39 citations · 230 across the 69 of their papers we have counts for
71 papers
Mining DTA with SMT by Exploiting Simple Elementary Language and Timed Augmented Prefix Acceptor
Ziran Wang, Jie An, Naijun Zhan
Timed automata, which extend finite state automata by introducing clock variables, serve as a popular formalism for specifying and analyzing the timed behaviors of real-time system…
Designing Versatile Samples for Learned Trajectory Scoring
Yaguang Li, Jiaru Zhang, Chuheng Wei +2
Many current end-to-end driving policies emit a pool of candidate trajectories and select one, which makes selection a separable component: a scorer can be retrained while the plan…
How Can Driving World Models Do Counterfactual Prediction?
Jiaru Zhang, Can Cui, Yi Xu +3
Driving world models are often interpreted as counterfactual simulators for observed driving episodes: given a factual driving log, they are asked what would have happened under an…
Post-Training in End-to-End Autonomous Driving
Ruining Yang, Muxing Wang, Yixiao Chen +8
End-to-end models that map multimodal inputs directly to future trajectories/maneuvers have emerged as an increasingly prominent research paradigm in autonomous driving. This class…
Controllable Sim Agents with Behavior Latents
Juanwu Lu, Junyu Zhu, Ziran Wang
Realistic traffic simulation requires agents that imitate logged behavior and can also be steered along interpretable axes. Such controllability enables engineers to isolate variab…
On Variance Reduction in Learning Mean Flows
Juanwu Lu, Ziran Wang
One-step generative modeling has emerged as a leading approach for amortizing the inference cost of diffusion and flow-matching models. Among distillation-free methods, MeanFlow tr…
VISTA-DZ: Visual Semantic Trajectory Adaptation for Personalized Dilemma Zone Prediction
Chuheng Wei, Ziye Qin, Ziran Wang +1
Driver decision making in the dilemma zone at signalized intersections is safety critical, as vehicles approaching a yellow signal must decide whether to stop or proceed within lim…
FleetAgent: Teleoperation Assistant for Autonomous Fleets via Vectorized V2N Messages
Juntong Peng, Qi Chen, Deyuan Qu +3
Large-scale autonomous fleets rely on teleoperation to resolve rare failures, yet streaming raw sensor data from many vehicles is costly, and remote operators can only monitor a li…
OmniV2X: A Generative Foundation Planner for Efficient End-to-End Cooperative Driving
Juntong Peng, Juanwu Lu, Yupeng Zhou +3
We present OmniV2X, a generative foundation model for vehicle-to-everything (V2X) cooperative driving. The model directly interprets independent context sequences comprising multi-…
Analytical Correction for Subsampling Bias in Drifting Models
Jiaru Zhang, Zeyun Deng, Juanwu Lu +2
Drifting models are capable one-step generative models trained to follow a drifting field. The field combines attractive and repulsive softmax-weighted centroids over the data and…
Efficient and Explainable End-to-End Autonomous Driving via Masked Vision-Language-Action Diffusion
Jiaru Zhang, Manav Gagvani, Can Cui +3
Large Language Models (LLMs) and Vision-Language Models (VLMs) have emerged as promising candidates for end-to-end autonomous driving. However, these models typically face challeng…
Mollified Value Learning
Hrishikesh Viswanath, Juanwu Lu, S. Talha Bukhari +4
Offline goal-conditioned reinforcement learning (GCRL) learns goal-reaching behaviors from static datasets, but accurate value estimation remains challenging under limited state-ac…
A Universal and Robust Framework for Multiple Gas Recognition Based-on Spherical Normalization-Coupled Mahalanobis Algorithm
Shuai Chen, Yang Song, Chen Wang +1
Electronic nose (E-nose) systems face two interconnected challenges in open-set gas recognition: feature distribution shift caused by signal drift and decision boundary failure ind…
Digital Twin AI: Opportunities and Challenges from Large Language Models to World Models
Rong Zhou, Dongping Chen, Zihan Jia +24
Digital twins, as precise digital representations of physical systems, have evolved from passive simulation tools into intelligent and autonomous entities through the integration o…
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…