ESR2 – Long Term Effects of Automation on User Behavior

Popular scientific abstract

In the past years, the transport field welcomed an emerging field of practices captured under the umbrella term ‘automated driving systems’ (ADS), which consist of automated/automatous vehicles (AV) and connected automated vehicles (CAV) equipped with various automated features. For example, levels of automation (e.g. SAE), shortly described under the following categories – non-autonomous (level 0), assistive (level 1-4) or fully autonomous (level 5). The term ADS is surrounded with positive rhetoric and promises about the ability to provide support (in the form of safe, trustworthy, comfortable and pleasant user experiences, etc.) for real-world transport issues quickly and comprehensively, and thus provide assistance through desired transportation methods. These ADSs are artificially intelligent embodied transport agents that appear to behave logically and intuitively through their interactive interfaces. They are envisioned to lead to a paradigm shift in transportation systems in terms of user experience, mode choices, and business models.

However, as ADSs are in the route to becoming the new normal, quite a few questions and concerns still need to be addressed before these automated vehicles are regarded as functionally members on societies’ roads and urban traffic. For example, how and to what degree do we need to properly study the interaction between humans and automation in order to prepare for future cities and the environment of tomorrow? As well as addressing concerns on collaborative interaction atmospheres between humans and automation in urban cities, thus leading a better functioning society. By applying better research and design methods with the human user in the loop, we could evaluate through patterns of learning strategies and interaction design strategies the long-term effects of automation exposure on user behaviour over repeated use. This could help us identify how humans or AV users experience the interaction process with automated driving systems over repeated use, thus trace and track successful patterns of interaction, trust and acceptance levels, as well as the assimilation process. We aim to evaluate the quality of interaction of HMI based on different user types, as users navigate through road stressors of urban traffic density and atmospheres, as the vehicle attempts to interact/communicate with the driver, other vehicles on the road, and VRUs in order to achieve safety assurance. This also includes understanding and being aware of the specific surrounding or environment features, for example, rail crossing, new constructed buildings, road constructions, new road signs, among others. In this study, we aim to analyse the epistemological claims based on user behaviour that accompany vehicle automation technology in the technical and behavioural domain. Foremost, we will systematically review scientific literature that reflect on developments in the field. Through a holistic and cross-cultural approach, the aim is that of capturing, illuminating and harnessing automation and user data on long-term use, as a result lead to valid and useful knowledge discoveries, so that the field of HMI can be scientifically and practically relevant to industry and society.

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 affiliation

My host university is 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, under the department of mechanical engineering.

Main Supervisor:

Prof. Dr. Klaus Bengler (TUM) : bengler@tum.de

Co-Supervisor:

Prof Natasha Merat (Leeds) : N.Merat@its.leeds.ac.uk

Background

In current years, we have seen a steady rise in AV models on the market, and many discussions around AV design in general. Currently, efforts made involve advanced driver assistance systems (ADASs), which consist of automatic braking and lane assistance – designed to inhibit user interference and advance safety. 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 the transport technology could provide to society and global economics. However, there still exist a gap in fully understanding users’ behaviour changes over long use, and thus still a long way to go in terms of making them a central part of the everyday life of users. As a result, more studies are needed that focus on interaction design strategies and patterns of learning strategies over long-term use of automation.

In this case, interaction design strategies will be employed to explore several use case scenarios to synthesize how the user might possibly interact with the AV (over repeated use) and the changes to their behaviour (in terms of experiences, acceptance patterns, and trust levels) afterwards. 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 with the AV and vice versa. 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 satisfying experiences, trust and acceptance.

What is also of interest to examine is the behavioural process or route that users take when interacting with automation over time. Thus leading to the following questions: do users tend to trust an automation before accepting or do they accept it and then come to trust it. In addition, what type of automation acceptance is associated with which level of trust to indicate the trustworthy of the automation? This however is based on their experiences with the automation over long-term use, and how they rate the automation’s behaviour towards various factors of use. We measure interaction quality through user experiences with an automation in use, and also based on user behaviours such as trust and acceptance. It is also interesting to explore experience – trust – acceptance as a cause-effect process. In this case, we need to explore different user-automation acceptance states and trust levels over long-term use.

Aims and objectives

 The research objectives are as follows:

  1. Evaluate different user types’ (based on age, gender, culture and personality) trust and acceptance of HMIs across different AV characteristics over long-term use.
  2. Improve understanding on how automation exposure can influence changes in user behaviour, such as acceptance and trust.
  3. Employ interaction design strategies to evaluate users’ compatibility modes with automation in order to understand users’ behaviour changes, thus improve on interaction quality.

Research description

This study generates a set of questions relating to vehicle automation and user behaviour, and these broadly fit with the literature identified. While there has been some progress in the design and development of automated vehicles to aid in transport issues, many challenges remain and call for further research. These challenges include misunderstanding of AVs roles by users, mismatch of expectations, deficient engagement for a prolonged time by users (losing interest quickly), lack of trust due to safety and transparency issues, AV acceptance and assimilation by target users and in cross cultural settings, and ethical implications of using AVs as forms of transportation. These are large research areas in their own right; and it is not imagined nor predicted that all associated research questions can be answered in the immediate term. However, we do aim to provide insight and knowledge discoveries that may help assist the design of AVs that users find it pleasant to use, trustworthy, and useful in the long run.

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 effects of vehicle automation on a user 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.

Results

While users/drivers of AVs may be a focal point of interest in this study, we need to recognise their behavioural changes over long-term use when designing interactions based on automation exposure . This is because how the AV may react in different situational context and with other road users may be perceived by the user/driver as satisfying or dissatisfying, thus influencing on user experience, trust and acceptance on 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.

My publications

Forthcoming / impending …

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

Barg-Walkow, L. H., & Rogers, W. A. (2016). The Effect of Incorrect Reliability Information on Expectations, Perceptions, and Use of Automation. Human Factors: The Journal of the Human Factors and Ergonomics Society, 58(2), 242–260. doi.org/10.1177/0018720815610271