3 citations · 3 across the 3 of their papers we have counts for
8 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…
Small Talk, Big Impact? LLM-based Conversational Agents to Mitigate Passive Fatigue in Conditional Automated Driving
Lewis Cockram, Yueteng Yu, Jorge Pardo +6
Passive fatigue during conditional automated driving can compromise driver readiness and safety. This paper presents findings from a test-track study with 40 participants in a real…
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…
Zero-Shot Scene Understanding with Multimodal Large Language Models for Automated Vehicles
Mohammed Elhenawy, Shadi Jaradat, Taqwa I. Alhadidi +4
Scene understanding is critical for various downstream tasks in autonomous driving, including facilitating driver-agent communication and enhancing human-centered explainability of…
Vision-Language Models for Autonomous Driving: CLIP-Based Dynamic Scene Understanding
Mohammed Elhenawy, Huthaifa I. Ashqar, Andry Rakotonirainy +3
Scene understanding is essential for enhancing driver safety, generating human-centric explanations for Automated Vehicle (AV) decisions, and leveraging Artificial Intelligence (AI…
Visual Reasoning and Multi-Agent Approach in Multimodal Large Language Models (MLLMs): Solving TSP and mTSP Combinatorial Challenges
Mohammed Elhenawy, Ahmad Abutahoun, Taqwa I. Alhadidi +6
Multimodal Large Language Models (MLLMs) harness comprehensive knowledge spanning text, images, and audio to adeptly tackle complex problems, including zero-shot in-context learnin…