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Open AccessArticle

Development of Deep Learning Based Human-Centered Threat Assessment for Application to Automated Driving Vehicle

1
Department of Mechanical Systems Engineering, College of Engineering, Sookmyung Women’s University, Seoul 04310, Korea
2
Department of Computer Science, Korea Aerospace University, Goyang-si 10540, Korea
3
Department of Computer Science, College of Natural Science, Republic of Korea Naval Academy, Changwon-si 51704, Korea
4
Department of Mechanical and Aerospace Engineering, College of Engineering, Seoul National University, Seoul 08826, Korea
*
Authors to whom correspondence should be addressed.
Appl. Sci. 2020, 10(1), 253; https://doi.org/10.3390/app10010253
Received: 24 November 2019 / Revised: 22 December 2019 / Accepted: 25 December 2019 / Published: 28 December 2019
This paper describes the development of deep learning based human-centered threat assessment for application to automated driving vehicle. To achieve naturalistic driver model that would feel natural while safe to a human driver, manual driving characteristics are investigated through real-world driving test data. A probabilistic threat assessment with predicted collision time and collision probability is conducted to evaluate driving situations. On the basis of collision risk analysis, two kinds of deep learning have been implemented to reflect human driving characteristics for automated driving. A deep neural network (DNN) and recurrent neural network (RNN) are designed by neural architecture search (NAS), and by learning from the sequential data, respectively. The NAS is used to automatically design the individual driver’s neural network for efficient and effortless design process while ensuring training performance. Sequential trends in the host vehicle’s state can be incorporated through hand-made RNN. It has been shown from human-centered risk assessment simulations that two successfully designed deep learning driver models can provide conservative and progressive driving behavior similar to a manual human driver in both acceleration and deceleration situations by preventing collision. View Full-Text
Keywords: risk assessment; deep learning; neural architecture search; recurrent neural network; automated driving vehicle risk assessment; deep learning; neural architecture search; recurrent neural network; automated driving vehicle
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Shin, D.; Kim, H.-G.; Park, K.-M.; Yi, K. Development of Deep Learning Based Human-Centered Threat Assessment for Application to Automated Driving Vehicle. Appl. Sci. 2020, 10, 253.

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