How Does Explanation-Based Knowledge Influence Driver Take-Over in Conditional Driving Automation?

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.