10 citations · 12 across the 5 of their papers we have counts for
5 papers
TrafficAlign: Aligning Large Language Models for Traffic Scenario Generation
Zhi Tu, Liangkun Niu, Tianyi Zhang
Recent research has investigated the use of large language models (LLMs) to generate traffic scenarios for autonomous driving. However, pretrained LLMs often fail to align with rea…
Multi-modal Traffic Scenario Generation for Autonomous Driving System Testing
Zhi Tu, Liangkun Niu, Wei Fan +1
Autonomous driving systems (ADS) require extensive testing and validation before deployment. However, it is tedious and time-consuming to construct traffic scenarios for ADS testin…
HEPHA: A Mixed-Initiative Image Labeling Tool for Specialized Domains
Shiyuan Zhou, Bingxuan Li, Xiyuan Chen +4
Image labeling is an important task for training computer vision models. In specialized domains, such as healthcare, it is expensive and challenging to recruit specialists for imag…
DL3DV-10K: A Large-Scale Scene Dataset for Deep Learning-based 3D Vision
Lu Ling, Yichen Sheng, Zhi Tu +17
We have witnessed significant progress in deep learning-based 3D vision, ranging from neural radiance field (NeRF) based 3D representation learning to applications in novel view sy…
TARGET: Automated Scenario Generation from Traffic Rules for Testing Autonomous Vehicles via Validated LLM-Guided Knowledge Extraction
Yao Deng, Jiaohong Yao, Zhi Tu +3
Recent incidents with autonomous vehicles highlight the need for rigorous testing to ensure safety and robustness. Constructing test scenarios for autonomous driving systems (ADSs)…