Identifying Perturbed Roadway Signs: Perception of AI Capabilities

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References

Deng

Karam

L. J.

(2020). Universal adversarial attack via enhanced projected gradient descent. In 2020 IEEE International Conference on Image Processing (ICIP), 1241-1245.

Eykholt

Evtimov

Fernandes

Rahmati

Xiao

Prakash

Kohno

Song

(2018). Robust physical-world attacks on deep learning models.

Goodfellow

I. J.

Shlens

Szegedy

(2015). Explaining and harnessing adversarial examples. International Conference on Learning Representations.

Halim

Kalsoom

Bashir

Abbas

(2016). Artificial Intelligence techniques for driving safety and vehicle crash prediction. Artificial Intelligence Review, 46(3), 351-387.

Jian

J. Y.

Bisantz

A. M.

Drury

C. G.

(2000). Foundations for an empirically determined scale of trust in automated systems. International Journal of Cognitive Ergonomics, 4(1), 53-71.

Mishler

Garcia

Fuller-Jakaitis

Wang

Still

Chen

(2021). Predicting a malicious stop sign: Knowledge, exposure, trust in AI. In Proceedings of the Human Factors and Ergonomics Society 65th International Annual Meeting (Vol. 65, No. 1, pp. 347-348). Sage CA: Los Angeles, CA: SAGE Publications.

Parasuraman

Riley

(1997). Humans and automation: Use, misuse, disuse, abuse. Human Factors, 39(2), 230-253.

Qiu

Liu

Zhou

(2019). Review of Artificial Intelligence adversarial attack and defense technologies. Applied Sciences, 9(5), 909.

SAE International. (2021). Taxonomy and definitions for terms related to driving automation systems for on-road motor vehicles (Publication No. J3016_202104).