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Zhenhai Gao

2 accepted papers

2025

DiffE2E: Rethinking End-to-End Driving with a Hybrid Diffusion-Regression-Classification Policy

NeurIPS 2025poster

End-to-end learning has emerged as a transformative paradigm for autonomous driving. However, the inherently multimodal nature of driving behaviors remains a fundamental challenge to robust deployment. We propose DiffE2E, a diffusion-based end-to-end autonomous driving framework. The architecture fi…

Cited by 0SourceScholar
2025

Sce2DriveX: A Generalized MLLM Framework for Scene-to-Drive Learning

RA-L 2025

End-to-end autonomous driving, which directly maps raw sensor inputs to low-level vehicle controls, is an crucial part of Embodied AI. Despite successes in applying Multimodal Large Language Models (MLLMs) for high-level traffic scene semantic understanding, it remains challenging to effectively tra

Cited by 23SourceScholar