7 papers
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…
The Blind Spot of Adaptation: Quantifying and Mitigating Forgetting in Fine-tuned Driving Models
Runhao Mao, Hanshi Wang, Yixiang Yang +3
The integration of Vision-Language Models (VLMs) into autonomous driving promises to solve long-tail scenarios, but this paradigm faces the critical and unaddressed challenge of ca…
FlowAD: Ego-Scene Interactive Modeling for Autonomous Driving
Mingzhe Guo, Yixiang Yang, Chuanrong Han +4
Effective environment modeling is the foundation for autonomous driving, underpinning tasks from perception to planning. However, current paradigms often inadequately consider the…
Prompting the Unseen: Detecting Hidden Backdoors in Black-Box Models
Zi-Xuan Huang, Jia-Wei Chen, Zhi-Peng Zhang +1
Visual prompting (VP) is a new technique that adapts well-trained frozen models for source domain tasks to target domain tasks. This study examines VP's benefits for black-box mode…
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…