4 papers
Vision-Language-Action Models for Autonomous Driving: Past, Present, and Future
Tianshuai Hu, Xiaolu Liu, Song Wang +17
Autonomous driving has long relied on modular "Perception-Decision-Action" pipelines, where hand-crafted interfaces and rule-based components often break down in complex or long-ta…
LargeAD: Large-Scale Cross-Sensor Data Pretraining for Autonomous Driving
Lingdong Kong, Xiang Xu, Youquan Liu +6
Recent advancements in vision foundation models (VFMs) have revolutionized visual perception in 2D, yet their potential for 3D scene understanding, particularly in autonomous drivi…
Calib3D: Calibrating Model Preferences for Reliable 3D Scene Understanding
Lingdong Kong, Xiang Xu, Jun Cen +4
Safety-critical 3D scene understanding tasks necessitate not only accurate but also confident predictions from 3D perception models. This study introduces Calib3D, a pioneering eff…
SimCMF: A Simple Cross-modal Fine-tuning Strategy from Vision Foundation Models to Any Imaging Modality
Chenyang Lei, Liyi Chen, Jun Cen +5
Foundation models like ChatGPT and Sora that are trained on a huge scale of data have made a revolutionary social impact. However, it is extremely challenging for sensors in many d…