AI Road Sign Detection and Autonomous Driving Control System: A Complete Solution with Raspberry Pi + YOLO + Flutter

This project is a fully functional intelligent vehicle prototype integrating YOLO real-time object detection, Raspberry Pi embedded control, Flask backend, and Flutter mobile app to achieve traffic sign recognition and autonomous driving control. As an end-to-end system, it provides an excellent learning case for autonomous driving and embedded AI enthusiasts.

Autonomous driving technology is developing rapidly, but complete systems often involve complex hardware and software stacks, which can be daunting for beginners. This project provides a lightweight yet fully functional solution covering core AI visual recognition, hardware control, backend services, and mobile applications, forming an end-to-end system suitable for getting started with autonomous driving and embedded AI development.

System Architecture

1. Perception Layer: Based on the YOLO algorithm to recognize traffic signs (parking, no entry, speed limit, etc.), with the Raspberry Pi camera capturing real-time video streams.
2. Decision Layer: Raspberry Pi 4 runs Python programs to decide vehicle actions (e.g., stop, adjust speed) according to detection results and preset rules.
3. Execution Layer: Controls DC motors via the L298N motor driver module to realize vehicle movement.
4. Interaction Layer: The Flutter mobile app provides real-time video streams, detection results, speed monitoring, manual control, and other functions.

Technology Stack

• Backend: Flask provides API endpoints (video stream, status query, control, etc.)
• Video processing: OpenCV and Picamera2
• Communication: HTTP REST API (WiFi network)

System closed-loop process:

1. Image acquisition: Pi camera captures video frames
2. Object detection: YOLO model processes frames to recognize traffic signs
3. Decision generation: Raspberry Pi decides actions based on detection results
4. Motion control: GPIO sends signals to L298N to adjust motors
5. Data upload: Flask server provides video streams and status to the mobile app
6. User interaction: Flutter app displays images, users monitor or manually control

Hardware components include L298N motor driver, DC motors, robot chassis, battery pack; Flask API endpoints such as /video_feed, /status, /control/F, etc.

This project demonstrates the construction process of a complete AIoT system, covering YOLO detection, Raspberry Pi control, Flask backend, Flutter app, and other technology stacks. Value for beginners:

1. Completeness: See the full picture of the project from hardware to software
2. Practicality: All components are actually usable
3. Scalability: Modular design facilitates adding new features

As a prototype, areas for improvement include upgrading the decision system to reinforcement learning, multi-sensor fusion, replacing communication protocols, etc.

Application Scenarios:

• Education field: Teaching cases for AI, robotics, embedded systems
• Intelligent traffic research: Testing autonomous driving algorithms in controlled environments
• Warehouse logistics: Warehouse AGVs
• Agricultural automation: Orchard/greenhouse automatic inspection

Future Enhancement Directions:

• Lane detection, obstacle avoidance
• GPS navigation, voice control
• Cloud monitoring, simultaneous recognition of multiple signs
• Vehicle-to-vehicle communication