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| Auteurs principaux: | , , , |
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| Format: | Preprint |
| Publié: |
2024
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| Sujets: | |
| Accès en ligne: | https://arxiv.org/abs/2403.06086 |
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| _version_ | 1866916155404320768 |
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| author | Lu, Juanwu Zhan, Wei Tomizuka, Masayoshi Hu, Yeping |
| author_facet | Lu, Juanwu Zhan, Wei Tomizuka, Masayoshi Hu, Yeping |
| contents | Estimating the potential behavior of the surrounding human-driven vehicles is crucial for the safety of autonomous vehicles in a mixed traffic flow. Recent state-of-the-art achieved accurate prediction using deep neural networks. However, these end-to-end models are usually black boxes with weak interpretability and generalizability. This paper proposes the Goal-based Neural Variational Agent (GNeVA), an interpretable generative model for motion prediction with robust generalizability to out-of-distribution cases. For interpretability, the model achieves target-driven motion prediction by estimating the spatial distribution of long-term destinations with a variational mixture of Gaussians. We identify a causal structure among maps and agents' histories and derive a variational posterior to enhance generalizability. Experiments on motion prediction datasets validate that the fitted model can be interpretable and generalizable and can achieve comparable performance to state-of-the-art results. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2403_06086 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Towards Generalizable and Interpretable Motion Prediction: A Deep Variational Bayes Approach Lu, Juanwu Zhan, Wei Tomizuka, Masayoshi Hu, Yeping Artificial Intelligence Robotics Estimating the potential behavior of the surrounding human-driven vehicles is crucial for the safety of autonomous vehicles in a mixed traffic flow. Recent state-of-the-art achieved accurate prediction using deep neural networks. However, these end-to-end models are usually black boxes with weak interpretability and generalizability. This paper proposes the Goal-based Neural Variational Agent (GNeVA), an interpretable generative model for motion prediction with robust generalizability to out-of-distribution cases. For interpretability, the model achieves target-driven motion prediction by estimating the spatial distribution of long-term destinations with a variational mixture of Gaussians. We identify a causal structure among maps and agents' histories and derive a variational posterior to enhance generalizability. Experiments on motion prediction datasets validate that the fitted model can be interpretable and generalizable and can achieve comparable performance to state-of-the-art results. |
| title | Towards Generalizable and Interpretable Motion Prediction: A Deep Variational Bayes Approach |
| topic | Artificial Intelligence Robotics |
| url | https://arxiv.org/abs/2403.06086 |