Think Aloud Protocol and Decision Tree for Driver Behavior Modeling at Roundabouts
Learning from human driver’s strategies for undertaking complex traffic scenarios has the potential to improve decision-making methods for designing ADAS systems, as well as for design self-driving rules for automated vehicles. This paper proposes a human-like decision-making algorithm bu...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
IEEE
2023-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10106245/ |
| _version_ | 1797834843997863936 |
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| author | Borja Monsalve Nourdine Aliane Enrique Puertas Javier Fernandez Andres |
| author_facet | Borja Monsalve Nourdine Aliane Enrique Puertas Javier Fernandez Andres |
| author_sort | Borja Monsalve |
| collection | DOAJ |
| description | Learning from human driver’s strategies for undertaking complex traffic scenarios has the potential to improve decision-making methods for designing ADAS systems, as well as for design self-driving rules for automated vehicles. This paper proposes a human-like decision-making algorithm built up from human drivers experiential naturalistic driving. The approach of this work consists of exploring two main techniques. Firstly, the use of “think aloud protocol” to build a dataset based on naturalistic driving, capturing driver’s intentions. Afterwards, the technique of decision tree is used to generate an algorithm to categorize driving patterns as a function of circumstantial driving parameters. The study is focused on simple roundabouts in presence of other vehicles. The decision tree is translated into algorithmic rules, where the tree pathways are represented as ‘if-then’ clauses, resulting in a model of driver behavior at roundabouts. Finally, the accuracy of the driver behavior model has been assessed, yielding promising results. |
| first_indexed | 2024-04-09T14:43:55Z |
| format | Article |
| id | doaj.art-dc836cef6e0a47eda1ab02cdf8fe8d10 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-04-09T14:43:55Z |
| publishDate | 2023-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-dc836cef6e0a47eda1ab02cdf8fe8d102023-05-02T23:00:42ZengIEEEIEEE Access2169-35362023-01-0111414444145410.1109/ACCESS.2023.326938210106245Think Aloud Protocol and Decision Tree for Driver Behavior Modeling at RoundaboutsBorja Monsalve0https://orcid.org/0000-0002-3597-3334Nourdine Aliane1https://orcid.org/0000-0001-7739-881XEnrique Puertas2https://orcid.org/0000-0002-5115-1226Javier Fernandez Andres3https://orcid.org/0000-0001-8230-7011Science, Computing and Technology Department, Universidad Europea de Madrid, Madrid, Science, SpainIndustrial and Aerospace Engineering Department, Universidad Europea de Madrid, Madrid, SpainScience, Computing and Technology Department, Universidad Europea de Madrid, Madrid, Science, SpainIndustrial and Aerospace Engineering Department, Universidad Europea de Madrid, Madrid, SpainLearning from human driver’s strategies for undertaking complex traffic scenarios has the potential to improve decision-making methods for designing ADAS systems, as well as for design self-driving rules for automated vehicles. This paper proposes a human-like decision-making algorithm built up from human drivers experiential naturalistic driving. The approach of this work consists of exploring two main techniques. Firstly, the use of “think aloud protocol” to build a dataset based on naturalistic driving, capturing driver’s intentions. Afterwards, the technique of decision tree is used to generate an algorithm to categorize driving patterns as a function of circumstantial driving parameters. The study is focused on simple roundabouts in presence of other vehicles. The decision tree is translated into algorithmic rules, where the tree pathways are represented as ‘if-then’ clauses, resulting in a model of driver behavior at roundabouts. Finally, the accuracy of the driver behavior model has been assessed, yielding promising results.https://ieeexplore.ieee.org/document/10106245/ADASdatasetdecision treesdriver behavior modelingroundaboutsthink aloud protocol |
| spellingShingle | Borja Monsalve Nourdine Aliane Enrique Puertas Javier Fernandez Andres Think Aloud Protocol and Decision Tree for Driver Behavior Modeling at Roundabouts IEEE Access ADAS dataset decision trees driver behavior modeling roundabouts think aloud protocol |
| title | Think Aloud Protocol and Decision Tree for Driver Behavior Modeling at Roundabouts |
| title_full | Think Aloud Protocol and Decision Tree for Driver Behavior Modeling at Roundabouts |
| title_fullStr | Think Aloud Protocol and Decision Tree for Driver Behavior Modeling at Roundabouts |
| title_full_unstemmed | Think Aloud Protocol and Decision Tree for Driver Behavior Modeling at Roundabouts |
| title_short | Think Aloud Protocol and Decision Tree for Driver Behavior Modeling at Roundabouts |
| title_sort | think aloud protocol and decision tree for driver behavior modeling at roundabouts |
| topic | ADAS dataset decision trees driver behavior modeling roundabouts think aloud protocol |
| url | https://ieeexplore.ieee.org/document/10106245/ |
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