Publications (11)
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…
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…
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…
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…
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…
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…