LA-GAT encodes vehicle interactions in dynamic graphs with lane-aware attention bias, pre-trains on NGSIM data then fine-tunes on Chinese UAV merge trajectories, yielding ADE 0.865 m at 1 s and 2.518 m at 3 s on held-out data while tracking TTC and DRAC safety violations.
Ubiquitous Traffic Eyes: trajectory dataset focus on multiple traffic states and state transition on urban expressways
4 Pith papers cite this work. Polarity classification is still indexing.
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The adaptive bounded-rationality model anticipates hazardous takeovers with better coverage and lead time than baselines while aligning inferred parameters with eye-tracking metrics.
NeuroTrace framework builds heterogeneous graphs of inference provenance to detect adversarial examples in DNNs, showing strong transferable performance across attack families in vision and malware domains.
citing papers explorer
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Lane-Aware Graph Attention Network for Multi-Vehicle Trajectory Prediction in Expressway Merge Zones
LA-GAT encodes vehicle interactions in dynamic graphs with lane-aware attention bias, pre-trains on NGSIM data then fine-tunes on Chinese UAV merge trajectories, yielding ADE 0.865 m at 1 s and 2.518 m at 3 s on held-out data while tracking TTC and DRAC safety violations.
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Adaptive Bounded-Rationality Modeling of Early-Stage Takeover in Shared-Control Driving
The adaptive bounded-rationality model anticipates hazardous takeovers with better coverage and lead time than baselines while aligning inferred parameters with eye-tracking metrics.
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NeuroTrace: Inference Provenance-Based Detection of Adversarial Examples
NeuroTrace framework builds heterogeneous graphs of inference provenance to detect adversarial examples in DNNs, showing strong transferable performance across attack families in vision and malware domains.
- Finding Memory Leaks in C/C++ Programs via Neuro-Symbolic Augmented Static Analysis