5 papers
DecoupleGS: Interactive 3D Gaussian Splatting for End-to-End Autonomous Driving Testing
Siying Li, Ying Ni, Jie Sun +2
End-to-end (E2E) autonomous driving algorithms require rigorous closed-loop validation in simulation environments offering high visual fidelity, strong interactivity, and real-time…
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…
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…
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…
WGSR-Bench: Wargame-based Game-theoretic Strategic Reasoning Benchmark for Large Language Models
Qiyue Yin, Pei Xu, Qiaozhe Li +15
Recent breakthroughs in Large Language Models (LLMs) have led to a qualitative leap in artificial intelligence' s performance on reasoning tasks, particularly demonstrating remarka…