3 papers
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
EMMA: End-to-End Multimodal Model for Autonomous Driving
Jyh-Jing Hwang, Runsheng Xu, Hubert Lin +11
We introduce EMMA, an End-to-end Multimodal Model for Autonomous driving. Built upon a multi-modal large language model foundation like Gemini, EMMA directly maps raw camera sensor…
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…