Reinforcement learning in autonomous vehicles with limited input

Abstract

The automotive industry is currently undergoing two simultaneous revolutions: electrification and autonomy. In recent years, various regulations that apply to all manufacturers have mandated the incorporation of advanced driver assistance systems (ADAS) into their vehicles. Manufacturers are already delving into the development of Level L3 autonomous functions that can be homologated. Additionally, numerous institutions and companies are pushing the boundaries, aiming to create prototypes that achieve Level L5 autonomy, enabling fully autonomous driving. Within the scope of this project, we will delve into the cutting-edge developments in this field. Our objective is to implement and train a model utilizing reinforcement learning, and subsequently, compare its performance with other models. To ensure safety throughout the process, the training will be carried out within simulated environments. However, our ultimate goal is to seamlessly integrate the trained model into a real-world vehicle.