4 papers
Driving on Registers
Ellington Kirby, Alexandre Boulch, Yihong Xu +11
We present DrivoR, a simple and efficient transformer-based architecture for end-to-end autonomous driving. Our approach builds on pretrained Vision Transformers (ViTs) and introdu…
IPA: An Information-Reconstructive Input Projection Framework for Efficient Foundation Model Adaptation
Yuan Yin, Shashanka Venkataramanan, Tuan-Hung Vu +2
Parameter-efficient fine-tuning (PEFT) methods, such as LoRA, reduce adaptation cost by injecting low-rank updates into pretrained weights. However, LoRA's down-projection is rando…
PPT: Pretraining with Pseudo-Labeled Trajectories for Motion Forecasting
Yihong Xu, Yuan Yin, Éloi Zablocki +3
Accurately predicting how agents move in dynamic scenes is essential for safe autonomous driving. State-of-the-art motion forecasting models rely on datasets with manually annotate…
ReGentS: Real-World Safety-Critical Driving Scenario Generation Made Stable
Yuan Yin, Pegah Khayatan, Éloi Zablocki +2
Machine learning based autonomous driving systems often face challenges with safety-critical scenarios that are rare in real-world data, hindering their large-scale deployment. Whi…