10 papers
Bridging Local Observation and Global Simulation in Closed-Loop Traffic Modeling
Ziyan Wang, Tan Xiang, Peng Chen +1
A local-to-global context mismatch arises when autoregressive traffic simulators trained on ego-centric driving logs are deployed in globally observable closed-loop environments. I…
Long-term Traffic Simulation via Structured Autoregressive Modeling
Lingyu Xiao, Zexin Feng, Xintao Yan
Interactive traffic simulation is a vital world model for autonomous driving. A central challenge in long-horizon simulation is modeling sustained multi-agent interactions, which i…
Horizon Adaptive Offline Policy Learning via Value Stitching
Kexin Zheng, Xianyuan Zhan, Xintao Yan
Learning accurate value functions plays a decisive role for reinforcement learning (RL) agents to solve long-horizon, complex tasks. Conventional temporal-difference (TD) learning…
Learning Responsibility-Attributed Adversarial Scenarios for Testing Autonomous Vehicles
Yizhuo Xiao, Haotian Yan, Ying Wang +5
Establishing trustworthy safety assurance for autonomous driving systems (ADSs) requires evidence that failures arise from avoidable system deficiencies rather than unavoidable tra…
TeraSim-World: Worldwide Safety-Critical Data Synthesis for End-to-End Autonomous Driving
Jiawei Wang, Haowei Sun, Xintao Yan +3
Safe and scalable deployment of end-to-end (E2E) autonomous driving requires extensive and diverse data, particularly safety-critical events. Existing data are mostly generated fro…
Improving Traffic Signal Data Quality for the Waymo Open Motion Dataset
Xintao Yan, Erdao Liang, Jiawei Wang +2
Datasets pertaining to autonomous vehicles (AVs) hold significant promise for a range of research fields, including artificial intelligence (AI), autonomous driving, and transporta…