Towards Safe, Explainable, and Regulated Autonomous Driving
- URL: http://arxiv.org/abs/2111.10518v4
- Date: Fri, 26 May 2023 05:28:30 GMT
- Title: Towards Safe, Explainable, and Regulated Autonomous Driving
- Authors: Shahin Atakishiyev, Mohammad Salameh, Hengshuai Yao, Randy Goebel
- Abstract summary: We propose a framework that integrates autonomous control, explainable AI (XAI), and regulatory compliance.
We describe relevant XAI approaches that can help achieve the goals of the framework.
- Score: 11.043966021881426
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: There has been recent and growing interest in the development and deployment
of autonomous vehicles, encouraged by the empirical successes of powerful
artificial intelligence techniques (AI), especially in the applications of deep
learning and reinforcement learning. However, as demonstrated by recent traffic
accidents, autonomous driving technology is not fully reliable for safe
deployment. As AI is the main technology behind the intelligent navigation
systems of self-driving vehicles, both the stakeholders and transportation
regulators require their AI-driven software architecture to be safe,
explainable, and regulatory compliant. In this paper, we propose a design
framework that integrates autonomous control, explainable AI (XAI), and
regulatory compliance to address this issue, and then provide an initial
validation of the framework with a critical analysis in a case study. Moreover,
we describe relevant XAI approaches that can help achieve the goals of the
framework.
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