Graph neural network based abnormal perception information reconstruction and robust autonomous navigation

Zhiwei Zhang, Zhe Liu, Yanzi Miao, Xiaoping Ma
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引用次数: 0

Abstract

Purpose This paper aims to develop a robust navigation enhancement framework to handle one of the most urgent needs for real applications of autonomous vehicles nowadays, as these corner cases act as the most commonly occurred risks in potential self-driving accidents. Design/methodology/approach In this paper, the main idea is to fully exploit the consistent features among spatio-temporal data and thus detect the anomalies and build residual channels to reconstruct the abnormal information. The authors first develop an anomaly detection algorithm, then followed by a corresponding disturbed information reconstruction network which has strong robustness to address both the nature disturbances and external attacks. Finally, the authors introduce a fully end-to-end resilient navigation performance enhancement framework to improve the driving performance of existing self-driving models under attacks and disturbances. Findings Comparison results on CARLA platform and real experiments demonstrate strong resilience of the authors’ approach which enhances the navigation performance under disturbances and attacks. Originality/value Reliable and resilient navigation performance under various nature disturbances and even external attacks is one of the most urgent needs for real applications of autonomous vehicles nowadays, as these corner cases act as the most commonly occurred risks in potential self-driving accidents. The information reconstruction approach provides a resilient navigation performance enhancement method for existing self-driving models.
基于图神经网络的异常感知信息重构和鲁棒自主导航
设计/方法/途径 本文的主要思路是充分利用时空数据之间的一致性特征,从而检测异常情况,并建立残差通道来重构异常信息。作者首先开发了一种异常检测算法,然后开发了相应的干扰信息重构网络,该网络具有很强的鲁棒性,可以同时应对自然干扰和外部攻击。研究结果在 CARLA 平台和实际实验中的对比结果表明,作者的方法具有很强的鲁棒性,可以提高在干扰和攻击下的导航性能。原创性/价值在各种自然干扰甚至外部攻击下,可靠而有弹性的导航性能是当今自动驾驶汽车实际应用中最迫切的需求之一,因为这些角情况是潜在自动驾驶事故中最常发生的风险。信息重构方法为现有的自动驾驶模型提供了一种弹性导航性能增强方法。
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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