AtteConDA: Attention-Based Conflict Suppression in Multi-Condition Diffusion Models

Researchers have introduced AtteConDA, a novel framework designed to enhance synthetic data augmentation for high-level autonomous driving tasks. While conditional image generation improves controllability, using multiple conditions like sketches and depth maps often leads to conflicts that disrupt structural preservation. This study addresses this challenge by inputting semantic segmentation, depth, and edges into a multi-condition diffusion model. The core innovation is an attention-based mechanism that suppresses conflicts among these diverse structural cues, ensuring generated images faithfully retain the detailed high-level structure of original scenes. This approach is critical for tasks such as traffic-rule extraction and driving-behavior understanding, where simple annotations are insufficient. The authors also established a comprehensive generation framework and evaluation protocol specifically for driving scenarios, facilitating future comparisons. By effectively mitigating condition conflicts, AtteConDA significantly improves the quality of synthetic training data, offering a vital solution to data scarcity in autonomous driving research and advancing the field of conditional image generation.