Multi-task perception in traffic scenes requires unified representations for object recognition, region understanding, and structural reasoning. However, heterogeneous tasks such as detection and segmentation impose conflicting optimization objectives on shared features, leading to representation imbalance and reduced robustness. We propose DDL-Net, a task-balanced panoptic perception network that addresses this issue through coordinated representation learning and task-aware decoding, introducing a Channel-Rearranging Attention Interaction (CRAI) module built upon Channel Rearranging Boosting Attention (CRBA) units to reduce attention redundancy and enhance informative channel responses, improving representation of small and degraded targets. In addition, a task-specific decoding mechanism combining the Task-Guided Feature Balancing Module (TGBM) and Frequency-Aware Task-Interaction Guided Decoder (FTIG-Decoder) aligns shared features with task semantics, enabling balanced performance across detection and segmentation tasks. Experiments on BDD100K validate the effectiveness and robustness of the proposed design.

Segment Routing (SR) is of great significance in the evolving landscape of Space-Air-Ground Integrated Networks (SAGIN). However, owing to hardware limitations and bandwidth constraints, directly applying existing SR-related solutions of terrestrial networks to satellite networks faces resource and performance challenges. Thus, in this paper, we introduce a novel framework called Satellite-Tailored Segment Routing (STSR) for satellite networks in SAGIN. We first propose the STSR data-plane protocol to provide lightweight source routing. Then, a routing policy integration scheme is proposed to support routing consistency in SAGIN. Simulation results reveal that compared to SRv6, in the case of 2000 satellites, STSR reduces routing path information in the header by 43.51 % and increases load efficiency by 43%-92%.