TinyML Vehicle Driving Status Detection System

A real-time vehicle driving status detection system based on the QuecPython embedded platform, utilizing the MPU6500 sensor and a Random Forest classifier.

System Overview

This TinyML system employs machine learning directly on embedded hardware to detect vehicle driving status in real time. The system uses a small sensor called MPU6500 to measure motion and rotation, and it is currently optimized for motion along the X-axis, Y-axis, Z-axis, and gyroscopic motion.

Features

• Real-time Detection: Recognition response time ranges from 150ms to 300ms
• Memory Efficiency: Automatic buffer management with overflow prevention
• False Alarm Prevention: Requires 3 consecutive consistent results before detection confirmation
• Timer-based Processing: Non-blocking hardware timer architecture
• Gesture Separation: Independent analysis of each gesture to avoid mutual interference

Directory Structure

tinyml-car/
├── src
	├── _main.py              # Main application based on the timer system
	├── mpu6500.py            # MPU6500 sensor driver with m/s² scaling
	├── random_forest.py      # Pre-trained random forest model
	├── tinyml.py             # TinyML pipeline with anti-shake mechanism
├── fw
├── LICENSE
├── README.md
└── README_zh.md

Technical Details

• Platform: Quectel embedded module running MicroPython
• Sensor: MPU6500 6-axis IMU (3-axis accelerometer + 3-axis gyroscope)
• Model: Random Forest classifier (4 categories: 1 = driving on flat ground, 2 = uphill, 3 = downhill, 4 = skidding)
• Sampling: 50Hz sensor reading, 20Hz inference
• Detection: 3 consecutive consistent results within a 450ms window
• Data Format: Accelerometer (m/s²), Gyroscope (deg/s)

Current Status

• X/Y/Z-axis Motion - Reliable operation and stable detection
• Memory Management - Buffer contamination issue resolved
• Real-time Processing - Timer-based architecture implemented
• Anti-shake System - False alarm prevention function working properly
• Circular Motion - Currently under development

Quick Start

Prerequisites

Before you start, please ensure you have the following prerequisites:

• Hardware:
  • A QuecPython development board
  • A computer (Windows 7, Windows 10, or Windows 11)
  • An MPU6500 sensor
• Software:
  • Debugging tool QPYcom
  • QuecPython firmware (The EG912U firmware can be found in the fw directory of the repository)
  • Python text editor (e.g., VSCode, PyCharm)

Installation

1. Clone the repository:

git clone https://github.com/aaronchenzhihe/Tinyml-car.git

2.Flash the firmware: Flash the firmware to the development board according to the instructions.

Run the Application

1. Hardware Connection: Correctly connect the MPU6500 sensor to the I2C interface of the development board.
2. Connect to the host computer via Type-C cable.
3. Download the code to the device:
   • Launch the QPYcom debugging tool.
   • Connect the data cable to the computer.
   • Press the PWRKEY button on the development board to power it on.
   • Import all files in the src folder into the module's file system while maintaining the directory structure, following the instructions.
4. Run the code
   • Select the File tab.
   • Select the _main.py script.
   • Right-click and select Run to execute the script.

Usage

The system automatically performs real-time detection. When a vehicle driving status is recognized, it outputs the classification result (1, 2, 3, 4) and clears all buffers to prevent interference from previous results.

Performance

• Detection Latency: Theoretical minimum of 150ms, actual range of 200-300ms
• Memory Usage: Total < 50KB
• Accuracy: Optimized for X/Y/Z-axis motion and gyroscopic motion