Graph-Based Auto-Driving Reasoning Task Scheduling

With the increase of vehicle mounted sensors, the rapid change of urban landmarks and traffic facilities as well as the complex traffic conditions of vehicles and pedestrians, the demand for real-time auto-driving response capability is continuously becoming urgent. How to provide safety guarantee for auto-driving systems by handling the continuing events from sensors and accomplishing the reasoning process via scheduling strategies is worth studying. In this paper, a hard real-time scheduling method of reasoning tasks for automatic driving system is proposed, including a task model based on parallel directed acyclic graphs with hard deadlines, a scheduling algorithm and admission control algorithm to ensure the reasoning operations and reactions within their hard real-time constraints. The experimental results show that our proposed method can effectively increase the success ratio of auto-driving reasoning tasks by average 9.62% and 7.31% compared with the direct scheduling algorithm and model transformation scheduling algorithm; and has also higher admission control capability by average 7.15% compared with the algorithm proposed by Baruah, which is promising to be applied in the auto-driving system for the security concern.