94 citations · 175 across the 11 of their papers we have counts for
11 papers
World Models as Adversaries: Multi-Agent Self-Play Fine-Tuning for Robust Motion Planning
Tong Nie, Yuewen Mei, Junlin He +3
Robust motion planning in dense traffic requires autonomous vehicles to interact in rare and safety-critical scenarios that are underrepresented in naturalistic driving data. Altho…
Steerable Adversarial Scenario Generation through Test-Time Preference Alignment
Tong Nie, Yuewen Mei, Yihong Tang +5
Adversarial scenario generation is a cost-effective approach for safety assessment of autonomous driving systems. However, existing methods are often constrained to a single, fixed…
Exploring the Roles of Large Language Models in Reshaping Transportation Systems: A Survey, Framework, and Roadmap
Tong Nie, Jian Sun, Wei Ma
Modern transportation systems face pressing challenges due to increasing demand, dynamic environments, and heterogeneous information integration. The rapid evolution of Large Langu…
Collaborative Imputation of Urban Time Series through Cross-city Meta-learning
Tong Nie, Wei Ma, Jian Sun +2
Urban time series, such as mobility flows, energy consumption, and pollution records, encapsulate complex urban dynamics and structures. However, data collection in each city is im…
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…
Generalizable Implicit Neural Representation As a Universal Spatiotemporal Traffic Data Learner
Tong Nie, Guoyang Qin, Wei Ma +1
. Spatiotemporal Traffic Data (STTD) meas…
Spatiotemporal Implicit Neural Representation as a Generalized Traffic Data Learner
Tong Nie, Guoyang Qin, Wei Ma +1
Spatiotemporal Traffic Data (STTD) measures the complex dynamical behaviors of the multiscale transportation system. Existing methods aim to reconstruct STTD using low-dimensional…
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…
Towards better traffic volume estimation: Jointly addressing the underdetermination and nonequilibrium problems with correlation-adaptive GNNs
Tong Nie, Guoyang Qin, Yunpeng Wang +1
Traffic volume is an indispensable ingredient to provide fine-grained information for traffic management and control. However, due to limited deployment of traffic sensors, obtaini…