1 citations · 1 across the 12 of their papers we have counts for
15 papers
Scaling Curriculum Learning For Autonomous Driving
Cevahir Koprulu, David Paz, Feng Tao +4
Batched simulators for autonomous driving have recently enabled training reinforcement learning (RL) agents at scale, encompassing thousands of traffic scenarios and billions of in…
EmbodiedMidtrain: Bridging the Gap between Vision-Language Models and Vision-Language-Action Models via Mid-training
Yiyang Du, Zhanqiu Guo, Haochen Zhang +5
Vision-Language-Action Models (VLAs) inherit their visual and linguistic capabilities from Vision-Language Models (VLMs), yet most VLAs are built from off-the-shelf VLMs that are n…
ExploreVLA: Dense World Modeling and Exploration for End-to-End Autonomous Driving
Zihao Sheng, Xin Ye, Jingru Luo +2
End-to-end autonomous driving models based on Vision-Language-Action (VLA) architectures have shown promising results by learning driving policies through behavior cloning on exper…
Reconstruction Matters: Learning Geometry-Aligned BEV Representation through 3D Gaussian Splatting
Yiren Lu, Xin Ye, Burhaneddin Yaman +4
Bird's-Eye-View (BEV) perception serves as a cornerstone for autonomous driving, offering a unified spatial representation that fuses surrounding-view images to enable reasoning fo…
UniDrive-WM: Unified Understanding, Planning and Generation World Model for Autonomous Driving
Zhexiao Xiong, Xin Ye, Burhan Yaman +5
World models have become central to autonomous driving, where accurate scene understanding and future prediction are crucial for safe control. Recent work has explored using vision…
End-to-End Visual Autonomous Parking via Control-Aided Attention
Chao Chen, Shunyu Yao, Yuanwu He +7
Precise parking requires an end-to-end system where perception adaptively provides policy-relevant details - especially in critical areas where fine control decisions are essential…
LTDA-Drive: LLMs-guided Generative Models based Long-tail Data Augmentation for Autonomous Driving
Mahmut Yurt, Xin Ye, Yunsheng Ma +5
3D perception plays an essential role for improving the safety and performance of autonomous driving. Yet, existing models trained on real-world datasets, which naturally exhibit l…
ALN-P3: Unified Language Alignment for Perception, Prediction, and Planning in Autonomous Driving
Yunsheng Ma, Burhaneddin Yaman, Xin Ye +5
Recent advances have explored integrating large language models (LLMs) into end-to-end autonomous driving systems to enhance generalization and interpretability. However, most exis…
Generative AI for Autonomous Driving: Frontiers and Opportunities
Yuping Wang, Shuo Xing, Cui Can +46
Generative Artificial Intelligence (GenAI) constitutes a transformative technological wave that reconfigures industries through its unparalleled capabilities for content creation,…
BEVDiffuser: Plug-and-Play Diffusion Model for BEV Denoising with Ground-Truth Guidance
Xin Ye, Burhaneddin Yaman, Sheng Cheng +3
Bird's-eye-view (BEV) representations play a crucial role in autonomous driving tasks. Despite recent advancements in BEV generation, inherent noise, stemming from sensor limitatio…
AdaWM: Adaptive World Model based Planning for Autonomous Driving
Hang Wang, Xin Ye, Feng Tao +5
World model based reinforcement learning (RL) has emerged as a promising approach for autonomous driving, which learns a latent dynamics model and uses it to train a planning polic…
MTA: Multimodal Task Alignment for BEV Perception and Captioning
Yunsheng Ma, Burhaneddin Yaman, Xin Ye +5
Bird's eye view (BEV)-based 3D perception plays a crucial role in autonomous driving applications. The rise of large language models has spurred interest in BEV-based captioning to…
LORD: Large Models based Opposite Reward Design for Autonomous Driving
Xin Ye, Feng Tao, Abhirup Mallik +2
Reinforcement learning (RL) based autonomous driving has emerged as a promising alternative to data-driven imitation learning approaches. However, crafting effective reward functio…
CLIP-BEVFormer: Enhancing Multi-View Image-Based BEV Detector with Ground Truth Flow
Chenbin Pan, Burhaneddin Yaman, Senem Velipasalar +1
Autonomous driving stands as a pivotal domain in computer vision, shaping the future of transportation. Within this paradigm, the backbone of the system plays a crucial role in int…
VLP: Vision Language Planning for Autonomous Driving
Chenbin Pan, Burhaneddin Yaman, Tommaso Nesti +4
Autonomous driving is a complex and challenging task that aims at safe motion planning through scene understanding and reasoning. While vision-only autonomous driving methods have…