Computer Vision for Autonomous Vehicles

Object Detection and Tracking Algorithms for Self-Driving Cars

Computer Vision for Autonomous Vehicles

This project focuses on developing advanced computer vision algorithms for autonomous vehicle applications, specifically targeting object detection and tracking systems that are crucial for safe self-driving operations. The work was conducted during my AI internship at Jego Pods, where I collaborated with a team to build robust perception systems.

Project Overview

The goal was to create a comprehensive computer vision system that could:

  • Detect and classify various road objects (vehicles, pedestrians, traffic signs)
  • Track objects across multiple frames for trajectory prediction
  • Handle challenging environmental conditions (low light, weather, occlusions)
  • Provide real-time performance for autonomous driving applications

Technical Implementation

Object Detection Pipeline

  • Multi-scale Feature Extraction: Implemented feature pyramid networks for detecting objects at various scales
  • Real-time Processing: Optimized algorithms to achieve sub-100ms inference times
  • Robust Classification: Trained models on diverse datasets to handle edge cases

Tracking System

  • Multi-object Tracking: Developed algorithms to maintain object identity across frames
  • Trajectory Prediction: Implemented Kalman filtering for motion prediction
  • Association Logic: Built robust data association methods for tracking consistency

Road Quality Assessment

  • Surface Analysis: Created algorithms to classify road conditions (good, fair, poor)
  • Damage Detection: Implemented specialized models for identifying road damage patterns
  • Real-time Mapping: Integrated with GPS systems for road quality mapping

Technologies Used

  • Deep Learning: PyTorch for model development and training
  • Computer Vision: OpenCV for image processing and augmentation
  • Data Processing: NumPy and Pandas for data manipulation
  • Cloud Computing: AWS for scalable training and deployment
  • Version Control: Git for collaborative development

Key Achievements

  • Improved Detection Accuracy: Achieved 95%+ accuracy on standard autonomous driving benchmarks
  • Real-time Performance: Reduced inference time by 40% through model optimization
  • Robust Tracking: Maintained 90%+ tracking consistency across challenging scenarios
  • Road Quality Classification: Successfully classified road conditions with 88% accuracy

Impact and Applications

This work directly contributed to:

  • Safety Enhancement: Improved perception capabilities for autonomous vehicles
  • Infrastructure Mapping: Better road quality assessment for maintenance planning
  • Research Advancement: Contributed to the broader field of autonomous vehicle perception
  • Industry Collaboration: Demonstrated practical application of academic research

Future Directions

The project opens several exciting research avenues:

  • Integration with LiDAR and radar sensors for multi-modal perception
  • Advanced trajectory prediction using transformer architectures
  • Real-time adaptation to changing environmental conditions
  • Scalable deployment across different vehicle platforms

This project showcases practical expertise in computer vision and demonstrates the ability to translate research concepts into real-world applications, which is highly valuable for AI research programs like Cohere Labs Scholar.