DriveLM

The 'DriveLM' dataset contains images related to autonomous driving scenarios, which are used to evaluate the robustness of Vision-Language Models against backdoor attacks.

Papers using DriveLM (6)

Multimodal Backdoor Attack on VLMs for Autonomous Driving via Graffiti and Cross-Lingual Triggers2026MMDrive: Interactive Scene Understanding Beyond Vision with Multi-representational Fusion2025Natural Reflection Backdoor Attack On Vision Language Model For Autonomous Driving2025TS-VLM: Text-Guided SoftSort Pooling for Vision-Language Models in Multi-View Driving Reasoning2025ReasonDrive: Efficient Visual Question Answering for Autonomous Vehicles with Reasoning-Enhanced Small Vision-Language Models2025Multi-Frame, Lightweight & Efficient Vision-Language Models for Question Answering in Autonomous Driving2024 · 6 cites