用于自动驾驶汽车预测性维护的机器学习算法

Chirag Vinalbhai Shah
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引用次数: 0

摘要

自动驾驶汽车系统的复杂性和危险性给预测性维护带来了巨大挑战。由于自动驾驶汽车系统软件和硬件的无能可能导致危及生命的撞车事故,因此应定期进行维护以保护人类安全。对于汽车系统而言,预测未来故障并提前采取行动以维护系统可靠性和安全性在大规模产品设计中至关重要。本文将探讨几种机器学习算法,包括用于自动驾驶汽车系统维护需求评估的回归技术、分类技术、集合技术、聚类技术和深度学习技术。实验结果表明,预测性维护对自动驾驶汽车改进系统设计或降低威胁风险大有帮助。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Machine Learning Algorithms for Predictive Maintenance in Autonomous Vehicles
The complexity and hazards of autonomous vehicle systems have posed a significant challenge in predictive maintenance. Since the incompetence of autonomous vehicle system software and hardware could lead to life-threatening crashes, maintenance should be performed regularly to protect human safety. For automotive systems, predicting future failures and taking actions in advance to maintain system reliability and safety is very crucial in large-scale product design. This paper will explore several machine learning algorithms including regression techniques, classification techniques, ensemble techniques, clustering techniques, and deep learning techniques used for system maintenance need assessment in autonomous vehicles. Experimental results indicate that predictive maintenance can be greatly helpful for autonomous vehicles either in improving system design or mitigating the risk of threats.
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