Precise Time Synchronization in Packet Networks Using Deep Learning for Future Intelligent Transportation

Precise time synchronization is foundational for future intelligent transportation systems (ITS), where safety-critical functions like cooperative Vehicle-to-Everything (V2X) communication and multi-sensor fusion demand a leap from sub-microsecond- to nanosecond-level precision. Standard protocols like the Precision Time Protocol (PTP) are limited by inherent errors (e.g., timestamping inaccuracies and clock drift) that are typically only solvable with expensive hardware upgrades. This paper proposes a cost-effective, software-based solution. We introduce a novel method that leverages deep reinforcement learning (DRL) to actively predict and compensate for these synchronization errors in real time. An experimental environment is constructed to rigorously evaluate the performance of the proposed method. The results demonstrate that our approach achieves a significant leap in synchronization accuracy, showcasing its potential to meet the stringent timing demands of future intelligent transportation.