Path Tracking Control Using Imitation Learning with Variational Auto-Encoder

This paper presents a path tracking algorithm for autonomous driving that learns an action command from high-dimensional input state vector, e.g., grid-maps. The learning framework is built upon a variational auto-encoder (VAE) and takes advantage of the efficient path tracking results that already exist or can be obatined from a human expert. The VAE is known to give smooth latent representations of the input data and we make one of the latent attribute follow the expert's command. We implement an autonomous driving system on the open source robot simulator (Webots) and collect the demonstration data for training the VAE. Numerical results show that the proposed tracking method can drive a vehicle robustly without explicitly detecting the road features.