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cs.CV2025
WOD-E2E: Waymo Open Dataset for End-to-End Driving in Challenging Long-tail Scenarios
Runsheng Xu, Hubert Lin, Wonseok Jeon +11
Vision-based end-to-end (E2E) driving has garnered significant interest in the research community due to its scalability and synergy with multimodal large language models (MLLMs).…
cs.CV2025
Enhanced Motion Forecasting with Plug-and-Play Multimodal Large Language Models
Katie Luo, Jingwei Ji, Tong He +4
Current autonomous driving systems rely on specialized models for perceiving and predicting motion, which demonstrate reliable performance in standard conditions. However, generali…
cs.CV2025
S4-Driver: Scalable Self-Supervised Driving Multimodal Large Language Modelwith Spatio-Temporal Visual Representation
Yichen Xie, Runsheng Xu, Tong He +9
The latest advancements in multi-modal large language models (MLLMs) have spurred a strong renewed interest in end-to-end motion planning approaches for autonomous driving. Many en…