108 citations · 184 across the 12 of their papers we have counts for
14 papers
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…
LUNA-AD: Lightweight Uncertainty-Aware Language Model with Lifelong Learning for Autonomous Driving
Ruoyu Yao, Pei Liu, Ruiguo Zhong +3
While large language models (LLMs) offer promising reasoning capabilities, their integration into safety-critical driving systems is hindered by limited reasoning diversity, high c…
Decision-Making with Lightweight Confidence-Aware Language Model for Autonomous Driving
Ruoyu Yao, Ruiguo Zhong, Pei Liu +3
Large Language Models (LLMs) and Multimodal LLMs (MLLMs) have demonstrated immense potential in autonomous driving (AD) by offering human-like reasoning and open-world generalizati…
Language-Grounded Multi-Agent Planning for Personalized and Fair Participatory Urban Sensing
Xusen Guo, Mingxing Peng, Hongliang Lu +3
Participatory urban sensing leverages human mobility for large-scale urban data collection, yet existing methods typically rely on centralized optimization and assume homogeneous p…
Coordinated Pandemic Control with Large Language Model Agents as Policymaking Assistants
Ziyi Shi, Xusen Guo, Hongliang Lu +7
Effective pandemic control requires timely and coordinated policymaking across administrative regions that are intrinsically interdependent. However, human-driven responses are oft…
nuPlan-R: A Closed-Loop Planning Benchmark for Autonomous Driving via Reactive Multi-Agent Simulation
Mingxing Peng, Ruoyu Yao, Xusen Guo +1
Recent advances in closed-loop planning benchmarks have significantly improved the evaluation of autonomous vehicles. However, existing benchmarks still rely on rule-based reactive…
AgentSense: LLMs Empower Generalizable and Explainable Web-Based Participatory Urban Sensing
Xusen Guo, Mingxing Peng, Xixuan Hao +4
Web-based participatory urban sensing has emerged as a vital approach for modern urban management by leveraging mobile individuals as distributed sensors. However, existing urban s…
LD-Scene: LLM-Guided Diffusion for Controllable Generation of Adversarial Safety-Critical Driving Scenarios
Mingxing Peng, Yuting Xie, Xusen Guo +3
Ensuring the safety and robustness of autonomous driving systems necessitates a comprehensive evaluation in safety-critical scenarios. However, these safety-critical scenarios are…
Safety-Critical Traffic Simulation with Guided Latent Diffusion Model
Mingxing Peng, Ruoyu Yao, Xusen Guo +3
Safety-critical traffic simulation plays a crucial role in evaluating autonomous driving systems under rare and challenging scenarios. However, existing approaches often generate u…
Automating Traffic Model Enhancement with AI Research Agent
Xusen Guo, Xinxi Yang, Mingxing Peng +3
Developing efficient traffic models is crucial for optimizing modern transportation systems. However, current modeling approaches remain labor-intensive and prone to human errors d…
Diffusion Models for Intelligent Transportation Systems: A Survey
Mingxing Peng, Kehua Chen, Xusen Guo +4
Intelligent Transportation Systems (ITS) are vital in modern traffic management and optimization, significantly enhancing traffic efficiency and safety. Recently, diffusion models…
GenFollower: Enhancing Car-Following Prediction with Large Language Models
Xianda Chen, Mingxing Peng, PakHin Tiu +4
Accurate modeling of car-following behaviors is essential for various applications in traffic management and autonomous driving systems. However, current approaches often suffer fr…
Towards Explainable Traffic Flow Prediction with Large Language Models
Xusen Guo, Qiming Zhang, Junyue Jiang +4
Traffic forecasting is crucial for intelligent transportation systems. It has experienced significant advancements thanks to the power of deep learning in capturing latent patterns…
LC-LLM: Explainable Lane-Change Intention and Trajectory Predictions with Large Language Models
Mingxing Peng, Xusen Guo, Xianda Chen +2
To ensure safe driving in dynamic environments, autonomous vehicles should possess the capability to accurately predict lane change intentions of surrounding vehicles in advance an…