2023 Volume 17 Issue 5 Pages JAMDSM0057
This study focused on demonstrating how to design riding behavior-based collision avoidance systems (RB-CAWS). Statistical and machine learning algorithms were adopted and analyzed previously collected data from the riding simulator-based experiments. The experiments involved 23 participants who completed a 20 km course under four driving conditions, including high and low arousal and positive and negative valence emotional states. Our analysis started by implementing structural equation modeling (SEM) to examine the relationship between riding behaviors measured by speed variability and lateral instability and how these factors influence riding performance. Results revealed that lateral instability strongly affects riding performance. The average lateral deviation from the center of the lane (DCavg) and rolling entropy (RE) were clarified as more influential riding behaviors. Next, using the decision tree (DT) algorithm, significant thresholds for DCavg and RE were determined as 0.302m and 0.019, respectively. The neural network (NN) algorithm was also adopted and defined 0.5 as the optimal threshold value at a combined risk level. We compared the models based on their collision prediction accuracy and found that the DT model performed better with an accuracy rate of 80% compared to the NN model's accuracy rate of 76%. Using the identified thresholds, we highlighted potential warning systems and provided a framework for developing an RB-CAWS that integrates SEM and DT analysis techniques. This study can benefit researchers of motorcyclists' riding behavior and designers of advanced rider assistance systems (ARAS).
