Evaluations of Different Human Machine Interfaces for Presenting Right-Turn Timing at Intersections

Yang, Bo; Obana, Tatsuya; Wang, Zheng; Kaizuka, Tsutomu; Sugimachi, Toshiyuki; Sakurai, Toshiaki; Maki, Tetsuo; Nakano, Kimihiko

doi:10.1007/s13177-020-00223-4

Cited by 4 publications

(1 citation statement)

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“…[29], for example, propose an adaptive system that supports by presenting the approach information of other vehicles through peripheral vision with color information. Furthermore, collisions at intersections can be avoided and the driver's reaction time can be reduced by informing drivers about the existence of oncoming vehicles using right-turn timing assistance [30]. However, an important aspect that needs to be considered when designing driver assistance systems is the real-time assessment of the state of the driver [1].…”

Section: Introductionmentioning

confidence: 99%

Familiarity and Complexity during a Takeover in Highly Automated Driving

Scharfe-Scherf

Rußwinkel

2021

Int. J. ITS Res.

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This paper shows, how objective complexity and familiarity impact the subjective complexity and the time to make an action decision during the takeover task in a highly automated driving scenario. In the next generation of highly automated driving the driver remains as fallback and has to take over the driving task whenever the system reaches a limit. It is thus highly important to develop an assistance system that supports the individual driver based on information about the drivers’ current cognitive state. The impact of familiarity and complexity (objective and subjective) on the time to make an action decision during a takeover is investigated. To produce replicable driving scenarios and manipulate the independent variables situation familiarity and objective complexity, a driving simulator is used. Results show that the familiarity with a traffic situation as well as the objective complexity of the environment significantly influence the subjective complexity and the time to make an action decision. Furthermore, it is shown that the subjective complexity is a mediator variable between objective complexity/familiarity and the time to make an action decision. Complexity and familiarity are thus important parameters that have to be considered in the development of highly automated driving systems. Based on the presented mediation effect, the opportunity of gathering the drivers’ subjective complexity and adapting cognitive assistance systems accordingly is opened up. The results of this study provide a solid basis that enables an individualization of the takeover by implementing useful cognitive modeling to individualize cognitive assistance systems for highly automated driving.

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Section: Introductionmentioning

confidence: 99%