activity
20242026
collaborators

6 papers

cs.AI2026

Choose Your Game Wisely: Measuring Game-Theoretic Structures in Real-World Vehicle Interactions

Yueyuan Li, Rongcheng Nie, Weijie Xi +4

Game-theoretic models provide principled frameworks for modeling vehicle interactions, but their underlying temporal assumptions have not been systematically examined against real-…

cs.RO2026

RoadWeaver: Large-Scale Lane-Level HD Map Generation from Scratch for Autonomous Driving Simulation

Yueyuan Li, Zexi Chen, Weijie Xi +4

Autonomous driving simulation requires diverse and scalable lane-level HD maps to support long-horizon evaluation across complex road networks. Existing approaches either rely on h…

cs.RO2026

Generating Roadside LiDAR Datasets from Vehicle-Side Datasets via Novel View Synthesis

Yuhan Xia, Runxin Zhao, Hanyang Zhuang +2

Intelligent Transportation Systems (ITS) require reliable environmental perception to support safe and efficient transportation. With the rapid development of Vehicle-to-everything…

cs.CV2025

Which LiDAR scanning pattern is better for roadside perception: Repetitive or Non-repetitive?

Zhiqi Qi, Runxin Zhao, Hanyang Zhuang +2

LiDAR-based roadside perception is a cornerstone of advanced Intelligent Transportation Systems (ITS). While considerable research has addressed optimal LiDAR placement for infrast…

cs.RO2025

Bench-RNR: Dataset for Benchmarking Repetitive and Non-repetitive Scanning LiDAR for Infrastructure-based Vehicle Localization

Runxin Zhao, Chunxiang Wang, Hanyang Zhuang +1

Vehicle localization using roadside LiDARs can provide centimeter-level accuracy for cloud-controlled vehicles while simultaneously serving multiple vehicles, enhanc-ing safety and…

cs.CV2024

SAM4UDASS: When SAM Meets Unsupervised Domain Adaptive Semantic Segmentation in Intelligent Vehicles

Weihao Yan, Yeqiang Qian, Xingyuan Chen +3

Semantic segmentation plays a critical role in enabling intelligent vehicles to comprehend their surrounding environments. However, deep learning-based methods usually perform poor…