20 papers
SWIFT: A Small-World Interaction Framework for Flow-Aware Trajectory Prediction in Autonomous Driving
Chengyue Wang, Bin Rao, Haicheng Liao +3
Accurate trajectory prediction in autonomous driving hinges on modeling dynamic and context-dependent interactions among traffic agents. However, most existing approaches are purel…
TRACER: Training-Free Closed-Loop Structured Inference for Traffic Accident Reconstruction
Yanchen Guan, Chengyue Wang, Bin Rao +5
Traffic accident reconstruction is a forensic inverse problem that requires recovering physically consistent motion from sparse and heterogeneous evidence. Existing learning-based…
Think Before You Drive: World Model-Inspired Multimodal Grounding for Autonomous Vehicles
Haicheng Liao, Huanming Shen, Bonan Wang +8
Interpreting natural-language commands to localize target objects is critical for autonomous driving (AD). Existing visual grounding (VG) methods for autonomous vehicles (AVs) typi…
Learning from the Unseen: Generative Data Augmentation for Geometric-Semantic Accident Anticipation
Yanchen Guan, Haicheng Liao, Chengyue Wang +4
Anticipating traffic accidents is a critical yet unresolved problem for autonomous driving, hindered by the inherent complexity of modeling interactions between road users and the…
Learning physically grounded traffic accident reconstruction from public accident reports
Yanchen Guan, Haicheng Liao, Chengyue Wang +1
Traffic accidents are routinely documented in textual reports, yet physically grounded accident reconstruction remains difficult because detailed scene measurements and expert reco…
SAIL: Scene-aware Adaptive Iterative Learning for Long-Tail Trajectory Prediction in Autonomous Vehicles
Bin Rao, Haicheng Liao, Chengyue Wang +3
Autonomous vehicles (AVs) rely on accurate trajectory prediction for safe navigation in diverse traffic environments, yet existing models struggle with long-tail scenarios-rare but…