/adas_project

ADAS system implementation on gazebo using opencv and yolo

Primary LanguagePython

ObstacleAVoidance3

Overview

This repository contains a comprehensive implementation of an Advanced Driver Assistance System (ADAS) using ROS (Robot Operating System). The system integrates lane detection, motion control, and safety features to provide autonomous driving capabilities with a focus on safety and reliability.

System Architecture

The system consists of three main components:

  1. Lane Detection System
  2. Motion Control System
  3. Safety and Monitoring System

Key Features

  • Real-time lane detection and tracking
  • Adaptive motion control with PID
  • Obstacle detection and avoidance
  • Automatic lane changing
  • Time-to-Collision (TTC) calculation
  • Emergency stop system
  • Visualization and debugging tools

Requirements

Hardware

  • Camera with ROS driver support
  • Robot/Vehicle with ROS interface
  • Computer with ROS installation

Software

  • ROS (Robot Operating System)
  • Python 3.x
  • OpenCV
  • NumPy
  • Additional ROS packages:
    • geometry_msgs
    • sensor_msgs
    • visualization_msgs
    • cv_bridge
    • std_msgs

Installation

# Create a catkin workspace
mkdir -p ~/catkin_ws/src
cd ~/catkin_ws/src

# Clone the repository
git clone https://github.com/AlpMercan/adas_system.git
git clone https://github.com/aws-robotics/aws-robomaker-racetrack-world

# Install dependencies
sudo apt install ros-noetic-husky-desktop

sudo apt-get update
sudo apt-get install python3-opencv
sudo apt-get install ros-noetic-cv-bridge
sudo apt-get install ros-noetic-vision-opencv
rosdep install --from-paths src --ignore-src -r -y


# Build the workspace
cd ~/catkin_ws
catkin_make

# Source the workspace
source devel/setup.bash

System Components

1. Lane Detection Node

Features

  • Real-time lane boundary detection
  • Perspective transformation for bird's-eye view
  • Color thresholding for lane marker detection
  • Rolling average for stable lane tracking
  • ROI optimization

Parameters

# Lane Detection Parameters
min_pixels: 50
nwindows: 15
margin: 150
minpix: 30

Subscribed Topics

  • /camera/rgb/image_raw (sensor_msgs/Image)

Published Topics

  • /processed_image (sensor_msgs/Image)

2. Motion Control Node

Features

  • PID-based steering control
  • Adaptive speed control
  • Automatic lane changing
  • Safety-first approach
  • Real-time visualization

Parameters

# Motion Control Parameters
linear_speed: 1.0
max_angular_speed: 0.5
Kp: 0.005
Ki: 0.0001
Kd: 0.001
max_integral: 50
safety_distance_threshold: 1.0

Subscribed Topics

  • /processed_image (sensor_msgs/Image)
  • /person_in_lane (std_msgs/Bool)
  • /person_distance (std_msgs/Float32)
  • /ttc (std_msgs/Float32)

Published Topics

  • /cmd_vel (geometry_msgs/Twist)
  • /detected_object (visualization_msgs/Marker)
  • /detection_visualization (sensor_msgs/Image)

Usage

Starting the System

  1. Launch the ADAS system:
roslaunch adas_project adas_visualization.launch
  1. Launch the motion controler:
rosrun adas_project motion_controller.py

Basic Operations

  1. Lane Following Mode

    • System automatically follows detected lanes
    • Maintains center position between lane markers
    • Adjusts speed based on curve intensity

  2. Obstacle Avoidance

    • Automatically detects obstacles
    • Initiates lane change when safe
    • Maintains safe following distance

  3. Emergency Procedures

    • Automatic emergency stop when critical obstacles detected
    • Graceful degradation when lane markers lost
    • Safety distance maintenance

Configuration

Lane Detection Tuning

# config/lane_detection.yaml
color_threshold:
  white_lower: [0, 0, 200]
  white_upper: [180, 30, 255]
  yellow_lower: [20, 100, 100]
  yellow_upper: [30, 255, 255]

transform_points:
  src: [[0.35, 0.6], [0.65, 0.6], [0.9, 0.95], [0.1, 0.95]]
  dst: [[0.2, 0], [0.8, 0], [0.8, 1], [0.2, 1]]

Motion Control Tuning

# config/motion_control.yaml
pid_params:
  Kp: 0.005
  Ki: 0.0001
  Kd: 0.001

safety_params:
  min_distance: 1.0
  emergency_stop_distance: 0.5
  max_speed: 1.0

Safety Features

Emergency Stop Conditions

  • Obstacle within critical distance
  • Lane detection failure
  • System malfunction detection
  • TTC below threshold

Failsafe Mechanisms

  1. Automatic speed reduction in curves
  2. Gradual deceleration on detection loss
  3. Redundant obstacle detection
  4. System health monitoring

Debugging and Visualization

Available Debug Topics

  • /detection_visualization: Lane detection visualization
  • /path_planning_debug: Motion planning visualization
  • /safety_zone_visualization: Safety zone indicators

Debug Commands

# View lane detection
rosrun image_view image_view image:=/detection_visualization

# Monitor vehicle status
rostopic echo /vehicle_status

# View TTC values
rostopic echo /ttc

Troubleshooting

Common Issues and Solutions

  1. Lane Detection Issues

    • Problem: Poor lane detection
    • Solution: Adjust color thresholds and ROI parameters

  2. Motion Control Issues

    • Problem: Unstable movement
    • Solution: Fine-tune PID parameters

  3. Safety System Issues

    • Problem: False emergency stops
    • Solution: Adjust safety thresholds

Contributing

  1. Fork the repository
  2. Create your feature branch (git checkout -b feature/AmazingFeature)
  3. Commit your changes (git commit -m 'Add some AmazingFeature')
  4. Push to the branch (git push origin feature/AmazingFeature)
  5. Open a Pull Request

License

MIT License

Authors

  • Alp Mercan

Images and Videos

ObstacleAVoidance3