Mikael Johansson
Mikael is researching about the users’ mental models and understanding of automated vehicles. Mental models are a cognitive representation that users have of the system, in order to solve problems, explain behavior and predict consequences of an interaction. The purpose of Mikael’s research is to be able to create safer and more user centered systems by better understanding what affects the users’ mental models and how it changes over time.
Showing 29 publications
Understanding Drivers' Interaction With Traffic Environments - A Traffic Semantic Approach
Unveiling the Complexity of Car Ride Comfort: A Holistic Model
Park & Ride Revisited. A commuter perspective on Park & Ride today and tomorrow.
Drivers’ Performance in Non-critical Take-Overs From an Automated Driving System—An On-Road Study
Action-Meaning Networks - A Novel Methodology to Identify Unsafe Use of Driving Automation
Making Sense of Making Sense - Exploring users’ understanding of automated vehicles during use
The Devil is in the Details - Trust Development During Initial Usage of an Automated Vehicle
The decline of user experience in transition from automated driving to manual driving
Levels of what? Investigating drivers' understanding of different levels of automation in vehicles
To Drive or Not to Drive – When Users Prefer to Use Automated Driving Functions
Capable and considerate: Exploring the assigned attributes of an automated vehicle
Demystifying the Black Box - Exploring How Users Make Sense of Fully Automated Vehicles
Exploring automated vehicle driving styles as a source of trust information
Designing Multi-Modal Interaction – A Basic Operations Approach
Understanding the Users’ Understanding of Automated Vehicles
Creating Appropriate Trust in Automated Vehicle Systems: A Framework for HMI Design
Understanding trust in an AV context: A mixed method approach
Creating trust in automated vehicles using a HMI-based framework
Creating Appropriate Trust for Autonomous Vehicle Systems: A Framework for HMI Design
To See or Not To See – The Effect of Object Recognition on Users’ Trust for "Automated Vehicles"
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Showing 6 research projects
SEAMLESS: Systematic Evaluations and Assessments of MaaS – Leading towards Sustainable Solutions
Users' understanding of autonomou vehicles - DAUT
HMI design for automated vehicles - HATric
AIMMIT - Automotive Integration of MultiModal Interaction Technologies