Promotion - End-to-End Trainable System for Autonomous Driving with Introspection
Core
Researching the next generation of autonomous driving systems by transitioning from reactive Vision-Language-Action models to predictive, agent-based world models with introspection capabilities.
Role type
Research PhD candidate (Autonomous Driving / AI)
Builds
Frameworks integrating VLA models, world models, and agent-based planners for holistic driving systems
Domain
Autonomous Driving / Machine Learning / Robotics
Deliverable
research
Required skills
Machine learning algorithms, Deep Learning, Python, C++, Data preparation, Simulation
Preferred skills
Long-term prediction, Handling edge cases, Efficient training of large systems
Responsibilities
Develop framework for integrating VLA models and agent-based planners; Demonstrate improvements in long-term predictions via proactive simulation; Drive changes in data preparation and efficient training of large systems
Seniority
PhD Candidate, Research