## 5 Conclusions  
The primary contributions of this work are twofold. First, we proposed a novel baseline TAS scheduling algorithm that integrates an innovative sub-flow division mechanism. This mechanism enhances schedulability by intelligently segmenting high-bandwidth data streams that would otherwise cause scheduling conflicts, a common issue in mixed-criticality automotive environments. The algorithm employs an iterative, SMT-solver-based approach to determine tight constraint boundaries, aiming to generate GCLs optimized for low latency and jitter. Second, to address the computational demands of large-scale scenarios, we introduced a distinct scheduling computation acceleration algorithm. This algorithm utilizes a linear-time heuristic strategy, including a two-level sorting method and local search, to drastically reduce GCL computation time without significant performance degradation. A comprehensive experimental evaluation, conducted through both simulations and on a real-world hardware testbed, validated the effectiveness of the proposed framework. The key findings demonstrate that:  
* For realistic in-vehicle traffic, the proposed scheduling algorithm achieved a reduction in end-to-end latency of up to 59% compared to standard Strict Priority scheduling, with jitter controlled at the nanosecond level. Queuing delay was virtually eliminated by the precise GCL scheduling. * The algorithm successfully scales to larger networks, maintaining low latency (less than 700 ns above transmission delay) and low jitter (less than 400 ns) for scenarios with 100 or more data streams. * The scheduling computation acceleration algorithm proved highly efficient, capable of computing a GCL for 200 data streams in approximately 1.06 s, a significant improvement over traditional methods like ILP, which become computationally intractable for large stream sets. * Real-world experiments on a ZIGGO/FPGA testbed confirmed the practical applicability of our approach, demonstrating deterministic packet reception with low, stable latency (a mean of approx. 26.24 us) and extremely low jitter (within a 100 ns range) on physical hardware. In summary, this paper presents a practical and scalable solution for TAS-based scheduling in autonomous in-vehicle networks. By addressing both schedulability for complex traffic mixes and the computational efficiency for large-scale systems, this work contributes to the advancement of robust and reliable communication backbones essential for the future of autonomous driving. Future work will focus on extending these contributions. Key directions include (1) validating the algorithm in more complex, real-world in-vehicle network environments with an even greater diversity of nodes and traffic patterns; (2) optimizing the algorithms to handle dynamic data stream requirements, where traffic characteristics may change during operation; (3) exploring the integration and interplay of the proposed TAS scheduling algorithm with other TSN mechanisms, such as Cyclic Queuing and Forwarding (CQF); and (4) addressing the practical challenges of interoperability between TSN devices from different manufacturers. Conceptualization, C.Z. and Y.W. ; methodology, C.Z. ; software, C.Z. ; validation, C.Z. ; formal analysis, C.Z. ; investigation, C.Z. ; resources, Y.W. ; data curation, C.Z. ; writing--original draft preparation, C.Z. ; writing--review and editing, C.Z. and Y.W. ; visualization, C.Z. ; supervision, Y.W. ; project Administration, Y.W. ; funding Acquisition, Y.W. All authors have read and agreed to the published version of the manuscript. This research received no external funding. Data Availability Statement: All data generated or analyzed during this study are included in this published article. **Acknowledgments:** The authors would like to thank anonymous reviewers for their constructive feedback. The authors also gratefully acknowledge the automotive manufacturer that provided the valuable in-vehicle traffic data specifications used in the simulation studies.