Explainable action prediction through self-supervision on scene graphs
This work explores scene graphs as a distilled representation of high-level information for autonomous driving, applied to future driver-action prediction. Given the scarcity and strong imbalance of data samples, we propose a selfsupervision pipeline to infer representative and well-separated embedd...
| Main Authors: | , , , |
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| Format: | Conference item |
| Language: | English |
| Published: |
IEEE
2023
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| Summary: | This work explores scene graphs as a distilled representation of high-level information for autonomous driving,
applied to future driver-action prediction. Given the scarcity
and strong imbalance of data samples, we propose a selfsupervision pipeline to infer representative and well-separated
embeddings. Key aspects are interpretability and explainability;
as such, we embed in our architecture attention mechanisms
that can create spatial and temporal heatmaps on the scene
graphs. We evaluate our system on the ROAD dataset against
a fully-supervised approach, showing the superiority of our
training regime. |
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