5 papers
VECTOR-Drive: Tightly Coupled Vision-Language and Trajectory Expert Routing for End-to-End Autonomous Driving
Rui Zhao, Jianlin Yu, Zhenhai Gao +2
End-to-end autonomous driving requires models to understand traffic scenes, infer driving intent, and generate executable motion plans. Recent vision-language-action (VLA) models i…
VLADriver-RAG: Retrieval-Augmented Vision-Language-Action Models for Autonomous Driving
Rui Zhao, Haofeng Hu, Zhenhai Gao +2
Vision-Language-Action (VLA) models have emerged as a promising paradigm for end-to-end autonomous driving, yet their reliance on implicit parametric knowledge limits generalizatio…
DiffE2E: Rethinking End-to-End Driving with a Hybrid Action Diffusion and Supervised Policy
Rui Zhao, Yuze Fan, Ziguo Chen +2
End-to-end learning has emerged as a transformative paradigm in autonomous driving. However, the inherently multimodal nature of driving behaviors and the generalization challenges…
Sce2DriveX: A Generalized MLLM Framework for Scene-to-Drive Learning
Rui Zhao, Qirui Yuan, Jinyu Li +4
End-to-end autonomous driving, which directly maps raw sensor inputs to low-level vehicle controls, is an important part of Embodied AI. Despite successes in applying Multimodal La…
An Enhanced Low-Resolution Image Recognition Method for Traffic Environments
Zongcai Tan, Zhenhai Gao
Currently, low-resolution image recognition is confronted with a significant challenge in the field of intelligent traffic perception. Compared to high-resolution images, low-resol…