94 citations · 156 across the 12 of their papers we have counts for
6 papers · 1 filter
ADV-0: Closed-Loop Min-Max Adversarial Training for Long-Tail Robustness in Autonomous Driving
Tong Nie, Yihong Tang, Junlin He +5
Deploying autonomous driving systems requires robustness against long-tail scenarios that are rare but safety-critical. While adversarial training offers a promising solution, exis…
LLM-attacker: Enhancing Closed-loop Adversarial Scenario Generation for Autonomous Driving with Large Language Models
Yuewen Mei, Tong Nie, Jian Sun +1
Ensuring and improving the safety of autonomous driving systems (ADS) is crucial for the deployment of highly automated vehicles, especially in safety-critical events. To address t…
Joint Estimation and Prediction of City-wide Delivery Demand: A Large Language Model Empowered Graph-based Learning Approach
Tong Nie, Junlin He, Yuewen Mei +4
The proliferation of e-commerce and urbanization has significantly intensified delivery operations in urban areas, boosting the volume and complexity of delivery demand. Data-drive…
Channel-Aware Low-Rank Adaptation in Time Series Forecasting
Tong Nie, Yuewen Mei, Guoyang Qin +2
The balance between model capacity and generalization has been a key focus of recent discussions in long-term time series forecasting. Two representative channel strategies are clo…
ImputeFormer: Low Rankness-Induced Transformers for Generalizable Spatiotemporal Imputation
Tong Nie, Guoyang Qin, Wei Ma +2
Missing data is a pervasive issue in both scientific and engineering tasks, especially for the modeling of spatiotemporal data. This problem attracts many studies to contribute to…
Contextualizing MLP-Mixers Spatiotemporally for Urban Data Forecast at Scale
Tong Nie, Guoyang Qin, Lijun Sun +3
Spatiotemporal traffic data (STTD) displays complex correlational structures. Extensive advanced techniques have been designed to capture these structures for effective forecasting…