NARRATE: A Multimodal Real-World Australian Driving Dataset for Human-Centred Explanations in Automated Driving

[2608.14767] NARRATE: A Multimodal Real-World Australian Driving Dataset for Human-Centred Explanations in Automated Driving Skip to main content Search Submit Donate Log in Search arXiv Press Enter to search · Advanced search Computer Science > Computer Vision and Pattern Recognition arXiv:2608.14767 (cs) [Submitted on 14 Aug 2026] Title: NARRATE: A Multimodal Real-World Australian Driving Dataset for Human-Centred Explanations in Automated Driving Authors: Ashkan Yousefi Zadeh , Zishuo Zhu , Xiaomeng Li , Andry Rakotonirainy , Sebastien Glaser , Ronald Schroeter , Patricia Delhomme , Zahra Mehraban View a PDF of the paper titled NARRATE: A Multimodal Real-World Australian Driving Dataset for Human-Centred Explanations in Automated Driving, by Ashkan Yousefi Zadeh and 7 other authors View PDF HTML (experimental) Abstract: Automated vehicles must explain their decisions in ways that passengers can understand, monitor, and trust. Existing language-annotated driving datasets are mostly observer-written, post-hoc, simulation-based, or generated from sensor inputs, rather than elicited from the driver performing the action. We introduce NARRATE, a multimodal real-world Australian driving dataset comprising 2,050 annotated events from 35 experienced drivers and driving instructors on public roads. Each event is grounded in synchronised visual, localisation, motion, and LiDAR streams and paired with in-vehicle and/or post-drive free-text explanations. NARRATE provides action labels, scenario-context labels spanning six high-level and 32 fine-grained categories, and span-level Situational Awareness (SA) annotations over driver explanations for Perception, Comprehension and Projection. Four benchmark tasks (SA, scenario-context, driver-action classification, and explanation generation) show that this structure is learnable from driver language, while fine-grained context recognition and explanation generation remain challenging. NARRATE paves a path towards more human-centred and domain-aware explanation models for automated driving. Comments: Accepted at The 19th European Conference on Computer Vision (ECCV 2026) DriveX Workshop (Foundation Models for Autonomous Driving) Subjects: Computer Vision and Pattern Recognition (cs.CV) ; Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Robotics (cs.RO) Cite as: arXiv:2608.14767 [cs.CV] (or arXiv:2608.14767v1 [cs.CV] for this version) https://doi.org/10.48550/arXiv.2608.14767 Focus to learn more arXiv-issued DOI via DataCite Submission history From: Ashkan Yousefi Zadeh [ view email ] [v1] Fri, 14 Aug 2026 12:35:24 UTC (14,780 KB) Full-text links: Access Paper: View a PDF of the paper titled NARRATE: A Multimodal Real-World Australian Driving Dataset for Human-Centred Explanations in Automated Driving, by Ashkan Yousefi Zadeh and 7 other authors View PDF HTML (experimental) TeX Source view license Current browse context: cs.CV < prev | next > new | recent | 2026-08 Change to browse by: cs cs.AI cs.CL cs.RO References & Citations NASA ADS Google Scholar Semantic Scholar export BibTeX citation Loading... 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