Workshop on explainable AI in automated driving: a user-centered interaction approach

Quentin Meteier, Marine Capallera, Leonardo Angelini, E. Mugellini, Omar Abou Khaled, S. Carrino, Emmanuel de Salis, Stéphane Galland, Susanne CJ Boll
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引用次数: 6

Abstract

With the increasing use of automation, users tend to delegate more tasks to the machines. Such complex systems are usually developed with "black box" Artificial Intelligence (AI), which makes these systems difficult to understand for the user. This assumption is particularly true in the field of automated driving since the level of automation is constantly increasing via the use of state-of-the-art AI solutions. We believe it is important to investigate the field of Explainable AI (XAI) in the context of automated driving since interpretability and transparency are key factors for increasing trust and security. In this workshop, we aim at gathering researchers and industry practitioners from different fields to brainstorm about XAI with a special focus on human-vehicle interaction. Questions like "what kind of explanation do we need", "which is the best trade-off between performance and explainability" and "how granular should the explanations be" will be addressed in this workshop.
自动驾驶中可解释的人工智能研讨会:以用户为中心的交互方法
随着自动化使用的增加,用户倾向于将更多的任务委托给机器。这种复杂的系统通常是用“黑匣子”人工智能(AI)开发的,这使得这些系统对用户来说很难理解。这一假设在自动驾驶领域尤其正确,因为通过使用最先进的人工智能解决方案,自动化水平正在不断提高。我们认为,在自动驾驶的背景下,研究可解释的人工智能(XAI)领域是很重要的,因为可解释性和透明度是增加信任和安全性的关键因素。在本次研讨会中,我们旨在聚集来自不同领域的研究人员和行业从业者,就人机交互进行头脑风暴。像“我们需要什么样的解释”、“性能和可解释性之间的最佳权衡”和“解释应该有多细”这样的问题将在本次研讨会中得到解决。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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