5 papers
Long-Horizon Consistent and Interaction-Aware World Models for Multi-Style End-to-End Driving
Yuxuan Han, Kunyuan Wu, Liyunong Yang +4
End-to-end autonomous driving has increasingly adopted world model-based reinforcement learning frameworks to improve learning efficiency through \textit{imagined rollouts}. Howeve…
AppleVLM: End-to-end Autonomous Driving with Advanced Perception and Planning-Enhanced Vision-Language Models
Yuxuan Han, Kunyuan Wu, Qianyi Shao +6
End-to-end autonomous driving has emerged as a promising paradigm integrating perception, decision-making, and control within a unified learning framework. Recently, Vision-Languag…
Group Evidence Matters: Tiling-based Semantic Gating for Dense Object Detection
Yilun Xiao
Dense small objects in UAV imagery are often missed due to long-range viewpoints, occlusion, and clutter[cite: 5]. This paper presents a detector-agnostic post-processing framework…
UDA4Inst: Unsupervised Domain Adaptation for Instance Segmentation
Yachan Guo, Yi Xiao, Danna Xue +2
Instance segmentation is crucial for autonomous driving, but is hindered by the lack of annotated real-world data due to expensive labeling costs. Unsupervised Domain Adaptation (U…
Guiding Attention in End-to-End Driving Models
Diego Porres, Yi Xiao, Gabriel Villalonga +2
Vision-based end-to-end driving models trained by imitation learning can lead to affordable solutions for autonomous driving. However, training these well-performing models usually…