5 papers · 1 filter
Structured Labeling Enables Faster Vision-Language Models for End-to-End Autonomous Driving
Hao Jiang, Chuan Hu, Yukang Shi +4
Vision-Language Models (VLMs) offer a promising approach to end-to-end autonomous driving due to their human-like reasoning capabilities. However, troublesome gaps remains between…
MetaDAT: Generalizable Trajectory Prediction via Meta Pre-training and Data-Adaptive Test-Time Updating
Yuning Wang, Pu Zhang, Yuan He +2
Existing trajectory prediction methods exhibit significant performance degradation under distribution shifts during test time. Although test-time training techniques have been expl…
Active Learning from Scene Embeddings for End-to-End Autonomous Driving
Wenhao Jiang, Duo Li, Menghan Hu +3
In the field of autonomous driving, end-to-end deep learning models show great potential by learning driving decisions directly from sensor data. However, training these models req…
Cyclic Refiner: Object-Aware Temporal Representation Learning for Multi-View 3D Detection and Tracking
Mingzhe Guo, Zhipeng Zhang, Liping Jing +3
We propose a unified object-aware temporal learning framework for multi-view 3D detection and tracking tasks. Having observed that the efficacy of the temporal fusion strategy in r…
End-to-End Autonomous Driving without Costly Modularization and 3D Manual Annotation
Mingzhe Guo, Zhipeng Zhang, Yuan He +2
We propose UAD, a method for vision-based end-to-end autonomous driving (E2EAD), achieving the best open-loop evaluation performance in nuScenes, meanwhile showing robust closed-lo…