Hybrid Inference Based Scheduling Mechanism for Efficient Real Time Task and Resource Management in Smart Cars for Safe Driving

In recent years, the focus of the smart transportation industry has been shifting towards the research and development of smart cars with autonomous control. Smart cars are considered to be a smart investment, as they promote safe driving while focusing on an alternate transportation fuel resource, making them eco-friendly too. Safe driving is one of the crucial concerns in autonomous smart cars. The major issue for the better provision of safe driving is real time tasks management and an efficient inference system for autonomous control. Real time task management is of huge significance in smart cars control systems. An optimal control system consists of a knowledge base and a control unit; where the knowledge base contains the data and thresholds for rules and the control unit contains the functionality for smart vehicle autonomous control. In this work, we propose a hybrid of an inference engine and a real time task scheduler for an efficient task management and resource consumption. Our proposed hybrid inference engine and task scheduler mechanism provides an efficient way of controlling smart cars in different scenarios such as heavy rainfall, obstacle detection, driver’s focus diversion etc., while ensuring the practices of safe driving. For the performance analysis of our proposed hybrid inference based scheduling mechanism, we have simulated a non-hybrid version with the same system constraints and a basic implementation of inference engine. For performance evaluation, CPU time utilization, tasks’ missing rate, average response time are used as performance metrics.