Zhiwei Zhang, Saasha Nair, Zhe Liu, Yanzi Miao, Xiaoping Ma
{"title":"Robust and transferable end-to-end navigation against disturbances and external attacks: an adversarial training approach","authors":"Zhiwei Zhang, Saasha Nair, Zhe Liu, Yanzi Miao, Xiaoping Ma","doi":"10.1108/ria-08-2023-0109","DOIUrl":null,"url":null,"abstract":"Purpose\nThis paper aims to facilitate the research and development of resilient navigation approaches, explore the robustness of adversarial training to different interferences and promote their practical applications in real complex environments.\n\nDesign/methodology/approach\nIn this paper, the authors first summarize the real accidents of self-driving cars and develop a set of methods to simulate challenging scenarios by introducing simulated disturbances and attacks into the input sensor data. Then a robust and transferable adversarial training approach is proposed to improve the performance and resilience of current navigation models, followed by a multi-modality fusion-based end-to-end navigation network to demonstrate real-world performance of the methods. In addition, an augmented self-driving simulator with designed evaluation metrics is built to evaluate navigation models.\n\nFindings\nSynthetical experiments in simulator demonstrate the robustness and transferability of the proposed adversarial training strategy. The simulation function flow can also be used for promoting any robust perception or navigation researches. Then a multi-modality fusion-based navigation framework is proposed as a light-weight model to evaluate the adversarial training method in real-world.\n\nOriginality/value\nThe adversarial training approach provides a transferable and robust enhancement for navigation models both in simulation and real-world.\n","PeriodicalId":501194,"journal":{"name":"Robotic Intelligence and Automation","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2024-06-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Robotic Intelligence and Automation","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1108/ria-08-2023-0109","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
Purpose
This paper aims to facilitate the research and development of resilient navigation approaches, explore the robustness of adversarial training to different interferences and promote their practical applications in real complex environments.
Design/methodology/approach
In this paper, the authors first summarize the real accidents of self-driving cars and develop a set of methods to simulate challenging scenarios by introducing simulated disturbances and attacks into the input sensor data. Then a robust and transferable adversarial training approach is proposed to improve the performance and resilience of current navigation models, followed by a multi-modality fusion-based end-to-end navigation network to demonstrate real-world performance of the methods. In addition, an augmented self-driving simulator with designed evaluation metrics is built to evaluate navigation models.
Findings
Synthetical experiments in simulator demonstrate the robustness and transferability of the proposed adversarial training strategy. The simulation function flow can also be used for promoting any robust perception or navigation researches. Then a multi-modality fusion-based navigation framework is proposed as a light-weight model to evaluate the adversarial training method in real-world.
Originality/value
The adversarial training approach provides a transferable and robust enhancement for navigation models both in simulation and real-world.