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Autonomous systems have the ability to replace human-performed tasks like personal assistants in residential or commercial settings. Self-driving cars, which have shown potential, are one area of significant interest in AI. This may include anticipating the activities and goals of people, such as pedestrians, as well as those of other vehicles. The creation of high-resolution images and sophisticated obstacle-clearing manoeuvres at high speeds are other developments. Increased highway safety and better use of travel time are only two of the many advantages of autonomous cars. In order for automated steering to retain the best trajectory despite changes in the road's conditions, lateral steering control is a critical component of autonomous cars. This study aims to investigate the use of a dynamic bicycle model and Stanley controller as a route tracking method, as well as the use of sensors to identify objects and lanes on the way. Discussion will also include the outcomes of MATLAB and SIMULINK tests performed to evaluate these approaches.

In Indonesia, there is still no real-time system for monitoring the health of cattle. Instead, a microchip RFID ear tag is used, which is implanted in the cow, but there is still no connection to the Internet of Things to help cattle producers with monitoring. The existing monitoring still relies on human labour, which is undoubtedly inefficient, particularly if you have to keep track on the health of many cows on a big farm. Cattle's movement, heart rate, and body temperature may all be read via a collar gadget. By gathering information from temperature sensors, heart rate sensors, and an inertial measurement unit installed on a dairy cow's neck, information is sent via the internet. To provide normal, less normal, and abnormal health categorization outputs, the gathered data is saved and categorised using machine learning. Also, depending on the axis sensor, generate a graphic output.