In safety-critical autonomous systems, data freshness presents a fundamental design challenge. While the Logical Execution Time (LET) paradigm ensures compositional determinism, it often does so at the cost of injected latency, degrading the phase margin of high-frequency control loops. Furthermore, mapping heterogeneous, multi-rate sensor fusion requirements onto rigid task-centric schedules typically implies in resource-inefficient oversampling. This paper proposes a Task-based scheduling framework extended with data freshness constraints. Unlike traditional models, scheduling decisions are driven by the lifespan of data. We introduce task offset based on the data freshness constraint to order data production in a Just-in-Time (JIT) fashion: the completion of the production of data with strictest data freshness constraint is delayed to the instant its consumers will be ready to use it. This allows for flexible task release offsets. We introduce a formal methodology to decompose Data Dependency Graphs into Dominant Paths by tracing the strictest data freshness constraints backward from the actuators. Based on this decomposition, we propose a Consensus Offset Search algorithm that synchronizes shared producers and private predecessors. This approach enforces end-to-end data freshness without the artificial latency of LET buffering. We formally prove that this offset-based alignment preserves the 100\% schedulability capacity of Global EDF, ensuring data freshness while eliminating the computational overhead of redundant sampling.

翻译：在安全关键自主系统中，数据新鲜性构成了一个基础性的设计挑战。虽然逻辑执行时间（LET）范式确保了组合确定性，但其代价往往是引入延迟，从而降低高频控制回路的相位裕度。此外，将异构、多速率传感器融合需求映射到刚性的以任务为中心的调度方案上，通常意味着资源效率低下的过采样。本文提出了一种扩展了数据新鲜性约束的基于任务的调度框架。与传统模型不同，调度决策由数据的生命周期驱动。我们引入了基于数据新鲜性约束的任务偏移，以准时制（JIT）方式安排数据生产：具有最严格数据新鲜性约束的数据生产完成时刻被延迟到其消费者准备好使用该数据的时刻。这允许灵活的任务释放偏移。我们提出了一种形式化方法，通过从执行器向后追溯最严格的数据新鲜性约束，将数据依赖图分解为主导路径。基于此分解，我们提出了一种共识偏移搜索算法，用于同步共享生产者和私有前驱任务。该方法在无需LET缓冲引入人为延迟的情况下，强制执行端到端数据新鲜性。我们形式化证明了这种基于偏移的对齐方式保留了全局最早截止时间优先（Global EDF）调度策略100%的可调度能力，在确保数据新鲜性的同时消除了冗余采样的计算开销。