汎化性と解釈性を両立する運動予測：深層変分ベイズアプローチ
Towards Generalizable and Interpretable Motion Prediction: A Deep Variational Bayes Approach
周囲車両の将来挙動を予測するため、目的地の空間分布を変分混合ガウスで推定する解釈可能な生成モデルGNeVAを提案し、分布外ケースへの汎化性も向上させた。
著者: Juanwu Lu, Wei Zhan, Masayoshi Tomizuka, Yeping Hu
分類: cs.AI, cs.RO
原文アブストラクト
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.
