Interaction-Based Trajectory Prediction Over a Hybrid Traffic Graph Article Swipe
Sumit Kumar
,
Yiming Gu
,
Jerrick Hoang
,
Galen Clark Haynes
,
Micol Marchetti-Bowick
·
YOU?
·
· 2020
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2009.12916
YOU?
·
· 2020
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2009.12916
Behavior prediction of traffic actors is an essential component of any real-world self-driving system. Actors' long-term behaviors tend to be governed by their interactions with other actors or traffic elements (traffic lights, stop signs) in the scene. To capture this highly complex structure of interactions, we propose to use a hybrid graph whose nodes represent both the traffic actors as well as the static and dynamic traffic elements present in the scene. The different modes of temporal interaction (e.g., stopping and going) among actors and traffic elements are explicitly modeled by graph edges. This explicit reasoning about discrete interaction types not only helps in predicting future motion, but also enhances the interpretability of the model, which is important for safety-critical applications such as autonomous driving. We predict actors' trajectories and interaction types using a graph neural network, which is trained in a semi-supervised manner. We show that our proposed model, TrafficGraphNet, achieves state-of-the-art trajectory prediction accuracy while maintaining a high level of interpretability.
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Metadata
- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/2009.12916
- https://arxiv.org/pdf/2009.12916
- OA Status
- green
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4287660688
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Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4287660688Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.48550/arxiv.2009.12916Digital Object Identifier
- Title
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Interaction-Based Trajectory Prediction Over a Hybrid Traffic GraphWork title
- Type
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preprintOpenAlex work type
- Language
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enPrimary language
- Publication year
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2020Year of publication
- Publication date
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2020-09-27Full publication date if available
- Authors
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Sumit Kumar, Yiming Gu, Jerrick Hoang, Galen Clark Haynes, Micol Marchetti-BowickList of authors in order
- Landing page
-
https://arxiv.org/abs/2009.12916Publisher landing page
- PDF URL
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https://arxiv.org/pdf/2009.12916Direct link to full text PDF
- Open access
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YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
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https://arxiv.org/pdf/2009.12916Direct OA link when available
- Concepts
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Interpretability, Computer science, Trajectory, Graph, Component (thermodynamics), Artificial intelligence, Theoretical computer science, Machine learning, Thermodynamics, Physics, AstronomyTop concepts (fields/topics) attached by OpenAlex
- Cited by
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0Total citation count in OpenAlex
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10Other works algorithmically related by OpenAlex
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