Editorial for Special Issue: Test and Evaluation Methods for Human-Machine Interfaces of Automated Vehicles

Today, OEMs and suppliers can rely on commonly agreed and standardized test and evaluation methods for in-vehicle human–machine interfaces (HMIs). These have traditionally focused on the context of manually driven vehicles and put the evaluation of minimizing distraction effects and enhancing usability at their core (e.g., AAM guidelines or NHTSA visual-manual distraction guidelines). However, advances in automated driving systems (ADS) have already begun to change the driver’s role from actively driving the vehicle to monitoring the driving situation and being ready to intervene in partially automated driving (SAE L2). Higher levels of vehicle automation will likely only require the driver to act as a fallback ready user in case of system limits and malfunctions (SAE L3) or could even act without any fallback within their operational design domain (SAE L4). During the same trip, different levels of automation might be available to the driver (e.g., L2 in urban environments, L3 on highways). These developments require new test and evaluation methods for ADS, as available test methods cannot be easily transferred and adapted. The shift towards higher levels of vehicle automation has also moved the discussion towards the interaction between automated and non-automated road users using exterior HMIs. This Special Issue includes theoretical papers a well as empirical studies that deal with these new challenges by proposing new and innovative test methods in the evaluation of ADS HMIs in different areas.

[1]  Mascha C. van der Voort,et al.  Supporting Drivers of Partially Automated Cars through an Adaptive Digital In-Car Tutor , 2020, Inf..

[2]  Klaus Bengler,et al.  Effects of Marking Automated Vehicles on Human Drivers on Highways , 2020, Inf..

[3]  Nadine Rauh,et al.  Methodological Approach towards Evaluating the Effects of Non-Driving Related Tasks during Partially Automated Driving , 2020, Inf..

[4]  Alexandra Neukum,et al.  Standardized Test Procedure for External Human-Machine Interfaces of Automated Vehicles , 2020, Inf..

[5]  Fabio Tango,et al.  Human-Vehicle Integration in the Code of Practice for Automated Driving , 2020, Inf..

[6]  Nadja Schömig,et al.  Checklist for Expert Evaluation of HMIs of Automated Vehicles - Discussions on Its Value and Adaptions of the Method within an Expert Workshop , 2020, Inf..

[7]  Klaus Bengler,et al.  Comparison of Methods to Evaluate the Influence of an Automated Vehicle's Driving Behavior on Pedestrians: Wizard of Oz, Virtual Reality, and Video , 2020, Inf..

[8]  Christian Purucker,et al.  Sleep Inertia Countermeasures in Automated Driving: A Concept of Cognitive Stimulation , 2020, Inf..

[9]  Kathrin Zeeb,et al.  The Impact of Situational Complexity and Familiarity on Takeover Quality in Uncritical Highly Automated Driving Scenarios , 2020, Inf..

[10]  Heiko Wersing,et al.  Feeling Uncertain - Effects of a Vibrotactile Belt that Communicates Vehicle Sensor Uncertainty , 2020, Inf..

[11]  Lena Rittger,et al.  Methodological Considerations Concerning Motion Sickness Investigations during Automated Driving , 2020, Inf..

[12]  Klaus Bengler,et al.  Usability Evaluation - Advances in Experimental Design in the Context of Automated Driving Human-Machine Interfaces , 2020, Inf..

[13]  Natasha Merat,et al.  Measuring Drivers' Physiological Response to Different Vehicle Controllers in Highly Automated Driving (HAD): Opportunities for Establishing Real-Time Values of Driver Discomfort , 2020, Inf..

[14]  Y. B. Eisma,et al.  External Human-Machine Interfaces: The Effect of Display Location on Crossing Intentions and Eye Movements , 2019, Inf..

[15]  Barbara Metz,et al.  Repeated Usage of an L3 Motorway Chauffeur: Change of Evaluation and Usage , 2020, Inf..

[16]  Martin Baumann,et al.  Efficient Paradigm to Measure Street-Crossing Onset Time of Pedestrians in Video-Based Interactions with Vehicles , 2020, Inf..

[17]  Sebastian Hergeth,et al.  Engagement in Non-Driving Related Tasks as a Non-Intrusive Measure for Mode Awareness: A Simulator Study , 2020, Inf..

[18]  Klaus Bengler,et al.  Multi-Vehicle Simulation in Urban Automated Driving: Technical Implementation and Added Benefit , 2020, Inf..

[19]  Florian Raisch,et al.  Mode Awareness and Automated Driving - What Is It and How Can It Be Measured? , 2020, Inf..

[20]  Klaus Bengler,et al.  How Much Space Is Required? Effect of Distance, Content, and Color on External Human-Machine Interface Size , 2020, Inf..

[21]  Riender Happee,et al.  How Do eHMIs Affect Pedestrians' Crossing Behavior? A Study Using a Head-Mounted Display Combined with a Motion Suit , 2019, Inf..