论文标题
在模块化自动驾驶汽车软件中解释性
Towards Explainability in Modular Autonomous Vehicle Software
论文作者
论文摘要
安全至关重要的自治系统需要可信赖和透明的决策过程才能在现实世界中部署。机器学习的进步引入了高性能，主要是通过黑框算法。我们专门针对自动驾驶汽车（AV）的解释性讨论。作为一个安全至关重要的系统，AVS提供了独特的机会来利用尖端的机器学习技术，同时需要在决策中透明度。 AV采取的每种动作中的可解释性在事后分析中至关重要，在此可能需要责备分配的情况下。在本文中，我们提供了有关研究人员如何考虑将解释性和解释性纳入设计和优化的单独自动驾驶汽车模块（包括感知，计划和控制）的定位。
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.
