Age of Information Centric Task Scheduling in Autonomous Driving Systems

Project: Research

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An autonomous driving system consists of many inter-dependent software components to perform various tasks such as perception, motion planning, and control. In recent years, various models and algorithms have been developed for individual tasks to achieve shorter execution time and higher accuracy. When such tasks are put into a pipeline, driving safety depends on not only the performance of individual tasks, but also how they are scheduled on multi-core processors.   Driving safety requires that 1) driving decisions are made according to the latest surroundings, and 2) sensing data stays synchronized in the pipeline to avoid making driving decisions based on distorted surroundings. In this project, we introduce two new performance metrics, namely, age of information (AoI) and time disparity, to capture the requirements of autonomous driving. AoI is defined as the time difference between the time when the control task generates control commands and the minimum raw data timestamp of the control task’s inputs. Our preliminary results have shown that minimizing the maximum AoI is equivalent to jointly maximizing the throughput and minimizing the responsive time, where throughput and responsive time are often separately considered in conventional task scheduling. For a task with multiple inputs, time disparity is defined as the maximum time difference of the raw data timestamps among its inputs. In this project, we aim to develop an AoI-centric task scheduling framework for autonomous driving, which takes the impact of time disparity on driving safety and tasks' execution time dynamics into consideration. In particular, we will use Apollo CyberRT as our simulator. We will develop learning models to dynamically predict tasks’ execution time according to the surroundings. We will also develop learning models to study the impact of time disparity on driving safety, i.e., to empirically derive the threshold where time disparity above that will cause serious driving safety issues. Given the predicted execution time of tasks, a rolling horizon based task scheduling will be developed to minimize the maximum AoI while ensuring that time disparity is less than the derived threshold. We will then use the solutions obtained to train a deep reinforcement learning model offline so that real-time task scheduling decisions can be made through fast and accurate online inference.  In summary, this project will open a new landscape for task scheduling. We expect the proposed AoI-centric task scheduling framework will make contributions not only to autonomous driving, but also other applications demanding data freshness and synchronization. 


Project number9043175
Grant typeGRF
StatusNot started
Effective start/end date1/01/22 → …