Since the 2004 DARPA Grand Challenge, the autonomous driving technology has witnessed nearly two decades of rapid development. Particularly, in recent years, with the application of new sensors and deep learning technologies extending to the autonomous field, the development of autonomous driving technology has continued to make breakthroughs. Thus, many carmakers and high-tech giants dedicated to research and system development of autonomous driving. However, as the foundation of autonomous driving, the deep learning technology faces many new security risks. The academic community has proposed deep learning countermeasures against the adversarial examples and AI backdoor, and has introduced them into the autonomous driving field for verification. Deep learning security matters to autonomous driving system security, and then matters to personal safety, which is an issue that deserves attention and research.This paper provides an summary of the concepts, developments and recent research in deep learning security technologies in autonomous driving. Firstly, we briefly introduce the deep learning framework and pipeline in the autonomous driving system, which mainly include the deep learning technologies and algorithms commonly used in this field. Moreover, we focus on the potential security threats of the deep learning based autonomous driving system in each functional layer in turn. We reviews the development of deep learning attack technologies to autonomous driving, investigates the State-of-the-Art algorithms, and reveals the potential risks. At last, we provides an outlook on deep learning security in the autonomous driving field and proposes recommendations for building a safe and trustworthy autonomous driving system.
翻译:自2004年DARPA大挑战以来,自主驾驶技术经历了近20年的快速发展,特别是近年来,随着新传感器和深层次学习技术的应用扩展到自主领域,自主驾驶技术的发展继续取得突破,因此许多汽车制造商和高科技巨人致力于自主驾驶研究和系统开发,然而,作为自主驾驶的基础,深层学习技术面临许多新的安全风险。学术界提议对对抗性实例和AI后门进行深入学习,并将其引入自主驾驶领域进行核查。对自主驾驶系统安全进行深层学习,然后对人身安全进行深层研究,这是一个值得关注和研究的问题。本文概述了自主驾驶技术深层学习的概念、发展以及最近对自主驾驶安全技术的研究。首先,我们简要介绍了自主驾驶系统中的深层学习框架和管道,其中主要包括在这一领域常用的深层学习技术和算法。此外,我们侧重于每个功能层深层次基于深层次学习的自主驾驶驾驶系统的潜在安全威胁。我们审查了在自主驾驶领域进行深层学习的动态技术的发展,并提出了向自主驾驶领域展示未来风险。我们提出了在自主驾驶领域进行持续学习的动态研究的系统,我们提出了最后的学习领域。