{"ID":2827386,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2512.16178","arxiv_id":"2512.16178","title":"Towards Closing the Domain Gap with Event Cameras","abstract":"Although traditional cameras are the primary sensor for end-to-end driving, their performance suffers greatly when the conditions of the data they were trained on does not match the deployment environment, a problem known as the domain gap. In this work, we consider the day-night lighting difference domain gap. Instead of traditional cameras we propose event cameras as a potential alternative which can maintain performance across lighting condition domain gaps without requiring additional adjustments. Our results show that event cameras maintain more consistent performance across lighting conditions, exhibiting domain-shift penalties that are generally comparable to or smaller than grayscale frames and provide superior baseline performance in cross-domain scenarios.","short_abstract":"Although traditional cameras are the primary sensor for end-to-end driving, their performance suffers greatly when the conditions of the data they were trained on does not match the deployment environment, a problem known as the domain gap. In this work, we consider the day-night lighting difference domain gap. Instead...","url_abs":"https://arxiv.org/abs/2512.16178","url_pdf":"https://arxiv.org/pdf/2512.16178v1","authors":"[\"M. Oltan Sevinc\",\"Liao Wu\",\"Francisco Cruz\"]","published":"2025-12-18T04:57:32Z","proceeding":"cs.CV","tasks":"[\"cs.CV\",\"cs.AI\",\"cs.RO\"]","methods":"[]","has_code":false}