1 citations · 1 across the 17 of their papers we have counts for
20 papers
VehDyn: A Driving World Model Benchmark for Vehicle Dynamics
Tianyi Wang, Wangsheng Du, Jiazhou Chen +12
Video world models are emerging as data engines, action planners, and generative simulators for autonomous driving, but existing benchmarks primarily assess visual fidelity and coa…
RoboPhys-3D: A Comprehensive Embodied World Model Evaluation via 3D Reconstruction
Tianyi Wang, Jiazhou Chen, Yiming Xu +7
Video world models increasingly serve as data engines, action planners, and simulators for embodied AI, but conventional embodied world model (EWM) benchmarks lack a unified 3D-gro…
Solar phased arrays-based wireless power transfer for commercial airlines can reduce energy costs and carbon emissions in the United States
Tianyi Wang, Yiming Xu, Jiseop Byeon +5
Decarbonizing aviation remains challenging because energy-dense jet fuels dominate beyond short-range operations, while batteries impose severe range and payload penalties. Here we…
DBPnet: Damper Characteristics-Based Bayesian Physics-Informed Neural Network for Wheel Load Estimation
Tianyi Wang, Tianyi Zeng, Zimo Zeng +8
Advanced driver assistance systems (ADAS) play an important role in modern automotive intelligence, significantly enhancing vehicle safety and stability. The performance of ADAS cr…
EgoTraj: Real-World Egocentric Human Trajectory Dataset for Multimodal Prediction
Ahmad Yehia, Abduallah Mohamed, Tianyi Wang +4
Accurately forecasting human trajectories from an egocentric perspective plays a central role in applications such as humanoid robotics, wearable sensing systems, and assistive nav…
AERIC: Anticipatory Hidden-State Monitoring for Implicit Harmful Dialogue
Jihyung Park, Saleh Afroogh, Junfeng Jiao
Current language models create two safety challenges: risk must be detected early enough to avoid exposing harmful continuation, and the harmfulness itself may be implicit rather t…
When to Lock Attention: Training-Free KV Control in Video Diffusion
Tianyi Zeng, Jincheng Gao, Tianyi Wang +8
Maintaining background consistency while enhancing foreground quality remains a core challenge in video editing. Injecting full-image information often leads to background artifact…
LLM-MLFFN: Multi-Level Autonomous Driving Behavior Feature Fusion via Large Language Model
Xiangyu Li, Tianyi Wang, Xi Cheng +5
Accurate classification of autonomous vehicle (AV) driving behaviors is critical for safety validation, performance diagnosis, and traffic integration analysis. However, existing a…
ARCAS: An Augmented Reality Collision Avoidance System with SLAM-Based Tracking for Enhancing VRU Safety
Ahmad Yehia, Jiseop Byeon, Tianyi Wang +4
Vulnerable road users (VRUs) face high collision risks in mixed traffic, yet most existing safety systems prioritize driver or vehicle assistance over direct VRU support. This pape…
PILD: Physics-Informed Learning via Diffusion
Tianyi Zeng, Tianyi Wang, Jiaru Zhang +7
Diffusion models have emerged as powerful generative tools for modeling complex data distributions, yet their purely data-driven nature limits applicability in engineering and scie…
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…
SVBRD-LLM: Self-Verifying Behavioral Rule Discovery for Autonomous Vehicle Identification
Xiangyu Li, Tianyi Wang, Junfeng Jiao +2
As autonomous vehicles (AVs) are increasingly deployed on public roads, understanding their real-world behaviors is critical for traffic safety analysis and regulatory oversight. H…
KEPT: Knowledge-Enhanced Prediction of Trajectories from Consecutive Driving Frames with Vision-Language Models
Yujin Wang, Tianyi Wang, Quanfeng Liu +7
Accurate short-horizon trajectory prediction is crucial for safe and reliable autonomous driving. However, existing vision-language models (VLMs) often fail to accurately understan…
Measurement of Born Cross Sections and Effective Form Factors of from = 3.7 to 4.7 GeV
BESIII Collaboration, M. Ablikim, M. N. Achasov +635
Using collision data corresponding to an integrated luminosity of 22.7 fb, collected at center-of-mass energies between 3.7 and 4.7 GeV with the BESIII detector at…
HCOMC: A Hierarchical Cooperative On-Ramp Merging Control Framework in Mixed Traffic Environment on Two-Lane Highways
Tianyi Wang, Yangyang Wang, Jie Pan +2
Highway on-ramp merging areas are common bottlenecks to traffic congestion and accidents. Currently, a cooperative control strategy based on connected and automated vehicles (CAVs)…
TGLD: A Trust-Aware Game-Theoretic Lane-Changing Decision Framework for Automated Vehicles in Heterogeneous Traffic
Jie Pan, Tianyi Wang, Yangyang Wang +2
Automated vehicles (AVs) face a critical need to adopt socially compatible behaviors and cooperate effectively with human-driven vehicles (HVs) in heterogeneous traffic environment…
BIDA: A Bi-level Interaction Decision-making Algorithm for Autonomous Vehicles in Dynamic Traffic Scenarios
Liyang Yu, Tianyi Wang, Junfeng Jiao +3
In complex real-world traffic environments, autonomous vehicles (AVs) need to interact with other traffic participants while making real-time and safety-critical decisions accordin…
RAD: Retrieval-Augmented Decision-Making of Meta-Actions with Vision-Language Models in Autonomous Driving
Yujin Wang, Quanfeng Liu, Zhengxin Jiang +5
Accurately understanding and deciding high-level meta-actions is essential for ensuring reliable and safe autonomous driving systems. While vision-language models (VLMs) have shown…
A Cascading Cooperative Multi-agent Framework for On-ramp Merging Control Integrating Large Language Models
Miao Zhang, Zhenlong Fang, Tianyi Wang +4
Traditional Reinforcement Learning (RL) suffers from replicating human-like behaviors, generalizing effectively in multi-agent scenarios, and overcoming inherent interpretability i…
Damper-B-PINN: Damper Characteristics-Based Bayesian Physics-Informed Neural Network for Vehicle State Estimation
Tianyi Zeng, Tianyi Wang, Zimo Zeng +8
Accurate state estimation is fundamental to intelligent vehicles. Wheel load, one of the most important chassis states, serves as an essential input for advanced driver assistance…