JRM Vol.32 No.3 pp. 503-519
doi: 10.20965/jrm.2020.p0503


Personalized Subjective Driving Risk: Analysis and Prediction

Naren Bao*1, Alexander Carballo*2,*3, Chiyomi Miyajima*4, Eijiro Takeuchi*1,*3, and Kazuya Takeda*1,*2,*3

*1Graduate School of Informatics, Nagoya University
Furo-cho, Chikusa-ku, Nagoya 464-8601, Japan

*2Institutes of Innovation for Future Society, Nagoya University
Furo-cho, Chikusa-ku, Nagoya 464-8601, Japan

*3TierIV Inc., Open Innovation Center, Nagoya University
1-1-3 Meieki-cho, Nakamura-ku, Nagoya 450-6610, Japan

*4Department of Information Systems, School of Informatics, Daido University
10-3 Takiharu-cho, Minami-ku, Nagoya 457-8530, Japan

January 17, 2020
March 26, 2020
June 20, 2020
risk assessment, subjective risk, personalization, risk factor identification, lane change
Personalized Subjective Driving Risk: Analysis and Prediction

Personalized subjective driving risk model (PSDRM) proposed in this study, including our subjective risk dataset, and RFGA-BLTSM framework

Subjective risk assessment is an important technology for enhancing driving safety, because an individual adjusts his/her driving behavior according to his/her own subjective perception of risk. This study presents a novel framework for modeling personalized subjective driving risk during expressway lane changes. The objectives of this study are twofold: (i) to use ego vehicle driving signals and surrounding vehicle locations in a data-driven and explainable approach to identify the possible influential factors of subjective risk while driving and (ii) to predict the specific individual’s subjective risk level just before a lane change. We propose the personalized subjective driving risk model, a combined framework that uses a random forest-based method optimized by genetic algorithms to analyze the influential risk factors, and uses a bidirectional long short term memory to predict subjective risk. The results demonstrate that our framework can extract individual differences of subjective risk factors, and that the identification of individualized risk factors leads to better modeling of personalized subjective driving risk.

Cite this article as:
N. Bao, A. Carballo, C. Miyajima, E. Takeuchi, and K. Takeda, “Personalized Subjective Driving Risk: Analysis and Prediction,” J. Robot. Mechatron., Vol.32, No.3, pp. 503-519, 2020.
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