Promotion - End-to-End Trainable System for Autonomous Driving with Introspection
Core
Research and development of next-generation autonomous driving systems by transitioning from reactive Vision-Language-Action (VLA) models to predictive, agent-based world models with long-term prediction capabilities.
Role type
Research PhD candidate (Autonomous Driving / AI)
Builds
Integrated framework combining VLA models, world models, and agent-based planners for autonomous vehicles
Domain
Autonomous driving, Artificial Intelligence, Deep Learning
Deliverable
research
Required skills
Machine learning algorithms, Deep learning, Python programming, C++ programming
Preferred skills
Long-term prediction modeling, Agent-based planning, Proactive simulation, Data preparation for large-scale systems
Technologies
VLA models, Agent-based planners, Simulation environments