モジュール型自動運転ソフトウェアにおける説明可能性に向けて
Towards Explainability in Modular Autonomous Vehicle Software
自動運転車の知覚・計画・制御の各モジュールに説明可能性と解釈可能性を組み込むための研究の方向性を論じたポジションペーパー。
著者: Hongrui Zheng, Zirui Zang, Shuo Yang, Rahul Mangharam
分類: cs.RO
原文アブストラクト
Safety-critical Autonomous Systems require trustworthy and transparent decision-making process to be deployable in the real world. The advancement of Machine Learning introduces high performance but largely through black-box algorithms. We focus the discussion of explainability specifically with Autonomous Vehicles (AVs). As a safety-critical system, AVs provide the unique opportunity to utilize cutting-edge Machine Learning techniques while requiring transparency in decision making. Interpretability in every action the AV takes becomes crucial in post-hoc analysis where blame assignment might be necessary. In this paper, we provide positioning on how researchers could consider incorporating explainability and interpretability into design and optimization of separate Autonomous Vehicle modules including Perception, Planning, and Control.
