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...
Guardado en:
| Formato: | Online |
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| Lenguaje: | inglés |
| Publicado: |
MDPI - Multidisciplinary Digital Publishing Institute
2025
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| Materias: | |
| Acceso en línea: | ONIX_20250812T110751_9783725846375_574 |
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| _version_ | 1869516614200721408 |
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| collection | Directory of Open Access Books |
| description | 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. |
| format | Online |
| id | doab-20.500.12854ir-165819 |
| institution | Directory of Open Access Books |
| language | eng |
| publishDate | 2025 |
| publishDateRange | 2025 |
| publishDateSort | 2025 |
| publisher | MDPI - Multidisciplinary Digital Publishing Institute |
| publisherStr | MDPI - Multidisciplinary Digital Publishing Institute |
| record_format | ojs |
| spelling | doab-20.500.12854ir-1658192025-08-12T10:19:50Z Test and Evaluation Methods for Human-Machine Interfaces of Automated Vehicles II Naujoks, Frederik Forster, Yannick Keinath, Andreas Schömig, Nadja Hergeth, Sebastian Wiedemann, Katharina 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 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. 2025-08-12T10:19:48Z 2025-08-12T10:19:48Z 2025 book ONIX_20250812T110751_9783725846375_574 9783725846375 9783725846382 https://directory.doabooks.org/handle/20.500.12854/165819 eng image/jpeg Attribution 4.0 International https://mdpi.com/books https://mdpi.com/books/pdfview/book/11237 MDPI - Multidisciplinary Digital Publishing Institute 10.3390/books978-3-7258-4638-2 10.3390/books978-3-7258-4638-2 46cabcaa-dd94-4bfe-87b4-55023c1b36d0 9783725846375 9783725846382 182 open access |
| spellingShingle | 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 Test and Evaluation Methods for Human-Machine Interfaces of Automated Vehicles II |
| title | Test and Evaluation Methods for Human-Machine Interfaces of Automated Vehicles II |
| title_full | Test and Evaluation Methods for Human-Machine Interfaces of Automated Vehicles II |
| title_fullStr | Test and Evaluation Methods for Human-Machine Interfaces of Automated Vehicles II |
| title_full_unstemmed | Test and Evaluation Methods for Human-Machine Interfaces of Automated Vehicles II |
| title_short | Test and Evaluation Methods for Human-Machine Interfaces of Automated Vehicles II |
| title_sort | test and evaluation methods for human machine interfaces of automated vehicles ii |
| topic | 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 |
| topic_facet | 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 |
| url | ONIX_20250812T110751_9783725846375_574 |