Implementation of ADAS for Electric Vehicle Safety for Pediatric Patients Based on Fuzzy Logic

The implementation of an Advanced Driver Assistance System (ADAS) for electric vehicles used in transporting pediatric patients in a hospital setting can significantly enhance safety and comfort. This study aims to develop and implement a Fuzzy Logic-based Advanced Driver Assistance System (ADAS) to enhance the safety of electric transportation for pediatric patients in hospitals. The system utilizes Light Detection and Ranging (LIDAR) sensors and rotary encoders to detect distance and speed, while employing fuzzy control to regulate braking and avoid collisions. Testing of the ADAS with fuzzy logic yielded promising results in improving the safety of electric vehicles for pediatric patients. This is evident from the system's ability to swiftly respond to dynamic obstacles and effectively manage braking across various LIDAR detection ranges. The fuzzy system facilitates more flexible handling of uncertainties in dynamic environments, which is particularly crucial in a hospital setting with considerable activity and movement. The test results indicate that the sensors and fuzzy algorithms used in the system had a low error rate, with the highest being 5.90%. The system performed well in various dynamic and static conditions, stopping within the targeted distance in all scenarios. However, under loaded conditions, the speed during moderate braking fluctuated slightly (16 cm/s to 14 cm/s) at moderate PWM. Usability testing suggests that the system is generally well-received by users, although there remains room for further improvement.