Ayesha Ishaq — accepted AI-conference papers

2025

DriveLMM-o1: A Step-by-Step Reasoning Dataset and Large Multimodal Model for Driving Scenario Understanding

Ayesha Ishaq, Jean Lahoud, Ketan More, Omkar Thawakar, Ritesh Thawkar, Dinura Dissanayake +7

IROS 2025

While large multimodal models (LMMs) have demonstrated strong performance across various Visual Question Answering (VQA) tasks, certain challenges require complex multi-step reasoning to reach accurate answers. One particularly challenging task is autonomous driving, which demands thorough cognitive

Cited by 32 Source ↗code ↗Scholar ↗

2025

Open3DTrack: Towards Open-Vocabulary 3D Multi-Object Tracking

Ayesha Ishaq, Mohamed El Amine Boudjoghra, Jean Lahoud, Fahad Shahbaz Khan, Salman Khan, Hisham Cholakkal +1

ICRA 2025

3D multi-object tracking plays a critical role in autonomous driving by enabling the real-time monitoring and prediction of multiple objects' movements. Traditional 3D tracking systems are typically constrained by predefined object categories, limiting their adaptability to novel, unseen objects in

Cited by 3 Source ↗code ↗Scholar ↗