2 citations · 2 across the 26 of their papers we have counts for
6 papers · 2 filters
SafeCoop: Unravelling Full Stack Safety in Agentic Collaborative Driving
Xiangbo Gao, Tzu-Hsiang Lin, Ruojing Song +6
Collaborative driving systems leverage vehicle-to-everything (V2X) communication across multiple agents to enhance driving safety and efficiency. Traditional V2X systems take raw s…
Background Fades, Foreground Leads: Curriculum-Guided Background Pruning for Efficient Foreground-Centric Collaborative Perception
Yuheng Wu, Xiangbo Gao, Quang Tau +2
Collaborative perception enhances the reliability and spatial coverage of autonomous vehicles by sharing complementary information across vehicles, offering a promising solution to…
AirV2X: Unified Air-Ground Vehicle-to-Everything Collaboration
Xiangbo Gao, Yuheng Wu, Fengze Yang +7
While multi-vehicular collaborative driving demonstrates clear advantages over single-vehicle autonomy, traditional infrastructure-based V2X systems remain constrained by substanti…
DRAMA-X: A Fine-grained Intent Prediction and Risk Reasoning Benchmark For Driving
Mihir Godbole, Xiangbo Gao, Zhengzhong Tu
Understanding the short-term motion of vulnerable road users (VRUs) like pedestrians and cyclists is critical for safe autonomous driving, especially in urban scenarios with ambigu…
Generative AI for Autonomous Driving: Frontiers and Opportunities
Yuping Wang, Shuo Xing, Cui Can +46
Generative Artificial Intelligence (GenAI) constitutes a transformative technological wave that reconfigures industries through its unparalleled capabilities for content creation,…
STAMP: Scalable Task And Model-agnostic Collaborative Perception
Xiangbo Gao, Runsheng Xu, Jiachen Li +3
Perception is crucial for autonomous driving, but single-agent perception is often constrained by sensors' physical limitations, leading to degraded performance under severe occlus…