most citedOpenEMMA: Open-Source Multimodal Model for End-to-End Autonomous Driving

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cs.CV2025

NexusFlow: Unifying Disparate Tasks under Partial Supervision via Invertible Flow Networks

Fangzhou Lin, Yuping Wang, Yuliang Guo +7

Partially Supervised Multi-Task Learning (PS-MTL) aims to leverage knowledge across tasks when annotations are incomplete. Existing approaches, however, have largely focused on the…

cs.CV2025

AirV2X: Unified Air-Ground Vehicle-to-Everything Collaboration

Xiangbo Gao, Yuheng Wu, Fengze Yang +7

While multi-vehicular collaborative driving demonstrates clear advantages over single-vehicle autonomy, traditional infrastructure-based V2X systems remain constrained by substanti…

cs.CV2025

Generative AI for Autonomous Driving: Frontiers and Opportunities

Yuping Wang, Shuo Xing, Cui Can +44

Generative Artificial Intelligence (GenAI) constitutes a transformative technological wave that reconfigures industries through its unparalleled capabilities for content creation,…

cs.CV2025

Can Large Vision Language Models Read Maps Like a Human?

Shuo Xing, Zezhou Sun, Shuangyu Xie +6

In this paper, we introduce MapBench-the first dataset specifically designed for human-readable, pixel-based map-based outdoor navigation, curated from complex path finding scenari…

cs.CV2025

Re-Align: Aligning Vision Language Models via Retrieval-Augmented Direct Preference Optimization

Shuo Xing, Peiran Li, Yuping Wang +6

The emergence of large Vision Language Models (VLMs) has broadened the scope and capabilities of single-modal Large Language Models (LLMs) by integrating visual modalities, thereby…

cs.CV20241 cited

OpenEMMA: Open-Source Multimodal Model for End-to-End Autonomous Driving

Shuo Xing, Chengyuan Qian, Yuping Wang +4

Since the advent of Multimodal Large Language Models (MLLMs), they have made a significant impact across a wide range of real-world applications, particularly in Autonomous Driving…