Calibrating Driver Trust: How trust factors influence driver’s trust in Driver Assistance Systems in trucks

Abstract: Vehicle automation has garnered increasing attention as a means of improving safety and efficiency. Advanced Driver Assistance Systems (ADAS) have gained popularity in the transport industry. However, establishing an appropriate level of trust in these systems is crucial for their successful implementation. This research explores the factors influencing driver trust calibration in different levels of automation within driver assistance systems for commercial mobility trucks to ensure drivers comprehend the limitations of these systems and uphold road safety. A qualitative approach involved eleven interviews and observations with drivers to explore their perceptions, experiences, and expectations regarding these systems. The study’s findings extend the Hoff and Bashir Trust model to include significant social factors in calibrating trust. These findings offer valuable insights into the various trust factors that impact driver trust calibration at different levels of automation in driver assistance systems for commercial mobility trucks. These insights contribute to academia in that they help understand how trust in automation is formed and calibrated in real-world settings. In the automotive industry, they can guide the design and implementation of these systems to enhance future drivers’ safety and overall experience.