Autonomous machines often encounter complex timing constraints, such as those concerning end-to-end timing guarantees and real-time data fusion, etc. Tasks are often event-triggered or time-triggered at varying rates and exhibit data dependencies in between. Maintaining the real-time performance of autonomous machines becomes a highly challenging endeavor. In this paper, we formulate the workload of an autonomous machine as a hybrid Directed Acyclic Graph (DAG), which contains both time-trigger tasks and event-trigger tasks, with a distinct focus on the task of ensuring timing consistency in data fusion and adherence to end-to-end constraints within the DAG model. We design a concise mechanism to select suitable data received by a node and transmit them to successor nodes. This ensures both the timing disparity—as reflected by the differences in timestamps of the data used for fusion—and the end-to-end latency from the sensor to the controller is confined within a certain boundary. The proposed method is proven to be optimal as it always selects suitable data to guarantee the timing correctness of an autonomous machine as far as it (inherently) has the capacity. Experimental results show that our method can significantly improve the success rate of guaranteeing both timing consistency and end-to-end constraints of the autonomous machine.

In autonomous driving (AD) systems, complex data dependencies exist between tasks with different activation rates, making it very hard to analyze the system’s timing behaviors. This paper formulates an AD system as a multi-rate directed acyclic graph (DAG) and introduces a novel reaction time bound for critical chains within this multi-rate DAG. Furthermore, we introduce a priority assignment strategy tailored to optimize priority allocation, effectively minimizing the reaction time of critical task chains. This strategy comes with theoretical guarantees, ensuring that the achieved latency bound is only slightly higher than the ideal one. Our empirical work demonstrates that the newly proposed reaction time bound outperforms current standards, achieving an average improvement of $5.46 \%$. Furthermore, our strategy for priority assignment significantly enhances the success rate of achieving timing correctness in the AD system, exceeding the baseline method by a notable $19.24 \%$.

In the autonomous driving (AD) system, complex data dependencies exist between tasks with different activation rates, making it very hard to analyze systems’ real-time behaviors. This paper formulates the AD system as a multi-rate DAG and proposes an integrated framework to co-analyze the schedulability of individual tasks and the end-to-end latency of task chains in the multi-rate DAG. Integer linear programming (ILP) techniques are developed to guide how to drop redundant workload to increase the chance that timing requirements can be met. This paper proposed one analysis framework which enables an automated process in which designs of the AD system are created, analyzed and refined in an iterative way, i.e., the analysis result in the last iteration provides valuable guidance to redesign the AD system in the next iteration. Experiments are conducted to evaluate the performance of our analysis method.

BACKGROUND: The excessive production of lactic acid by L. bulgaricus during yogurt storage is a phenomenon we are always tried to prevent. The methods used in industry either control the post-acidification inefficiently or kill the probiotics in yogurt. Genetic methods of changing the activity of one enzyme related to lactic acid metabolism make the bacteria short of energy to growth, although they are efficient ways in controlling lactic acid production. RESULTS: A model of pH-induced promoter regulation on the production of lactic acid by L. bulgaricus was built. The modelled lactic acid metabolism without pH-induced promoter regulation fitted well with wild type L. bulgaricus (R2LAC = 0.943, R2LA = 0.942). Both the local sensitivity analysis and Sobol sensitivity analysis indicated parameters Tmax, GR, KLR, S, V0, V1 and dLR were sensitive. In order to guide the future biology experiments, three adjustable parameters, KLR, V0 and V1, were chosen for further simulations. V0 had little effect on lactic acid production if the pH-induced promoter could be well induced when pH decreased to its threshold. KLR and V1 both exhibited great influence on the producing of lactic acid. CONCLUSIONS: The proposed method of introducing a pH-induced promoter to regulate a repressor gene could restrain the synthesis of lactic acid if an appropriate strength of promoter and/or an appropriate strength of ribosome binding sequence (RBS) in lacR gene has been designed.