TSTNet: Temporal Semantic Transformer-Based Computing Power Network for Automatic Driving in the Internet of Vehicles

Automatic driving systems face critical challenges, including limited computational resources, complex data processing demands, and disruptions caused by high vehicular mobility, all of which hinder real-time decision-making and system accuracy. Existing solutions, such as edge computing and distributed architectures, partially address these issues but often fail to integrate semantic communication and mobility-aware optimizations. To tackle these challenges, we propose a temporal semantic transformer (TSTNet)-based edge computing network architecture (TSTNet) that enhances decision-making accuracy and reduces latency in automatic driving systems. TSTNet overcomes three key challenges in automatic driving. First, it efficiently utilizes limited computational resources by optimizing the processing of large-scale multimodal data through lightweight semantic extractors and attention-based feature integration, significantly reducing computational overhead. Second, it preserves semantic and behavioral continuity by ensuring seamless transitions of vehicle behavior and surrounding scene semantics during mobility and service handovers. This ensures consistent situational awareness even in highly dynamic vehicular environments. Third, TSTNet reduces decision-making latency by leveraging semantic inheritance to minimize redundant computations, enabling real-time performance and improving the reliability of driver-assist systems. Experimental results demonstrate that TSTNet improves task accuracy by over 90% while reducing decision-making latency by over 50% compared to conventional methods. This architecture offers a scalable, efficient, and robust solution to the computational and mobility challenges in automatic driving, enabling enhanced real-time adaptability in complex traffic scenarios.