Test and Evaluation Methods for Human-Machine Interfaces of Automated Vehicles II
Materialtyp:
ArtikelUtgivningsinformation: MDPI - Multidisciplinary Digital Publishing Institute 2025Beskrivning: 1 electronic resource (182 p.)Innehållstyp: - text
- computer
- online resource
- 9783725846375
- 9783725846382
- Technology, Engineering, Agriculture, Industrial processes
- Technology: general issues
- History of engineering and technology
- HMI
- Level 2 automation
- Light-band displays
- Malaysian driver
- Quality Automated Driving
- acceleration
- adaptive automation
- assessment method
- attention
- automated driving
- automated vehicles
- automation
- benchmarking
- cognitive architectures
- concepts for applying learnability engineering (CALE)
- decision making
- design of communication signals for CAV
- driver monitoring
- driving
- driving performance
- driving style
- evaluation
- external human–machine interface
- eye-tracking
- framework
- gaze behaviour
- guidelines for eHMI
- highly automated driving
- human machine interfaces
- human-machine interface
- human–automation interaction
- human–machine interaction
- human–machine interface
- interaction of automated vehicles and other road users
- interaction with automated vehicles
- learnability in automated driving (LiAD)
- learning effects
- on-road observation
- partially automated driving
- self-assessment
- situation awareness
- system operation
- tablet application
- takeover
- transition of control
- transparency
- trust in automation
- usability
- user experience
Open Access Unrestricted online access star
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.
Creative Commons Licence cc by cc https://creativecommons.org/licenses/by/4.0/
eng
Freely available e-book