Automated Driving Systems (ADS) have the potential to greatly decrease crashes and save lives. However, there are many important, unanswered questions about ADS particularly around L3, L4, and L5 vehicles. This project addressed critical questions around the: (1) applicability of telltales specified in FMVSS 101 to L4 and L5 vehicles, and (2) gaining a better understanding of how the mental models of both the driver and occupant impact the development of trust in the ADS and – ultimately- the safe deployment of ADS technology.
The TTI team conducted three integrated research activities to answer the central research questions. First, a focused literature and state-of-the-art review identified the characteristics of L4 and L5 vehicles relevant to existing telltales, indicators, controls, and warnings (audible and visual) as specified in the FMVSS, as well as data sources relevant to the relationship between drivers’ mental models of ADS and how they impact the development of appropriate vs. inappropriate trust in ADS (L3-L5), and relevant research methods and candidate behavioral measures. Next, we employed a mix of analytical and empirical activities that evaluated telltales of legacy vehicles and determined whether they were applicable to L4/L5 ADS vehicles.
Finally, an integrated series of driving simulator studies focused on the complex relationship between mental models, trust, and information presented through the HMI. Importantly, the simulator supported a flexible and valid approach to implementing L3-L5 functionality, and our approach included a mix of qualitative data, objective driving performance measures, and glance behaviors to assess and refine strategies for determining and calibrating the relationships between mental models and trust.
Project Title: Telltales and Human Machine Interface Concepts in the Development of Trust and Mental Models in Automated Driving Systems
Project Start and End Dates: September 2019 – est. November 2023
Author List: Mike Manser
Sponsor/Funding Source: National Highway Traffic Safety Administration