The Design and Implementation of a Waypoint-Based Autonomous Control System on a Low-Level Controller for Self-Driving Vehicles

Autonomous land transportation vehicles and Electric Vehicles (EV) have become the main choice of the majority of society due to their environmentally friendly nature. Furthermore, the acceleration of autonomous vehicle research can also reduce traffic accidents caused by human error, which have been significantly high in the last 10 years. However, the cost required to obtain assistance from autonomous systems still tends to be expensive. This paper presents the development of a device utilizing an Advanced RISC Machine (ARM) Cortex-an Advanced RISC Machine (ARM) Cortex-M4 STM32F411 microcontroller system with 32-bit architecture. This system offers optimal performance at a cost-effective price point. The proposed autonomous system is designed based on the waypoint method as a reference to the vehicle’s position. The Global Navigation Satellite System (GNSS) accessed through the U-Blox NEO-M8P module provides position data, whereas the HMC5883L magnetometer provides orientation data. The position data is further processed by utilizing a Kalman filter to enhance accuracy and reduce computational burden. The error detection angle read by the E-Compass is only 0.31% with the ground speed testing in comparison with true speed from car odometry with an average error of 1%, thus supporting good data accuracy, achieved with a track error of only 0.06 meters in real-scale environmental testing. This demonstrates the successful implementation of autonomous control algorithms on the low-level controller.