Popular scientific abstract
With a lot of work being done in the vehicle automation industry, AVs have significantly accelerated over the past years and have become a sudden research trend in the transportation industry and academic world. This is a result of the findings that AVs are becoming technically achievable and also findings that have acknowledged several significant and noteworthy benefits that automation technology could provide to society and global economics. However, there still exists a gap in fully understanding the long term effects of automation on users’ behaviour, and essentially still a long way in terms of making them a central part of the everyday life of society. Importantly, more long-term studies are needed that emphasise interaction design strategies and learning patterns over automation long-term use.
While AV users/drivers may be a focal point of interest in this study, we do also recognise the influence of vulnerable road users in the long-term HAI loop. This is because how the AV may react in different situational contexts and with other road users may be perceived by the user/driver differently, thus influencing user experience, trust and acceptance in the long run. How well the AV interprets situations and its interactive process is beneficially in how the driver’s behaviour is influenced. Thus, clear communication of intent would be recommended and the ability to act in a socially acceptable manner would be ideal for successful interaction to take place. Thus, creating a space where different user types might find the interaction satisfying and acceptable for a prolonged time.
Who am I?
My name is Naomi Yvonne Mbelekani (M.Sc) and I am a research associate at the Chair of Ergonomics (Lehrstuhl für Ergonomie), Department of Mechenical Engineering.
My host university is the Technical University of Munich (Technische Universität München). I am a doctorate and employed by the Chair of Ergonomics (Lehrstuhl für Ergonomie) as a research associate, in the department of mechanical engineering.
In this case, interaction design strategies will be employed to examine several scenarios to synthesize how the user might possibly interact with the AV (over long-term use) in order to understand behavioural changes and long-term automation effects. The development is largely hinged on the human-machine interface (HMI), which is the medium through which the driver or AV user meets and interacts. Thus, we argue that for AVs to interact in a successful manner with different users, their interactive architecture should encompass communication modalities (audible, visual, and other) and capabilities that users/drivers find useful. Furthermore, the interaction through the user interface should meet potential users’ requirements, concerning a number of factors in achieving safety, trust and acceptance. It is of interest to explore the relationship between experience – trust – acceptance as a cause-effect process over long-term use.
Aims and objectives
Aim: Investigation of different users’ long-term interaction with automation by measuring “learnability” and behaviour change, as well as the relation to trust and acceptability.
The research objectives are as follows:
- Define how long is long enough in long-term research,
- Analytically assess users’ learnability and trust trajectories towards automation, as a result, define their parameters
- Evaluate the influence of automation on different user types’ behaviour change over long-term use
- Evaluate users’ learning and trust patterns as moderators of acceptance, in order to improve driver-AV interaction quality
This study generates a set of questions relating to long-term human-automation interactions. While there has been some progress in the design and development of AVs to aid in mobility issues, many safety challenges remain and call for further research. We aim to provide insight that aid safe and risk-free interaction design strategies for HAIs and bridges the gap of knowledge in long-term research.
The research is divided into 3 stages:
- increasing understanding of users’ trust, learning patterns and learnability towards AVs for various interaction scenarios,
- incorporating different user types into the interaction design, and comparing trust and learnability ratios to acceptance
We plan on tackling the study by observing challenging urban traffic settings, evaluating users’ behaviour and iteratively prototyping for different user types. The aim is to investigate the long-term HAI in a sociotechnical system and intercultural setting. As humans have no experience regarding how to relate to AVs and how the AVs should relate to them, thus the resulting research questions are relevant – moving towards techno-cities and efficiency. The study will use advanced driving simulators as a basis for improving understanding of how AV users’ experience, trust, and acceptance of AVs change with long-term/repeated use in urban traffic. An evaluation of AV interaction design strategies will be performed, and patterns of learning strategies of AV users (“drivers/passengers”) by user types will be established.
The results will be shared once available…
References and links
Aria, E., Olstam, J., & Schwietering, C. (2016). Investigation of Automated Vehicle Effects on Driver’s Behavior and Traffic Performance. ISEHP 2016. International Symposium on Enhancing Highway Performance. Transportation Research Procedia. Volume 15, 2016, Pages 761–770. doi: 10.1016/j.trpro.2016.06.063