4 papers
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…
X-Blocks: Linguistic Building Blocks of Natural Language Explanations for Automated Vehicles
Ashkan Y. Zadeh, Xiaomeng Li, Andry Rakotonirainy +3
Natural language explanations play a critical role in establishing trust and acceptance of automated vehicles (AVs), yet existing approaches lack systematic frameworks for analysin…
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…