###### Abstract  
The evolution of the internet towards supporting time-critical applications, such as industrial cyber-physical systems (CPSs) and autonomous systems, has created an urgent demand for networks capable of providing deterministic, low-latency communication. Autonomous vehicles represent a particularly challenging use case within this domain, requiring both reliability and determinism for massive data streams--a requirement that traditional Ethernet technologies cannot satisfy. This paper addresses this critical gap by proposing a comprehensive scheduling framework based on Time-Aware Shaping (TAS) within the Time-Sensitive Networking (TSN) standard. The framework features two key contributions: (1) a novel baseline scheduling algorithm that incorporates a sub-flow division mechanism to enhance schedulability for high-bandwidth streams, computing Gate Control Lists (GCLs) via an iterative SMT-based method; (2) a separate heuristic-based computation acceleration algorithm to enable fast, scalable GCL generation for large-scale networks. Through extensive simulations, the proposed baseline algorithm demonstrates a reduction in end-to-end latency of up to 59% compared to standard methods, with jitter controlled at the nanosecond level. The acceleration algorithm is shown to compute schedules for 200 data streams in approximately one second. The framework's effectiveness is further validated on a real-world TSN hardware testbed, confirming its capability to achieve deterministic transmission with low latency and jitter in a physical environment. This work provides a practical and scalable solution for deploying deterministic communication in complex autonomous and cyber-physical systems.  
26 June 20251  
Among these emerging applications, autonomous vehicles present one of the most demanding use cases for deterministic networking [4]. The in-vehicle network (IVN) of a modern autonomous vehicle must reliably handle a massive influx of data from a diverse sensor suite (e.g., LiDAR, radar, cameras) and process critical control commands with stringent timing constraints [5; 6]. The challenge is further compounded by the need to support mixed-criticality traffic, where safety-critical Time-Triggered (TT) flows, which often require co-scheduling of computation and communication [7], must coexist with less critical best-effort (BE) traffic. Effectively scheduling such hybrid traffic is a key challenge in automotive network design [8; 9]. Consequently, the performance requirements for IVNs far exceed the capabilities of legacy bus-based architectures like CAN or even early automotive Ethernet concepts [10].  
The primary mechanism within TSN for achieving such deterministic guarantees is the Time-Aware Shaper (TAS), defined in IEEE 802.1 Qbv [11]. TAS operates by orchestrating packet transmissions according to a pre-calculated Gate Control List (GCL). While highly effective, the generation of an optimal GCL is an NP-complete problem [12], presenting a major scalability and efficiency challenge, particularly in complex systems like autonomous vehicles.  
This paper addresses this gap by proposing a comprehensive and scalable GCL scheduling framework. Our contributions are twofold: first, a novel baseline scheduling algorithm featuring a sub-flow division mechanism to enhance schedulability, which computes GCLs via an iterative SMT-based approach, and second, a heuristic-based acceleration algorithm designed for rapid GCL generation in large-scale scenarios. Using the demanding context of in-vehicle networks as a primary validation use case, we demonstrate through extensive simulations and physical testbed experiments that our framework provides a practical and efficient solution for deploying deterministic communication in real-time systems.