  This paper proposes a imitation learning model for autonomous driving on
highway traffic by mimicking human drivers' driving behaviours. The study
utilizes the HighD traffic dataset, which is complex, high-dimensional, and
diverse in vehicle variations. Imitation learning is an alternative solution to
autonomous highway driving that reduces the sample complexity of learning a
challenging task compared to reinforcement learning. However, imitation
learning has limitations such as vulnerability to compounding errors in unseen
states, poor generalization, and inability to predict outlier driver profiles.
To address these issues, the paper proposes mixture density network behaviour
cloning model to manage complex and non-linear relationships between inputs and
outputs and make more informed decisions about the vehicle's actions.
Additional improvement is using collision penalty based on the GAIL model. The
paper includes a simulated driving test to demonstrate the effectiveness of the
proposed method based on real traffic scenarios and provides conclusions on its
potential impact on autonomous driving.
