Jiacheng Zuo

RALAD: Bridging the Real-to-Sim Domain Gap in Autonomous Driving with Retrieval-Augmented Learning

As end-to-end autonomous driving advances toward real-world deployment, ensuring the safety of autonomous vehicles (AVs) has become a critical requirement for their commercial viability. While rule-based AVs have traditionally undergone rigorous testing in both real-world and simulated environments