ESR2 – Long Term Effects of Automation on User Behaviour

Popular scientific abstract

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.

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) :


Prof Natasha Merat (Leeds) :


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

Aim: Investigation of different user types’ long-term behavioural change towards automated driving by measuring “compatibility” and trust, as well as the relation to acceptance.

The research objectives are as follows:

  1. Analytically assess users’ compatibility and trust trajectories towards AVs, as a result, define their parameters
  2. Evaluate the influence of vehicle automation on different user types’ behaviour change over long-term use
  3. Evaluate users’ trust patterns and compatibility ratios as moderators of acceptance, in order to improve driver-AV 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.

The research is divided into 3 stages:

  1. increasing understanding of users’ trust patterns and compatibility towards AVs for various interaction scenarios,
  2. incorporating different user types into the interaction design, and comparing trust and compatibility 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 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.


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.