{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,7,30]],"date-time":"2025-07-30T16:04:30Z","timestamp":1753891470820,"version":"3.41.2"},"reference-count":61,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2025,5,30]],"date-time":"2025-05-30T00:00:00Z","timestamp":1748563200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Neurorobot."],"abstract":"<jats:p>Reliable visual perception is essential for autonomous driving test scenario generation, yet adverse weather and lighting variations pose significant challenges to simulation robustness and generalization. Traditional unpaired image-to-image translation methods primarily rely on RGB-based transformations, often resulting in geometric distortions and loss of structural consistency, which can negatively impact the realism and accuracy of generated test scenarios. To address these limitations, we propose a Depth-Aware Dual-Branch Generative Adversarial Network (DAB-GAN) that explicitly incorporates depth information to preserve spatial structures during scenario generation. The dual-branch generator processes both RGB and depth inputs, ensuring geometric fidelity, while a self-attention mechanism enhances spatial dependencies and local detail refinement. This enables the creation of realistic and structure-preserving test environments that are crucial for evaluating autonomous driving perception systems, especially under adverse weather conditions. Experimental results demonstrate that DAB-GAN outperforms existing unpaired image-to-image translation methods, achieving superior visual fidelity and maintaining depth-aware structural integrity. This approach provides a robust framework for generating diverse and challenging test scenarios, enhancing the development and validation of autonomous driving systems under various real-world conditions.<\/jats:p>","DOI":"10.3389\/fnbot.2025.1603964","type":"journal-article","created":{"date-parts":[[2025,5,30]],"date-time":"2025-05-30T06:11:41Z","timestamp":1748585501000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Depth-aware unpaired image-to-image translation for autonomous driving test scenario generation using a dual-branch GAN"],"prefix":"10.3389","volume":"19","author":[{"given":"Donghao","family":"Shi","sequence":"first","affiliation":[]},{"given":"Chenxin","family":"Zhao","sequence":"additional","affiliation":[]},{"given":"Cunbin","family":"Zhao","sequence":"additional","affiliation":[]},{"given":"Zhou","family":"Fang","sequence":"additional","affiliation":[]},{"given":"Chonghao","family":"Yu","sequence":"additional","affiliation":[]},{"given":"Jian","family":"Li","sequence":"additional","affiliation":[]},{"given":"Minjie","family":"Feng","sequence":"additional","affiliation":[]}],"member":"1965","published-online":{"date-parts":[[2025,5,30]]},"reference":[{"year":"2025","author":"Agarwal","key":"ref1"},{"key":"ref2","doi-asserted-by":"publisher","first-page":"102631","DOI":"10.1016\/j.ecoinf.2024.102631","article-title":"Mula-Gan: multi-level attention Gan for enhanced underwater visibility","volume":"81","author":"Bakht","year":"2024","journal-title":"Ecol. 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