94 citations · 324 across the 18 of their papers we have counts for
18 papers
From Attacks to Curricula: Learnability-Guided Adversarial Training for Safe Autonomous Driving
Yuewen Mei, Tong Nie, Jie Sun +3
Closed-loop adversarial training improves autonomous driving safety by exposing policies to rare safety-critical scenarios. Standard pipelines first generate adversarial scenarios…
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…
Predicting Large-scale Urban Network Dynamics with Energy-informed Graph Neural Diffusion
Tong Nie, Jian Sun, Wei Ma
Networked urban systems facilitate the flow of people, resources, and services, and are essential for economic and social interactions. These systems often involve complex processe…
Seeking to Collide: Online Safety-Critical Scenario Generation for Autonomous Driving with Retrieval Augmented Large Language Models
Yuewen Mei, Tong Nie, Jian Sun +1
Simulation-based testing is crucial for validating autonomous vehicles (AVs), yet existing scenario generation methods either overfit to common driving patterns or operate in an of…
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…
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…
Geolocation Representation from Large Language Models are Generic Enhancers for Spatio-Temporal Learning
Junlin He, Tong Nie, Wei Ma
In the geospatial domain, universal representation models are significantly less prevalent than their extensive use in natural language processing and computer vision. This discrep…
High-Dimensional Fault Tolerance Testing of Highly Automated Vehicles Based on Low-Rank Models
Yuewen Mei, Tong Nie, Jian Sun +1
Ensuring fault tolerance of Highly Automated Vehicles (HAVs) is crucial for their safety due to the presence of potentially severe faults. Hence, Fault Injection (FI) testing is co…
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…
Correlating sparse sensing for large-scale traffic speed estimation: A Laplacian-enhanced low-rank tensor kriging approach
Tong Nie, Guoyang Qin, Yunpeng Wang +1
Traffic speed is central to characterizing the fluidity of the road network. Many transportation applications rely on it, such as real-time navigation, dynamic route planning, and…
Truncated tensor Schatten p-norm based approach for spatiotemporal traffic data imputation with complicated missing patterns
Tong Nie, Guoyang Qin, Jian Sun
Rapid advances in sensor, wireless communication, cloud computing and data science have brought unprecedented amount of data to assist transportation engineers and researchers in m…