Stochastic Analysis of CAN-Based Real-Time Automotive Systems

Many automotive applications, including most of those developed for active safety and chassis systems, must comply with hard real-time deadlines, and are also sensitive to the average latency of the end-to-end computations from sensors to actuators. A characterization of the timing behavior of functions is used to estimate the quality of an architecture configuration in the early stages of architecture selection. In this paper, we extend previous work on stochastic analysis of response times for software tasks to controller area network messages, then compose them with sampling delays to compute probability distributions of end-to-end latencies. We present the results of the analysis on a realistic complex distributed automotive system. The distributions predicted by our method are very close to the probability of latency values measured on a simulated system. However, the faster computation time of the stochastic analysis is much better suited to the architecture exploration process, allowing a much larger number of configurations to be analyzed and evaluated.