Generation of Driving Scenario Trajectories with Generative Adversarial Networks
Ανδρέας Δημητρίου, Henrik Allsvag, Sadegh Rahrovani, Morteza Haghir Chehreghani
Abstract
The future of transportation is tightly connected to Autonomous Driving (AD). While a lot of progress has been made in recent years, there are still obstacles to overcome. One of the most critical issues is the safety verification of AD. A scenario-based verification approach that shifts tests from the fields to a virtual environment seems like a sophisticated approach to tackle the safety verification as tests need to be revised whenever changes are made to the AD. However, collecting and labelling data that can be used to construct scenarios is expensive and time-consuming to compute. In this work, we propose a unified framework for trajectory generation and validation in a consistent and principled way. We first explore methods to generate artificial trajectories that resemble the previously captured ones. More specifically, we consider two architectures based on Generative Adversarial Networks (GANs): recurrent GANs and a recurrent Autoencoder in combination with GANs. Moreover, we investigate the use of different metrics to evaluate the quality of generated trajectories which is a nontrivial task.