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. In this position paper, 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.


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    Title :

    Towards Explainability in Modular Autonomous System Software


    Contributors:


    Publication date :

    2023-06-04


    Size :

    7631146 byte





    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



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