Towards Generalizable and Interpretable Motion Prediction: A Deep Variational Bayes Approach
This addresses the safety issue for autonomous vehicles in mixed traffic by improving interpretability and generalizability, though it is incremental as it builds on existing deep learning approaches.
The paper tackles the problem of weak interpretability and generalizability in motion prediction for autonomous vehicles by proposing the Goal-based Neural Variational Agent (GNeVA), an interpretable generative model that achieves comparable performance to state-of-the-art methods.
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.