A generative video synthesis pipeline paired with a semantic graph neural network yields gains in accident anticipation accuracy and lead time on driving datasets, accompanied by a new benchmark release.
In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp
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FocalAD adds an ego-local graph interactor and focal loss to prioritize decision-critical neighbors, yielding lower collision rates than prior methods on nuScenes, Bench2Drive, and especially the Adv-nuScenes robustness set.
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Learning from the Unseen: Generative Data Augmentation for Geometric-Semantic Accident Anticipation
A generative video synthesis pipeline paired with a semantic graph neural network yields gains in accident anticipation accuracy and lead time on driving datasets, accompanied by a new benchmark release.
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FocalAD: Local Motion Planning for End-to-End Autonomous Driving
FocalAD adds an ego-local graph interactor and focal loss to prioritize decision-critical neighbors, yielding lower collision rates than prior methods on nuScenes, Bench2Drive, and especially the Adv-nuScenes robustness set.