  In future 6G Mobile Edge Computing (MEC), autopilot systems require the
capability of processing multimodal data with strong interdependencies.
However, traditional heuristic algorithms are inadequate for real-time
scheduling due to their requirement for multiple iterations to derive the
optimal scheme. We propose a novel TSNet-SAC based on Transformer, that
utilizes heuristic algorithms solely to guide the training of TSNet.
Additionally, a Sliding Augment Component (SAC) is introduced to enhance the
robustness and resolve algorithm defects. Furthermore, the Extender component
is designed to handle multi-scale training data and provide network
scalability, enabling TSNet to adapt to different access scenarios. Simulation
demonstrates that TSNet-SAC outperforms existing networks in accuracy and
robustness, achieving superior scheduling-making latency compared to heuristic
algorithms.
