Exploring the Potential of World Models for Anomaly Detection in
Autonomous Driving
- URL: http://arxiv.org/abs/2308.05701v2
- Date: Mon, 18 Sep 2023 11:32:53 GMT
- Title: Exploring the Potential of World Models for Anomaly Detection in
Autonomous Driving
- Authors: Daniel Bogdoll, Lukas Bosch, Tim Joseph, Helen Gremmelmaier, Yitian
Yang, J. Marius Z\"ollner
- Abstract summary: We show how world models can be leveraged to perform anomaly detection in the domain of autonomous driving.
We provide a characterization of world models and relate individual components to previous works in anomaly detection.
- Score: 11.091582432763738
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: In recent years there have been remarkable advancements in autonomous
driving. While autonomous vehicles demonstrate high performance in closed-set
conditions, they encounter difficulties when confronted with unexpected
situations. At the same time, world models emerged in the field of model-based
reinforcement learning as a way to enable agents to predict the future
depending on potential actions. This led to outstanding results in sparse
reward and complex control tasks. This work provides an overview of how world
models can be leveraged to perform anomaly detection in the domain of
autonomous driving. We provide a characterization of world models and relate
individual components to previous works in anomaly detection to facilitate
further research in the field.
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