Towards Generalizable and Interpretable Motion Prediction: A Deep Variational Bayes Approach

• URL: http://arxiv.org/abs/2403.06086v1
• Date: Sun, 10 Mar 2024 04:16:04 GMT
• Title: Towards Generalizable and Interpretable Motion Prediction: A Deep Variational Bayes Approach
• Authors: Juanwu Lu, Wei Zhan, Masayoshi Tomizuka, Yeping Hu
• Abstract summary: This paper proposes an interpretable generative model for motion prediction with robust generalizability to out-of-distribution cases. For interpretability, the model achieves the target-driven motion prediction by estimating the spatial distribution of long-term destinations. Experiments on motion prediction datasets validate that the fitted model can be interpretable and generalizable.
• Score: 54.429396802848224
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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