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.

No abstract

No abstract

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 selfdriving 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' rules. Finally, the accuracy of the driver behavior model has been assessed, yielding promising results.