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Test and Evaluation Methods for Human-Machine Interfaces of Automated Vehicles II

With the introduction of automated driving systems (ADSs) and advanced driver assistance systems, the communication of the driver’s responsibilities and the AD’s capabilities has become an important topic in recent years. For example, partially automated driving (SAE L2) systems need to be able to c...

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I tiakina i:
Hōputu: Online
Reo:Ingarihi
I whakaputaina: MDPI - Multidisciplinary Digital Publishing Institute 2025
Ngā marau:
driving
eye-tracking
attention
takeover
highly automated driving
automation
situation awareness
adaptive automation
human–automation interaction
cognitive architectures
decision making
user studies
automated driving
human-machine interface
transparency
assessment method
evaluation
HMI
tablet application
partially automated driving
Level 2 automation
driver monitoring
system operation
driving performance
automated vehicles
transition of control
trust in automation
human machine interfaces
gaze behaviour
Light-band displays
interaction with automated vehicles
external human–machine interface
design of communication signals for CAV
interaction of automated vehicles and other road users
guidelines for eHMI
learnability in automated driving (LiAD)
learning effects
concepts for applying learnability engineering (CALE)
Quality Automated Driving
framework
benchmarking
human–machine interaction
human–machine interface
usability
user experience
driving style
self-assessment
on-road observation
acceleration
Malaysian driver
thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TB Technology: general issues::TBX History of engineering and technology
Urunga tuihono:ONIX_20250812T110751_9783725846375_574
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Whakarāpopototanga:With the introduction of automated driving systems (ADSs) and advanced driver assistance systems, the communication of the driver’s responsibilities and the AD’s capabilities has become an important topic in recent years. For example, partially automated driving (SAE L2) systems need to be able to communicate that the driver is still fully responsible for driving safety, whereas higher levels of vehicle automation need to be able to communicate that the driver has to act as a fallback-ready user in case of system limits and malfunctions (SAE L3). During the same trip, different levels of automation might be available to the driver, making it even more crucial that the driving mode is efficiently displayed. These developments require new, standardized tests and evaluating methods for in-vehicle Human–Machine Interfaces (HMIs). This Special Issue includes theoretical papers as well as empirical studies that propose new and innovative test methods in the evaluation of ADS HMIs.

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