Document Type
Conference Proceeding
Abstract
The current approaches to autonomous driving focus on learning from observation or simulated data. These approaches are based on correlations rather than causation. For safety-critical applications, like autonomous driving, it’s important to represent causal dependencies among variables in addition to the domain knowledge expressed in a knowledge graph. This will allow for a better understanding of causation during scenarios that have not been observed, such as malfunctions or accidents. The causal knowledge graph, coupled with domain knowledge, demonstrates how autonomous driving scenes can be represented, learned, and explained using counterfactual and intervention reasoning to infer and understand the behavior of entities in the scene.
Publication Info
Preprint version International Semantic Web Conference, Fall 2024.
APA Citation
Jaimini, U., Henson, C., & Sheth, A. (2024). Causal knowledge graph for scene understanding in autonomous driving. International Semantic Web Conference. [Preprint]
Rights
© 2021 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0)