Ji Hye Ok  Wookyoung Kim  Honguk Woo

10.5626/JOK.2024.51.6.558

personalized autonomous driving multi-objective reinforcement learning autonomous vehicles Imitation Learning inverse reinforcement learning

The rise of Autonomous Vehicles (AVs) has brought humans and robots together on the same roads. As AVs integrate into the existing road system, it is crucial for them to establish a connection with human drivers and operate in a way that is convenient to humans. Moreover, as the desire for personalized autonomous driving experiences frows, there is a need to meet the demand for ‘personalized’ AVs. This paper examines imitation learning methods that imitate the driving behaviors of rule-based agents. It also proposes a controlled multi-objective imitation learning approach to generate diverse driving policies based on given data. Additionally, the study assesses the derived policies in various scenarios using the Carla simulator.