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Zhexi Lian

2 accepted papers

2025

Continuously Improved Reinforcement Learning for Automated Driving

IROS 2025

Reinforcement Learning (RL) offers a promising solution to enable evolutionary automated driving. However, conventional RL methods often struggle with risk performance, as updated policies may fail to enhance performance or even lead to deterioration. To address this challenge, this research introdu

Cited by 0SourceScholar
2025

ExpliDrive: Bridging Model Predictive Control and Transformers for Interactive Autonomous Driving

IROS 2025

Autonomous driving (AD) continues to grapple with the complexity of dynamic and interactive traffic environments, where the primary difficulty stems from insufficient modeling of inter-vehicle interactions—particularly, how autonomous agents should perceive and respond to surrounding vehicles’ influ

Cited by 1SourceScholar