5 citations · 5 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…
PVTransformer: Point-to-Voxel Transformer for Scalable 3D Object Detection
Zhaoqi Leng, Pei Sun, Tong He +2
3D object detectors for point clouds often rely on a pooling-based PointNet to encode sparse points into grid-like voxels or pillars. In this paper, we identify that the common Poi…
STT: Stateful Tracking with Transformers for Autonomous Driving
Longlong Jing, Ruichi Yu, Xu Chen +19
Tracking objects in three-dimensional space is critical for autonomous driving. To ensure safety while driving, the tracker must be able to reliably track objects across frames and…