18 citations · 23 across the 13 of their papers we have counts for
18 papers
Behavioral Safety Assessment towards Large-scale Deployment of Autonomous Vehicles, Part II: Assessment Results
Henry X. Liu, Tinghan Wang, Xintao Yan +6
Third-party evaluations of autonomous vehicle (AV) safety can play a vital role in improving public acceptance, building consumer confidence, and establishing effective safety stan…
Behavioral Safety Assessment towards Large-scale Deployment of Autonomous Vehicles, Part I: Methodology
Henry X. Liu, Tinghan Wang, Xintao Yan +6
Autonomous vehicles (AVs) have significantly advanced in real-world deployment in recent years, yet safety continues to be a critical barrier to widespread adoption. Traditional fu…
Human Behavior-Informed Crash Scenario Generation with Real-World Crash Priors for Autonomous Vehicle Safety Evaluation
Mingxing Peng, Xusen Guo, Long Chen +3
Reliable safety evaluation of autonomous vehicles (AVs) is essential to improving road safety, yet it depends critically on realistic simulation of rare crashes. Existing crash sce…
Copper-Policy: Focus on the Representation for Robust Robot Manipulation
Zexin Feng, Yixu Feng, Lingyu Xiao +6
World Action Models (WAMs) acquire behavioral priors by modeling future scene evolution, but predicting detailed futures in pixel or latent space incurs substantial cost. Recent ev…
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…
Behavioral Safety Assessment towards Large-scale Deployment of Autonomous Vehicles
Henry X. Liu, Xintao Yan, Haowei Sun +7
Autonomous vehicles (AVs) have significantly advanced in real-world deployment in recent years, yet safety continues to be a critical barrier to widespread adoption. Traditional fu…
RADE: Learning Risk-Adjustable Driving Environment via Multi-Agent Conditional Diffusion
Jiawei Wang, Xintao Yan, Yao Mu +3
Generating safety-critical scenarios in high-fidelity simulations offers a promising and cost-effective approach for efficient testing of autonomous vehicles. Existing methods typi…
TeraSim: Uncovering Unknown Unsafe Events for Autonomous Vehicles through Generative Simulation
Haowei Sun, Xintao Yan, Zhijie Qiao +14
Traffic simulation is essential for autonomous vehicle (AV) development, enabling comprehensive safety evaluation across diverse driving conditions. However, traditional rule-based…
CSDO: Enhancing Efficiency and Success in Large-Scale Multi-Vehicle Trajectory Planning
Yibin Yang, Shaobing Xu, Xintao Yan +3
This paper presents an efficient algorithm, naming Centralized Searching and Decentralized Optimization (CSDO), to find feasible solution for large-scale Multi-Vehicle Trajectory P…
Evaluation of automated driving system safety metrics with logged vehicle trajectory data
Xintao Yan, Shuo Feng, David J. LeBlanc +2
Real-time safety metrics are important for the automated driving system (ADS) to assess the risk of driving situations and to assist the decision-making. Although a number of real-…
A probabilistic model for missing traffic volume reconstruction based on data fusion
Xintao Yan, Yan Zhao, Henry X. Liu
Traffic volume information is critical for intelligent transportation systems. It serves as a key input to transportation planning, roadway design, and traffic signal control. Howe…
Corner Case Generation and Analysis for Safety Assessment of Autonomous Vehicles
Haowei Sun, Shuo Feng, Xintao Yan +1
Testing and evaluation is a crucial step in the development and deployment of Connected and Automated Vehicles (CAVs). To comprehensively evaluate the performance of CAVs, it is of…
Distributionally Consistent Simulation of Naturalistic Driving Environment for Autonomous Vehicle Testing
Xintao Yan, Shuo Feng, Haowei Sun +1
Microscopic traffic simulation provides a controllable, repeatable, and efficient testing environment for autonomous vehicles (AVs). To evaluate AVs' safety performance unbiasedly,…