研究目的
Investigating the method for determining utterance timings when a driving agent provides non-driving-related information to mitigate driver distraction.
研究成果
The proposed method, based on the double articulation analyzer, can determine better utterance timing for a driving agent to provide non-driving-related information, thereby mitigating driver distraction. The experimental results support the effectiveness of the method over baseline methods.
研究不足
The experiments were conducted in an offline manner and in virtual driving situations, which may not fully replicate real-world driving conditions. Additionally, the proposed method does not account for the cognitive load related to each driving word, which could be important for determining more appropriate utterance timings.
1：Experimental Design and Method Selection:
The study employs a double articulation analyzer (DAA), an unsupervised nonparametric Bayesian machine learning method, to detect contextual change points in driving behavior data.
2：Sample Selection and Data Sources:
Driving behavior data were collected from test drivers driving a car for five laps of a circuit in Kariya city, Aichi, Japan.
3：List of Experimental Equipment and Materials:
A desktop PC screen and a steering controller (Logicool Driving Force GT) were used for creating a simple virtual driving environment.
4：Experimental Procedures and Operational Workflow:
Participants were asked to virtually drive the car under three conditions corresponding to three methods for determining utterance timing. The driving agent provided non-driving-related information based on the method's determination.
5：Data Analysis Methods:
The effectiveness of the proposed method was evaluated through subjective evaluation experiments, including questionnaires and quizzes.
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