Challenges Of Testing Highly Automated Vehicles: A Literature Review

D. Karunakaran, J. S. Berrio, Stewart Worrall, E. Nebot
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引用次数: 2

Abstract

With the advent of the autonomous vehicle, there is potential to reduce the accident rate to a minimum level. Modern automated vehicles will undoubtedly include machine learning (ML) and probabilistic techniques. These algorithms with a non-deterministic world significantly complicate the safety assessment process. In addition, the autonomous system handles the responsibility of safe navigation, so the vehicle has to ensure its safety by itself. Due to these reasons, it is essential to thoroughly assess the system before deploying it on public roads. However, there are many testing challenges for highly automated vehicles (HAVs) to overcome before the wide-scale deployment. In this paper, we conducted a semi-systematic literature review on several issues and challenges related to the testing of HAVs.
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测试高度自动化车辆的挑战:文献综述
随着自动驾驶汽车的出现,有可能将事故率降低到最低水平。现代自动驾驶汽车无疑将包括机器学习(ML)和概率技术。这些具有非确定性世界的算法显著地使安全评估过程复杂化。此外,自动驾驶系统还承担着安全导航的责任,因此车辆必须自己确保自身的安全。因此,在正式投入使用之前,必须对该系统进行彻底的评估。然而,在大规模部署之前,高度自动化车辆(hav)需要克服许多测试挑战。在本文中,我们对与hav测试相关的几个问题和挑战进行了半系统的文献综述。
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