Autonomous real-time systems (ARTS), such as self-driving vehicles and robotic assembly lines, are increasingly deployed to improve efficiency, accuracy, and responsiveness with reduced human intervention. In ARTS networks, self-triggered (ST) traffic-initiated by internal decision-making rather than fixed schedules or external events-is becoming prevalent and plays a critical role in enabling timely autonomous actions. However, existing network schedulers do not adequately support ST traffic due to two inherent challenges: volatility, where bounded processing jitter leads to uncertain arrival times, and absence, where reserved network resources remain underutilized when ST traffic does not materialize. To address these challenges, we propose ARTSN, an ST-tailored scheduling paradigm built upon time-sensitive networking (TSN). ARTSN introduces two key techniques: (1) an exact offline scheduling method that leverages the inferable arrival information of ST traffic for precise time-slot reservation, and (2) an adaptive online slot-release mechanism that dynamically reclaims unused reservations when ST traffic is absent. Extensive experiments on both a TSN simulator and a real-world testbed show that ARTSN significantly improves schedulability, scalability, and efficiency over state-of-the-art methods while maintaining reliable transmission guarantees.

翻译：[translated abstract in Chinese] 自主实时系统（ARTS）——例如自动驾驶车辆和机器人装配线——正日益被部署以提升效率、精度和响应能力，同时减少人工干预。在ARTS网络中，由内部决策触发（而非固定调度或外部事件）的自触发流量（ST流量）正在成为主流，并在实现及时自主决策中发挥关键作用。然而，现有网络调度器由于面临两个固有挑战而无法充分支持ST流量：一是波动性，即有限的处理抖动导致不确定的到达时间；二是缺失性，即当ST流量未产生时，预留的网络资源处于低利用率状态。为解决这些挑战，我们提出ARTSN——一种基于时间敏感网络（TSN）的、专为ST流量设计的调度范式。ARTSN引入两项关键技术：（1）一种精确的离线调度方法，利用ST流量可推断的到达信息实现精确的时隙预留；（2）一种自适应在线时隙释放机制，在ST流量未产生时动态回收未使用的预留资源。在TSN模拟器和真实测试平台上的大量实验表明，与最先进方法相比，ARTSN在显著提升可调度性、可扩展性和效率的同时，依然保持了可靠的数据传输保障。