解釈可能な自己認識ニューラルネットワークによる堅牢な軌道予測
Interpretable Self-Aware Neural Networks for Robust Trajectory Prediction
自動運転の軌道予測において、低次元で解釈可能な潜在空間上でエビデンシャル深層学習により認識的不確実性を推定し、過去のエージェント行動・道路構造・社会的文脈という意味的概念に不確実性を分配する手法を提案した。
著者: Masha Itkina, Mykel J. Kochenderfer
分類: cs.RO, cs.CV, cs.LG
原文アブストラクト
Although neural networks have seen tremendous success as predictive models in a variety of domains, they can be overly confident in their predictions on out-of-distribution (OOD) data. To be viable for safety-critical applications, like autonomous vehicles, neural networks must accurately estimate their epistemic or model uncertainty, achieving a level of system self-awareness. Techniques for epistemic uncertainty quantification often require OOD data during training or multiple neural network forward passes during inference. These approaches may not be suitable for real-time performance on high-dimensional inputs. Furthermore, existing methods lack interpretability of the estimated uncertainty, which limits their usefulness both to engineers for further system development and to downstream modules in the autonomy stack. We propose the use of evidential deep learning to estimate the epistemic uncertainty over a low-dimensional, interpretable latent space in a trajectory prediction setting. We introduce an interpretable paradigm for trajectory prediction that distributes the uncertainty among the semantic concepts: past agent behavior, road structure, and social context. We validate our approach on real-world autonomous driving data, demonstrating superior performance over state-of-the-art baselines. Our code is available at: https://github.com/sisl/InterpretableSelfAwarePrediction.
