Analysis of trust and efficient take-over strategy with multi-sensor information fusion in conditionally automated driving

Chang Guo

doi:10.54254/2753-8818/51/2024CH0170

Research Article

Open access

Published on 1 November 2024

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Guo,C. (2024). Analysis of trust and efficient take-over strategy with multi-sensor information fusion in conditionally automated driving. Theoretical and Natural Science,51,58-64.

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Analysis of trust and efficient take-over strategy with multi-sensor information fusion in conditionally automated driving

Chang Guo *^,1,

¹ Chongqing University

* Author to whom correspondence should be addressed.

https://doi.org/10.54254/2753-8818/51/2024CH0170

Abstract

In the recent development of Autonomous Vehicles (AVs), human-machine trust and take-over strategy are important research contents, which is related to whether the driving system can make the most of its performance and ensure driving safety. At the same time, the unique advantages of Multi-Sensor Information Fusion (MSIF) also make it more and more applied to the auxiliary driving system. At present, research on MSIF applications in automated driving mainly focuses on external environment perception and map construction. However, there is little research on the application of MSIF in vehicle Human-Machine Interaction (HMI). Therefore, this paper analyzes the application of MSIF in trust detection and take-over HMI, proposes the advantages of applying MSIF to the HMI of AVs, and points out that the HMI design of intelligent vehicles in the future can enhance the application of MSIF. Dynamic monitoring of human-machine trust and dynamic adjustment of HMI strategy of the intelligent driving system are realized to achieve a better effect and further guarantee driving safety.

Keywords

Autonomous vehicle, multi-sensor information fusion, human-machine interaction, trust, take-over strategy.

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References

[1]. Huang, Z. X. , & Xia, M. . (2023). Interaction design in conditional autopilot takeover: a review based on transparency theory. Design, 8(4), 2364-2376.

[2]. Zhou, W. P. , Lu, L. , & Wang, J. M. . (2022). Research progress on multi-sensor information fusion in unmanned driving. Automotive Digest (01), 45-51. doi:10.19822/j.cnki.1671-6329.20210148.

[3]. Tan, Z. Y. , Dai, N. Y. , Zhang, R. F. , & Dai, K. Y. . (2020). Overview and perspectives on human-computer interaction in intelligent and connected vehicles. Computer Integrated Manufacturing Systems (10), 2615-2632. doi:10.13196/j.cims.2020.10.002.

[4]. Miwa, M. , Dibben, D. , & Yamada, T. . (2004). High accuracy torque calculation for a rotating machine using adaptive meshing. IEEE Transactions on Magnetics, 40(2), 1001-1004.

[5]. Li, J. . (2023). Interaction design for human-vehicle trust in human-vehicle co-driving scenarios Master (Dissertation, Beijing University of Posts and Telecommunications). Master https://link.cnki.net/doi/10.26969/d.cnki.gbydu.2023.002755 doi:10.26969/d.cnki.gbydu.2023.002755.

[6]. Jin, M. , Lu, G. , Chen, F. , & Shi, X. . (2020). How driving experience affect trust in automation from level 3 automated vehicles? An experimental analysis. 2020 IEEE 23rd Int. Conf. on Intelligent Transportation Systems (ITSC). IEEE.

[7]. He, W. H. , Wang T. Y. , Zhang, T. R. , & Tao, D. . (2024). The mechanism of influencing factors of human-machine trust in intelligent driving scenes. Chinese Journal of Ergonomics (02), 9-16. doi:10.13837/j.issn.1006-8309.2024.02.0002.

[8]. Gao, Z. F. , Li, W. M. , Liang, J. W. , Pan, H. X. , Xu, W. , & Shen, M. W. . (2021). Trust in automated vehicles. Advances in Psychological Science (12), 2172-2183.

[9]. Wu, Z. , Han, B. , & Ma, Y. F. . (2023). Research on driver take-over ability of human-vehicle co-driving in automated driving scenes. Special purpose vehicle (06), 81-84. doi:10.19999/j.cnki.1004-0226.2023.06.023.

[10]. Chu, Y. F. . (2023). Research on the Effect of Driver Trust Matching on Driving Control Take-over Master (Dissertation, Beijing University of Civil Engineering and Architecture). Master https://link.cnki.net/doi/10.26943/d.cnki.gbjzc.2023.000469 doi:10.26943/d.cnki.gbjzc.2023.000469.

[11]. Wan, X. D. , Zheng, H. , Sun, T. , Chen, H. Y. , & Li, C. . (2023). A method of requirement modeling for autonomous vehicle takeover system. Automotive Digest (11), 45-54. doi:10.19822/j.cnki.1671-6329.20220215.

[12]. You, F. , Zhang, J. H. , Zhang, J. , Deng, H. J. , & Liu, Y. J. . (2021). Interaction design for trust-based takeover systems in smart cars. Packaging Engineering (06), 20-28. doi:10.19554/j.cnki.1001-3563.2021.06.004.

[13]. Naujoks, F. , Mai, C. , & Neukum, A. . (2014). The effect of urgency of take-over requests during highly automated driving under distraction conditions. Int. Conf. on Applied Human Factors & Ergonomics Ahfe.

[14]. Yun, H. , & Yang, J. H. . (2020). Multimodal warning design for take-over request in conditionally automated driving. European Transport Research Review, 12(1).

Cite this article

Guo,C. (2024). Analysis of trust and efficient take-over strategy with multi-sensor information fusion in conditionally automated driving. Theoretical and Natural Science,51,58-64.

Data availability

The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.

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About volume

Volume title: Proceedings of CONF-MPCS 2024 Workshop: Quantum Machine Learning: Bridging Quantum Physics and Computational Simulations

Conference website: https://2024.confmpcs.org/

ISBN：978-1-83558-653-2(Print) / 978-1-83558-654-9(Online)

Conference date: 9 August 2024

Editor：Anil Fernando, Marwan Omar

Series: Theoretical and Natural Science

Volume number: Vol.51

ISSN：2753-8818(Print) / 2753-8826(Online)

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