39 citations · 230 across the 69 of their papers we have counts for
14 papers · 1 filter
XR-DT: Extended Reality-Enhanced Digital Twin for Safe Motion Planning via Human-Aware Model Predictive Path Integral Control
Tianyi Wang, Jiseop Byeon, Ahmad Yehia +7
As mobile robots increasingly operate alongside humans in shared workspaces, ensuring safe, efficient, and interpretable Human-Robot Interaction (HRI) has become a pressing challen…
One-Step Diffusion Samplers via Self-Distillation and Deterministic Flow
Pascal Jutras-Dube, Jiaru Zhang, Ziran Wang +1
Sampling from unnormalized target distributions is a fundamental yet challenging task in machine learning and statistics. Existing sampling algorithms typically require many iterat…
FSDAM: Few-Shot Driving Attention Modeling via Vision-Language Coupling
Kaiser Hamid, Can Cui, Khandakar Ashrafi Akbar +2
Understanding not only where drivers look but also why their attention shifts is essential for interpretable human-AI collaboration in autonomous driving. Driver attention is not p…
AstroMMBench: A Benchmark for Evaluating Multimodal Large Language Models Capabilities in Astronomy
Jinghang Shi, Xiaoyu Tang, Yang Huang +4
Astronomical image interpretation presents a significant challenge for applying multimodal large language models (MLLMs) to specialized scientific tasks. Existing benchmarks focus…
SIMSplat: Language-Aligned 4D Gaussian Splatting for Driving Scenario Generation
Sung-Yeon Park, Adam Lee, Juanwu Lu +6
Driving scene manipulation using real-world sensor data has emerged as a promising alternative to traditional driving simulators. Despite advances in language control and neural sc…
On Synthesis of Timed Regular Expressions
Ziran Wang, Jie An, Naijun Zhan +2
Timed regular expressions serve as a formalism for specifying real-time behaviors of Cyber-Physical Systems. In this paper, we consider the synthesis of timed regular expressions,…
ViLaD: A Large Vision Language Diffusion Framework for End-to-End Autonomous Driving
Can Cui, Yupeng Zhou, Juntong Peng +6
End-to-end autonomous driving systems built on Vision Language Models (VLMs) have shown significant promise, yet their reliance on autoregressive architectures introduces some limi…
On Learning Closed-Loop Probabilistic Multi-Agent Simulator
Juanwu Lu, Rohit Gupta, Ahmadreza Moradipari +3
The rapid iteration of autonomous vehicle (AV) deployments leads to increasing needs for building realistic and scalable multi-agent traffic simulators for efficient evaluation. Re…
A Hierarchical Test Platform for Vision Language Model (VLM)-Integrated Real-World Autonomous Driving
Yupeng Zhou, Can Cui, Juntong Peng +5
Vision-Language Models (VLMs) have demonstrated notable promise in autonomous driving by offering the potential for multimodal reasoning through pretraining on extensive image-text…
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,…
Accelerating Inference of Discrete Autoregressive Normalizing Flows by Selective Jacobi Decoding
Jiaru Zhang, Juanwu Lu, Xiaoyu Wu +2
Discrete normalizing flows are promising generative models with advantages such as analytical log-likelihood computation and end-to-end training. However, the architectural constra…
NuPlanQA: A Large-Scale Dataset and Benchmark for Multi-View Driving Scene Understanding in Multi-Modal Large Language Models
Sung-Yeon Park, Can Cui, Yunsheng Ma +4
Recent advances in multi-modal large language models (MLLMs) have demonstrated strong performance across various domains; however, their ability to comprehend driving scenes remain…
STAMP: Scalable Task And Model-agnostic Collaborative Perception
Xiangbo Gao, Runsheng Xu, Jiachen Li +3
Perception is crucial for autonomous driving, but single-agent perception is often constrained by sensors' physical limitations, leading to degraded performance under severe occlus…