6 papers
Modeling Vehicle-Type-Specific Pedestrian Crash Avoidance Behavior in Safety-Critical Interactions Using Smooth-Mamba Deep Reinforcement Learning
Qingwen Pu, Kun Xie, Hong Yang +2
As automated vehicles (AVs) increasingly share roadways with human-driven vehicles (HDVs), understanding how pedestrians respond to different vehicle types in safety-critical inter…
Generating Realistic Safety-Critical Scenarios for Vehicle-Pedestrian Interactions
Qingwen Pu, Kun Xie, Yuan Zhu +1
Automated driving system deployment requires rigorous validation across safety-critical vehicle-pedestrian interactions, yet real-world datasets rarely capture high-risk scenarios…
TransResAI: A Compound AI System for Coastal Transportation Resilience
Qingwen Pu, Kun Xie, Chenyu Yan
Coastal flooding increasingly threatens transportation infrastructure, yet the analytical tools needed for resilience management remain difficult for many non-specialist practition…
VLM-VPI: A Vision-Language Reasoning Framework for Improving Automated Vehicle-Pedestrian Interactions
Qingwen Pu, Kun Xie, Yuxiang Liu
Autonomous driving systems often infer pedestrian yielding behavior from geometric and kinematic cues alone, limiting their ability to reason about visual scene context and age-dep…
A Vision-and-Knowledge Enhanced Large Language Model for Generalizable Pedestrian Crossing Behavior Inference
Qingwen Pu, Kun Xie, Hong Yang +1
Existing paradigms for inferring pedestrian crossing behavior, ranging from statistical models to supervised learning methods, demonstrate limited generalizability and perform inad…
Drone Data Analytics for Measuring Traffic Metrics at Intersections in High-Density Areas
Qingwen Pu, Yuan Zhu, Junqing Wang +3
This study employed over 100 hours of high-altitude drone video data from eight intersections in Hohhot to generate a unique and extensive dataset encompassing high-density urban r…