Abstract
This article focuses on explanation-based knowledge about system limitations (SLs) under conditional driving automation (society of automotive engineers level 3) and aims to reveal how this knowledge influences driver intervention. By illustrating the relationships between the driving environment, system, and mental model, knowledge in dynamic decision-making processing for responding to an issued request to intervene (RtI), occurrence of SL, concept of RtI, and scene(s) related to SL are determined by knowledge-based learning. Based on three concepts, the knowledge is examined at five levels: 1) no explanation, 2) occurrence of SL, 3) concept of RtI, 4) some typical scenes related to SL, and 5) all of the above. Data collection is conducted on a driving simulator, and 100 people with no experience of automated driving participated. The experimental results show that instructing drivers in typical situations contributes to a greater increase in the rate of successful intervention in car control from 55% to 95%. Furthermore, instructing them on the concept of RtI is conducive to a significant reduction in response time from 5.48 to 3.62 s in their first experience of RtI. It is also revealed that the knowledge-based learning effect dwindles but does not vanish even after drivers experience RtI a number of times. Compared to explaining all possible situations to a driver, introducing typical situations results in better take-over performances even in critical or unexplained scenarios. This article demonstrates the importance and necessity of this knowledge, especially the explanation of sample scenes related to SL, which contributes to drivers' take-over behavior.
Highlights
T HE CONCEPT of automated driving has become widespread over the last decade as the capabilities of automated driving systems have rapidly increased, thereby raisingManuscript received August 4, 2019; revised July 11, 2020 and December 16, 2020; accepted December 28, 2020
We focus on the study of human-machine interactions in the dynamic information processing of driver intervention to illustrate the concepts of explanation-based knowledge about system limitation (SL)
The results of analysis of variables (ANOVA) conducted on the rates of successful intervention (RSI) for each participant show that the effect of the SL explanation level is statistically significant (F (4, 95) = 7.69, p < 0.001∗∗)
Summary
T HE CONCEPT of automated driving has become widespread over the last decade as the capabilities of automated driving systems have rapidly increased, thereby raisingManuscript received August 4, 2019; revised July 11, 2020 and December 16, 2020; accepted December 28, 2020. T HE CONCEPT of automated driving has become widespread over the last decade as the capabilities of automated driving systems have rapidly increased, thereby raising. The development of automated driving systems has been a step-by-step process. During the development of such systems, issues that influenced driving safety while the driver retained primary control, such as automation surprise [5], inattention and distraction [6], [7], excessive trust, and overreliance [8], were discussed and investigated
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