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Citation |
Riahi Samani A, Mishra S. IEEE Trans. Intel. Transp. Syst. 2022; 23(10): 19161-19172. |
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Copyright |
(Copyright © 2022, IEEE (Institute of Electrical and Electronics Engineers)) |
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DOI |
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PMID |
unavailable |
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Abstract |
Assessing drivers' behavior after transition from automated to manual driving (referred as to take-over condition) in highly automated vehicles (SAE Level 4) is a widely studied area. However, analyzing Commercial Motor Vehicle (CMV) drivers' post-take-over behavior has received less attention, whereas it is forecasted that CMVs will be the first to vastly adopt highly automated vehicle technology. This study aims to analyze and compare CMV drivers' driving styles in take-over conditions with continuous manual driving. Assessing driving style, which is a function of various variables and actions, provides a comprehensive understanding of the changes in post-take-over behavior. Hence, the driving behaviors of 45 CMV drivers are collected using a driving simulator, and we investigated whether the driving style is subject to driving mode (take-over or manual), automation duration, repeated take-overs, and driver's factors. Here, drivers' driving behavior is classified into three driving styles, normal, conservative, and risky by using Multivariate Dynamic Time Warping approach followed by $k$ -means clustering. Comparing driving styles in take-over and manual driving conditions showed that conservative and risky driving styles (as in more speed reduction, harder brakes, and unsafe turns) are more common in take-over conditions. Furthermore, to gain behavioral insight into the detected driving styles, Generalized Linear Models are applied to model the driving behavior indices in each driving style. Modeling results showed that long-phase automation, traffic/environmental conditions, and bad driving history deteriorate post-take-over behavior. The findings of this paper provide valuable information to automotive companies and transportation planners on the nature of take-over conditions. Language: en |
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Keywords |
Accidents; Automation; Automobiles; commercial motor vehicles; driving style; Feature extraction; highly automated vehicles; Manuals; multivariate dynamic time warping; Roads; Take-over; Vehicles |