Towards Generalizable and Interpretable Motion Prediction: A Deep Variational Bayes Approach
<|reference_start|>Towards Generalizable and Interpretable Motion Prediction: A Deep Variational Bayes Approach: 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.<|reference_end|>
@article{lu2024towards,
    title={Towards Generalizable and Interpretable Motion Prediction: A Deep
  Variational Bayes Approach},
    author={Juanwu Lu, Wei Zhan, Masayoshi Tomizuka, Yeping Hu},
    journal={arXiv preprint arXiv:2403.06086},
    year={2024},
    archivePrefix={arXiv},
    eprint={2403.06086},
    primaryClass={cs.AI cs.RO}
}