Vision-Based Autonomous Driving: A Model Learning Approach

Understand

We present an integrated approach for perception and control for an autonomous vehicle and demonstrate this approach in a high-fidelity urban driving simulator.

• Our approach first builds a model for the environment, then trains a policy exploiting the learned model to identify the action to take at each time-step.
• To build a model for the environment, we leverage several deep learning algorithms.
• To that end, first we train a variational autoencoder to encode the input image into an abstract latent representation.