F1-Tenth Autonomous System

In this project, I led to develop an autonomous driving system for an F1-Tenth car, focusing on creating a reliable perception and control pipeline for safe navigation under various conditions.

• Lane Detection System: Designed a window-based lane detection algorithm using a D435 depth camera and image filters, making the system robust to diverse lighting conditions and capable of accurately identifying lane boundaries in real time.
• Pure-Pursuit Controller Integration: Implemented and optimized a pure-pursuit controller using ROS on a Jetson Nano, achieving consistent lane following with an average lane deviation under 5 cm, ensuring precise control and stability during high-speed maneuvers.
• Dynamic Obstacle Avoidance: Developed and validated an obstacle avoidance algorithm that detects and reacts to dynamic objects within the environment. This system was integrated with a histogram of oriented gradients (HOG) feature-based human detector and LiDAR readings, resulting in a 20% reduction in failure rates by avoiding collisions in real-time.

Details can be found at the project repo.

Skills Used

• Programming: C++, Python
• Robotics Frameworks: ROS, Jetson Nano
• Computer Vision: OpenCV, HOG features, image filtering
• Sensors and Perception: D435 depth camera, LiDAR
• Control Algorithms: Pure-pursuit controller, dynamic obstacle avoidance