Prediction of driver visual distraction duration under a human-machine interaction mode with full-touch-screen
A visual distraction duration prediction model considering the driver's distraction style was established to accurately predict the driver's visual distraction duration in the full touch-screen human-computer interaction mode.Based on the multi-speed visual distraction data collected from the real car road test,a set of basic operation units suitable for full touch screen vehicle-mounted devices was constructed based on the Keystroke Level Model(KLM),and a Random Forest Model(RF)for visual distraction duration prediction with the number of each basic operation units and vehicle speed as input features was established.The Self-Organizing Map(SOM)Algorithm was used to cluster drivers into three types of distraction styles:Cautious,normal and aggressive to characterize the differences in drivers'visual distraction characteristics,and the parameters were added to the original model as input features to realize optimization.The results show that the optimized model has the best prediction accuracy.The mean square error,mean absolute error and determination coefficient R2 of the test set are 2.414 8 s2,1.037 1 s and 0.934 5,respectively,which are 30.52%,11.8%and 3.18%higher than those of the original model.The model performance is significantly better than the linear regression model and XGBoost model.The results can be used to assist in-vehicle driver assistance systems in timely warning or implementing intervention to reduce the risk of rear-end collision caused by distraction,and provide guidance for the design of interactive interfaces.
vehicle active safetyvisual distraction durationKeystroke Level Model(KLM)Random Forest(RF)distraction style
NETL
NSTL
万方数据
