45 citations · 94 across the 29 of their papers we have counts for
11 papers · 1 filter
Measuring What Matters: Scenario-Driven Evaluation for Trajectory Predictors in Autonomous Driving
Longchao Da, David Isele, Hua Wei +1
Being able to anticipate the motion of surrounding agents is essential for the safe operation of autonomous driving systems in dynamic situations. While various methods have been p…
FM-LC: A Hierarchical Framework for Urban Flood Mapping by Land Cover Identification Models
Xin Hong, Longchao Da, Hua Wei
Urban flooding in arid regions poses severe risks to infrastructure and communities. Accurate, fine-scale mapping of flood extents and recovery trajectories is therefore essential…
Joint-Local Grounded Action Transformation for Sim-to-Real Transfer in Multi-Agent Traffic Control
Justin Turnau, Longchao Da, Khoa Vo +4
Traffic Signal Control (TSC) is essential for managing urban traffic flow and reducing congestion. Reinforcement Learning (RL) offers an adaptive method for TSC by responding to dy…
DeepShade: Enable Shade Simulation by Text-conditioned Image Generation
Longchao Da, Xiangrui Liu, Mithun Shivakoti +3
Heatwaves pose a significant threat to public health, especially as global warming intensifies. However, current routing systems (e.g., online maps) fail to incorporate shade infor…
GE-Chat: A Graph Enhanced RAG Framework for Evidential Response Generation of LLMs
Longchao Da, Parth Mitesh Shah, Kuan-Ru Liou +2
Large Language Models are now key assistants in human decision-making processes. However, a common note always seems to follow: "LLMs can make mistakes. Be careful with important i…
A Survey of Sim-to-Real Methods in RL: Progress, Prospects and Challenges with Foundation Models
Longchao Da, Justin Turnau, Thirulogasankar Pranav Kutralingam +3
Deep Reinforcement Learning (RL) has been explored and verified to be effective in solving decision-making tasks in various domains, such as robotics, transportation, recommender s…
Uncertainty Quantification and Confidence Calibration in Large Language Models: A Survey
Xiaoou Liu, Tiejin Chen, Longchao Da +3
Large Language Models (LLMs) excel in text generation, reasoning, and decision-making, enabling their adoption in high-stakes domains such as healthcare, law, and transportation. H…
Generative AI in Transportation Planning: A Survey
Longchao Da, Tiejin Chen, Zhuoheng Li +14
The integration of generative artificial intelligence (GenAI) into transportation planning has the potential to revolutionize tasks such as demand forecasting, infrastructure desig…
Uncertainty Quantification of Large Language Models through Multi-Dimensional Responses
Tiejin Chen, Xiaoou Liu, Longchao Da +3
Large Language Models (LLMs) have demonstrated remarkable capabilities across various tasks due to large training datasets and powerful transformer architecture. However, the relia…
MCQA-Eval: Efficient Confidence Evaluation in NLG with Gold-Standard Correctness Labels
Xiaoou Liu, Zhen Lin, Longchao Da +3
Large Language Models (LLMs) require robust confidence estimation, particularly in critical domains like healthcare and law where unreliable outputs can lead to significant consequ…
Understanding the Uncertainty of LLM Explanations: A Perspective Based on Reasoning Topology
Longchao Da, Xiaoou Liu, Jiaxin Dai +3
Understanding the uncertainty in large language model (LLM) explanations is important for evaluating their faithfulness and reasoning consistency, and thus provides insights into t…