Machine Learning for Real-Time Autonomous Vehicle System Diagnostics

Abstract

Following the growth of development in automated and autonomous transportation, human reliance on vehicle systems, actuators, and sensors to ensure durability, versatility, and effective operation of mechatronics is increasing. With the large number of parts and the increasing level of complexity of the internal mechanisms in vehicles, vehicle diagnosis systems for malfunctions need to be developed and overhauled rapidly and effectively using state-of-the-art technologies. This is especially important for this type of complex car, as it can decrease the severity of any internal errors to the lowest level or prevent them altogether. Through timely real-time responses to device state events, this can even guarantee the reliability of the process and safety of operation and provide more reliable and robust driving of the car.

Automotive technology nowadays is working in lockstep with other technologies; the car itself is one big robot, functioning with the help of different systems and sensors that allow the driver to control it. The intelligence of car sensors is very important to know the current car conditions and to make possible engine management strategies. Machine learning will help to create more suitable and robust systems of car sensor intelligence to improve categories needed for the vehicle's main control systems. Among them, the diagnosis of the vehicle is crucial to achieve the system requirements; all car mechanisms need to run optimally and efficiently through safe and sustainable operation.