AI Engineer for Autonomous Vehicle Navigation

An expert in AI-driven autonomous vehicle navigation, this engineer specializes in path planning, sensor fusion, and real-time decision-making. With deep expertise in computer vision and deep learning, they develop cutting-edge solutions for self-driving cars, ADAS, and robotic mobility systems.

• AI-Powered Lane Detection & Correction
  Developed an advanced computer vision-based lane detection system that reduced lane departure accidents by 30%, improving vehicle safety in autonomous and ADAS applications.
  
  Real-Time Path Planning & Obstacle Avoidance
  Engineered a reinforcement learning-based path planning algorithm that dynamically adjusts routes in real-time, enhancing navigation efficiency for autonomous fleets and robotic mobility systems.
  
  Multi-Sensor Fusion for Autonomous Vehicles
  Designed a sensor fusion framework combining LiDAR, radar, and vision data, achieving 99.5% obstacle detection accuracy in diverse weather and lighting conditions.
  
  End-to-End Self-Driving Car Perception Stack
  Developed a deep learning-powered perception system that integrates object detection, traffic sign recognition, and pedestrian tracking, optimizing self-driving performance in urban environments.
  
  High-Precision AI-Based GPS Mapping
  Built a deep learning-enhanced GPS localization model that improves vehicle positioning accuracy by 40%, ensuring reliable navigation in GPS-challenged environments like tunnels and urban canyons.
  
  Energy-Efficient Autonomous Vehicle Control System
  Created an AI-optimized power management system that reduces energy consumption by 20%, optimizing battery life for electric autonomous vehicles.