Think Aloud Protocol Applied in Naturalistic Driving for Driving Rules Generation
Understanding naturalistic driving in complex scenarios is an important step towards autonomous driving, and several approaches have been adopted for modeling driver’s behaviors. This paper presents the methodology known as “Think Aloud Protocol” to model driving. This methodology is a data-gatherin...
| Autores: | , , , |
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| Tipo de recurso: | artículo |
| Fecha de publicación: | 2020 |
| País: | España |
| Institución: | Universidad Europea (UEM) |
| Repositorio: | ABACUS. Repositorio de Producción Científica |
| Idioma: | inglés |
| OAI Identifier: | oai:abacus.universidadeuropea.com:11268/9576 |
| Acceso en línea: | http://hdl.handle.net/11268/9576 |
| Access Level: | acceso abierto |
| Palabra clave: | Aprendizaje automático Inteligencia artificial Vehículos de motor Autoaprendizaje Vehículo automotor |
| Sumario: | Understanding naturalistic driving in complex scenarios is an important step towards autonomous driving, and several approaches have been adopted for modeling driver’s behaviors. This paper presents the methodology known as “Think Aloud Protocol” to model driving. This methodology is a data-gathering technique in which drivers are asked to verbalize their thoughts as they are driving which are then recorded, and the ensuing analysis of the audios and videos permits to derive driving rules. The goal of this paper is to show how think aloud methodology is applied in the naturalistic driving area, and to demonstrate the validity of the proposed approach to derive driving rules. The paper presents, firstly, the background of the think aloud methodology and then presents the application of this methodology to driving in roundabouts. The general deployment of this methodology consists of several stages: driver preparation, data collection, audio and video processing, generation of coded transcript files, and the generation of driving rules. The main finding of this study is that think aloud protocol can be applied to naturalistic driving, and even some potential limitations as discussed in the paper, the presented methodology is a relatively easy approach to derive driving rules. |
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