1 citations · 1 across the 6 of their papers we have counts for
4 papers · 1 filter
NARRATE: A Multimodal Real-World Australian Driving Dataset for Human-Centred Explanations in Automated Driving
Ashkan Yousefi Zadeh, Zishuo Zhu, Xiaomeng Li +5
Automated vehicles must explain their decisions in ways that passengers can understand, monitor, and trust. Existing language-annotated driving datasets are mostly observer-written…
FRED: A Multi-Modal Autonomous Driving Dataset for Flooded Road Environments
Connor Malone, Sebastien Demmel, Sebastien Glaser
The Flooded Road Environments Dataset (FRED) is, to our knowledge, the first multi-modal autonomous driving dataset specifically targeting the collection of data from scenarios inv…
Saliency-Guided Domain Adaptation for Left-Hand Driving in Autonomous Steering
Zahra Mehraban, Sebastien Glaser, Michael Milford +1
Domain adaptation is required for automated driving models to generalize well across diverse road conditions. This paper explores a training method for domain adaptation to adapt P…
PIP-Net: Pedestrian Intention Prediction in the Wild
Mohsen Azarmi, Mahdi Rezaei, He Wang
Accurate pedestrian intention prediction (PIP) by Autonomous Vehicles (AVs) is one of the current research challenges in this field. In this article, we introduce PIP-Net, a novel…