Abstract Traffic light recognition under adverse scenes is a fundamental yet challenging task for intelligent driving systems, where domain shift caused by variations in weather, illumination, and geographic conditions often leads to significant performance degradation. To address this issue, this paper proposes a unified domainadaptive traffic light recognition framework, termed DA-TLR, to improve cross-domain generalization and robustness. Unlike existing methods that treat feature alignment in a decoupled manner, DA-TLR formulates domain adaptation as a unified modeling problem across both image-level and instance-level discrepancies. Specifically, DA-TLR is built upon the YOLOv5-l architecture and integrates a teacher-student module, an image-level adaptation module, and a domain adaptation loss for joint optimization. The teacher-student module employs a knowledge distillation strategy to generate high-confidence pseudo labels, facilitating instance-level feature alignment. The image-level adaptation module combines bidirectional pseudo-image transformation and dynamic masking to reduce appearance discrepancies and enhance robustness against domain-specific variations. Extensive experiments on multiple domain adaptation scenarios, including foggy, rainy, nighttime, and crossregional conditions, demonstrate that DA-TLR consistently outperforms existing methods. In particular, the proposed method achieves mAP50 scores of 89.57% and 79.03% in cross-fog and cross-rain tasks, respectively, approaching the upper bound of target-domain supervised training. Further analysis indicates that jointly modeling multi-level domain discrepancies is more effective than treating them independently. Overall, DA-TLR provides an effective and practical solution for robust traffic light recognition under adverse scenes, and offers a promising direction for domain-adaptive perception in real-world intelligent driving systems. During the embargo period (the 12 month period from the publication of the Version of Record of this article), the Accepted Manuscript is fully protected by copyright and cannot be reused or reposted elsewhere. As the Version of Record of this article is going to be / has been published on a subscription basis, this Accepted Manuscript will be available for reuse