A Multi-Protocol Integrated Task Offloading Method for Heterogeneous In-Vehicle Networks

With the continuous advancement of intelligent connected vehicle (ICV) technologies, the demand for computing resources by in-vehicle applications has significantly increased. In future zone-oriented centralized electronic/electrical architectures (E/EA), the vehicular computing platform (VCP) equipped with high-performance CPUs/GPUs provides the majority of computing resources. Under this centralized allocation paradigm, the issues arising from the imbalanced distribution of computing resources cannot be overlooked. To efficiently utilize surplus computing resources in VCPs, this paper proposes a multi-protocol integrated task offloading method for heterogeneous in-vehicle networks (IVNs). The approach leverages topic-based communication in Data Distribution Service (DDS) to offload complex tasks from resource-limited embedded devices to the VCP, thereby reducing task execution time. Considering the stringent real-time requirements of safety-critical applications, the Audio Video Transport Protocol (AVTP) is employed instead of DDS for data transmission during offloading when large volumes of pending data need to be transferred, effectively reducing communication latency. A task offloading agent is designed to bridge protocol heterogeneity in IVNs, extending offloading functionality to resource-limited devices. Furthermore, a greedy latency-aware offloading strategy is developed to obtain optimal offloading decisions within nanosecond-level latency. Finally, simulated IVNs based on automotive-grade chips were established, with physical experiment results validating the effectiveness of the proposed methods.