An Intelligent Task Scheduling Mechanism for Autonomous Vehicles via Deep Learning

Gomatheeshwari Balasekaran, Selvakumar Jayakumar and Rocío Pérez de Prado
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Gomatheeshwari Balasekaran: Department of Electronics and Communication Engineering, SRM Institute of Science and Technology, Tamil Nadu 600026, India
Selvakumar Jayakumar: Department of Electronics and Communication Engineering, SRM Institute of Science and Technology, Tamil Nadu 600026, India
Rocío Pérez de Prado: Telecommunication Engineering Department, University of Jaén, 23700 Jaén, Spain

Energies, 2021, vol. 14, issue 6, 1-22

Abstract: With the rapid development of the Internet of Things (IoT) and artificial intelligence, autonomous vehicles have received much attention in recent years. Safe driving is one of the essential concerns of self-driving cars. The main problem in providing better safe driving requires an efficient inference system for real-time task management and autonomous control. Due to limited battery life and computing power, reducing execution time and resource consumption can be a daunting process. This paper addressed these challenges and developed an intelligent task management system for IoT-based autonomous vehicles. For each task processing, a supervised resource predictor is invoked for optimal hardware cluster selection. Tasks are executed based on the earliest hyper period first (EHF) scheduler to achieve optimal task error rate and schedule length performance. The single-layer feedforward neural network (SLFN) and lightweight learning approaches are designed to distribute each task to the appropriate processor based on their emergency and CPU utilization. We developed this intelligent task management module in python and experimentally tested it on multicore SoCs (Odroid Xu4 and NVIDIA Jetson embedded platforms). Connected Autonomous Vehicles (CAV) and Internet of Medical Things (IoMT) benchmarks are used for training and testing purposes. The proposed modules are validated by observing the task miss rate, resource utilization, and energy consumption metrics compared with state-of-art heuristics. SLFN-EHF task scheduler achieved better results in an average of 98% accuracy, and in an average of 20–27% reduced in execution time and 32–45% in task miss rate metric than conventional methods.

Keywords: autonomous vehicles; deep learning; heterogeneous multicore; IoT; task mapping; scheduling; energy consumption (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2021
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