VISTA-DZ converts trajectories to visual semantic profiles via vision-language models to condition a GRU-attention network for personalized dilemma-zone stop-go and timing prediction, reporting 93%+ accuracies on simulation data.
arXiv preprint arXiv:2503.06477 (2025)
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
years
2026 2representative citing papers
Person2Drive adds a 50-driver closed-loop CARLA dataset, MMDSS/KL style metrics, and a head-only fine-tuning method that shifts an end-to-end driving policy toward a target driver's style.
citing papers explorer
-
VISTA-DZ: Visual Semantic Trajectory Adaptation for Personalized Dilemma Zone Prediction
VISTA-DZ converts trajectories to visual semantic profiles via vision-language models to condition a GRU-attention network for personalized dilemma-zone stop-go and timing prediction, reporting 93%+ accuracies on simulation data.
-
Driving Like Yourself: A Benchmark for Closed-Loop Personalized End-to-End Autonomous Driving
Person2Drive adds a 50-driver closed-loop CARLA dataset, MMDSS/KL style metrics, and a head-only fine-tuning method that shifts an end-to-end driving policy toward a target driver's style.