5 citations · 6 across the 5 of their papers we have counts for
5 papers
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…
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…
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…
MoST: Multi-modality Scene Tokenization for Motion Prediction
Norman Mu, Jingwei Ji, Zhenpei Yang +11
Many existing motion prediction approaches rely on symbolic perception outputs to generate agent trajectories, such as bounding boxes, road graph information and traffic lights. Th…
3D Open-Vocabulary Panoptic Segmentation with 2D-3D Vision-Language Distillation
Zihao Xiao, Longlong Jing, Shangxuan Wu +9
3D panoptic segmentation is a challenging perception task, especially in autonomous driving. It aims to predict both semantic and instance annotations for 3D points in a scene. Alt…