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Autonomous Vehicle Perception System

Computer Vision Deep Learning Autonomous Driving Python ROS

Overview

During my time at TU Berlin’s Autonomous Driving Laboratories (BeIntelli/DaiLabor), I contributed to the development of a cutting-edge perception system for autonomous vehicles. This project focused on real-time object detection, tracking, and scene understanding.

Key Features

  • Real-time Object Detection: Implemented YOLO-based detection pipeline achieving 30+ FPS on embedded hardware
  • Multi-sensor Fusion: Combined camera, LiDAR, and radar data for robust perception
  • Path Planning Integration: Connected perception outputs to path planning and control systems
  • Edge Case Handling: Developed algorithms to handle challenging scenarios like occlusions and adverse weather

Technical Implementation

Computer Vision Pipeline

The perception system uses a multi-stage pipeline:

  1. Image Preprocessing: Normalize and augment sensor data
  2. Object Detection: Deep learning models for detecting vehicles, pedestrians, and obstacles
  3. Tracking: Kalman filter-based tracking for temporal consistency
  4. Scene Understanding: Semantic segmentation for drivable area detection

Technologies Used

  • Python and PyTorch for deep learning models
  • ROS (Robot Operating System) for system integration
  • OpenCV for image processing
  • PCL (Point Cloud Library) for LiDAR data processing

Results

  • Achieved 95%+ detection accuracy on challenging urban scenarios
  • Reduced false positives by 40% through multi-sensor fusion
  • Successfully deployed on test vehicles for real-world validation

Challenges and Learning

Working on autonomous driving systems taught me the importance of:

  • Safety-critical system design
  • Real-time performance optimization
  • Handling edge cases and uncertainty
  • Cross-functional collaboration with hardware and control teams

Future Improvements

  • Integration of transformer-based models for improved accuracy
  • Enhanced sensor fusion algorithms
  • Better handling of dynamic environments