   • Steering servo control

4. User Interface

   • Bluetooth commands
   • Audible/visual feedback

🏁 Accomplishments

• ✅ Fully functional obstacle avoidance system
• ✅ Accurate line following implementation
• ✅ Working parking assist mode
• ✅ Smooth mode transitions (auto/manual/parking)
• ✅ Emergency stop functionality
• ✅ Configurable speed parameters

🚀 Getting Started

1. Clone this repository

2. Upload main.ino to Arduino

3. Assemble hardware per pin definitions

4. Power on and send commands:

   • 'A': Autonomous mode
   • 'M': Manual mode
   • 'P': Parking mode
   • '+/-': Speed adjustment

🔢 Pin Configuration Summary

Component	Pin(s)
Ultrasonic Sensor	A4 (Trig), A5 (Echo)
IR Sensors	8 (Left), 9 (Right)
Servo Motor	7
Buzzer	3
LED	2
Motors	FL:4, VL:6, BL:5 (Left) / FR:12, VR:11, BR:13 (Right)

🔮 Future Enhancements

1. Add camera-based object recognition
2. Implement vehicle-to-vehicle communication
3. Add machine learning for pattern recognition
4. Improve parking algorithm for parallel parking
5. Improve servo-based path planning with finer logic
6. Implement data logging for performance analysis
7. Integrate GPS for navigation support

👨‍💻 Contributors

• Akash Adhya — Developer and Team Lead
• Prakas Dutta — Developer and Supporting Engineer
• Anurag Sarkar — Developer and Supporting Engineer
• Suvranil Chattopadhyay — Engineer

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🔗 This project serves as a foundational step into autonomous vehicle systems using embedded development. It’s a perfect mix of hardware and software to bring real-world ADAS logic to life in a simplified, low-cost format.