Do driver’s characteristics, system performance, perceived safety, and trust influence how drivers use partial automation?: A structural equation modelling analysis

The present study surveyed actual extensive users of SAE Level 2 partially automated cars to investigate how driver’s characteristics (i.e., socio-demographics, driving experience, personality), system performance, perceived safety, and trust in partial automation influence use of partial automation. 81% of respondents stated that they use their...

The influence of a color themed HMI on trust and take-over performance in automated vehicles

Introduction: SAE Level 3 is known as conditional driving automation. As long as certain conditions are met, there is no need to supervise the technology and the driver can engage in non-driving related tasks (NDRTs). However, a human driver must be present and alert to take over when the automation is facing its system limits. When such an...

How Stated Accuracy of an AI System and Analogies to Explain Accuracy Affect Human Reliance on the System

AI systems are increasingly being used to support human decision making. It is important that AI advice is followed appropriately. However, according to existing literature, users typically under-rely or over-rely on AI systems, and this leads to sub-optimal team performance. In this context, we investigate the role of stated system accuracy...