Autonomous Vehicle Simulation
Developed autonomous vehicles in CARLA for vehicle navigation, sensor integration, and decision-making. Built comprehensive simulation environment integrating LiDAR and camera sensors for real-time object detection with machine learning models for navigation optimization.
The Challenge
Autonomous vehicle development requires extensive real-world testing which is expensive, dangerous, and time-consuming. Traditional testing methods cannot efficiently simulate the vast array of driving scenarios needed to train and validate autonomous systems, creating a bottleneck in AV development and safety validation.
The Solution
Built a comprehensive autonomous vehicle simulation environment using CARLA that integrates multiple sensor types (LiDAR, cameras) with advanced machine learning models for real-time object detection and navigation. The system enables safe, repeatable testing of autonomous driving algorithms across diverse scenarios without real-world risks.
Technical Highlights
- Integrated LiDAR and camera sensor fusion achieving 80%+ object detection accuracy in diverse weather conditions
- Implemented advanced pathfinding algorithms (A*, RRT, IDA*) for optimal route planning in complex urban environments
- Developed machine learning models using Faster R-CNN and YOLO for real-time object classification and tracking
- Created comprehensive data visualization dashboards using Tableau and Power BI for performance analysis
- Built robust simulation framework handling multiple vehicle interactions and dynamic obstacle scenarios
Key Results & Impact
Business Impact
This simulation platform significantly accelerates autonomous vehicle development by providing a safe, cost-effective testing environment. The system demonstrates advanced skills in computer vision, machine learning, and sensor integration - critical technologies for the future of transportation and robotics industries.
Key Achievements
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