Daehyeok Kwon

Self-Improving Autonomous Vehicles via Real-World Reinforcement Learning

End-to-end autonomous driving systems have demonstrated advantages over traditional modular systems. Despite this progress, these end-to-end systems still struggle to be deployed in real-world driving environments, as they inevitably encounter undertrained scenarios in which autonomous vehicles may