Generation and analysis of driving scenario trajectories for safety verification of Autonomous Drive
Typ
Examensarbete för masterexamen
Program
Publicerad
2020
Författare
Demetriou, Andreas
Alfsvåg, Henrik
Modellbyggare
Tidskriftstitel
ISSN
Volymtitel
Utgivare
Sammanfattning
Autonomous vehicles are often considered to be the future of transportation. However,
many believe fully-autonomous vehicles are still far away from being realized.
One of the main reasons is the lack of confidence from a safety level standpoint.
While car manufacturers such as Volvo Cars have made great efforts to prove that
developed algorithms are safe enough, it is just not feasible to test each new feature
in the fields. Luckily, virtual environments address this issue by reproducing traffic
situations - scenarios. But even with virtual environments, a lot of data still has to be
collected to cover the whole spectrum of possible scenarios. This can be addressed
with simulated scenarios. Another approach that is investigated in this thesis is
generating scenarios based on real ones. We considered two approaches: Recurrent
Conditional Generative Adversarial Nets and a Recurrent Autoencoder with Generative
Adversarial Nets. The latter also shows useful properties for scenario analysis.
The second problem that we try to tackle in this thesis is the evaluation of our
developed models.
Beskrivning
Ämne/nyckelord
Generative Adversarial Nets , Recurrent Networks , Autonomous vehicles , Deep Learning