Harits Ar Rosyid, Oemar Syarif Burhan, Ilham Ari Elbaith Zaeni, Ahmad Naim Che Pee
Self-driving car is one of the automotive innovation technologies that uses a computerized system to control a car without human assistance. Big manufactures have been developing this innovation into the fifth level autonomous technology. This study contributes to create a new system as innovation for this self-driving car to avoid road hazards and potholes. This paper reports the result of the conducted experiment on how the self-driving model is able to avoid potholes using end-to-end approach with Convolutional Neural Network (CNN) as a driving simulator called AirSim. Three different CNN models were tested to compare their performance. The result indicates that all the models were able to evade the road hazards. © 2021 IEEE.
State University Of Malang, Electrical Engineering Department, Malang, Indonesia; Universiti Teknikal Malaysia Melaka, Human Centered Computing And Information System Lab, (UTeM)