Reliable Real-Time Message Scheduling in Automotive Cyber-Physical Systems
Published in Springer Nature, 2023
Recommended citation: V. K. Kukkala, T. Bradley, and S. Pasricha, "Reliable Real-Time Message Scheduling in Automotive Cyber-Physical Systems," in Machine Learning and Optimization Techniques for Automotive Cyber-Physical Systems, Springer Nature, 2023.
Abstract
Today’s vehicles employ a variety of time-triggered protocols, such as FlexRay and TTEthernet, to transmit periodic messages originating from safety-critical applications. One of the major challenges with time-triggered transmissions is jitter, which is the unpredictable delay-induced deviation from the actual periodicity of a message. Failure to account for jitter can have catastrophic consequences in time-critical automotive cyber-physical systems. In this chapter, we propose a novel message scheduling framework, called JAMS-SG, that synthesizes message schedules at design time using our proposed hybrid heuristic approach. At runtime, JAMS-SG employs a Multi-Level Feedback Queue (MLFQ) to handle jitter-affected time-triggered messages and high-priority event-triggered messages. Moreover, JAMS-SG uses a runtime scheduler to transmit messages and ensure that all deadline constraints are satisfied. Our simulation results, based on messages and network traffic data from a real-world vehicle, indicate that JAMS-SG is highly scalable and outperforms the best-known prior work in the area in the presence of jitter.