T. Driessen
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The main task during operating an automotive vehicle is driving. Nowadays, distractions form a potential risk of claiming the workload necessary for the driving task. Interacting with the User Interface (UI) of the vehicle can be such a distraction. Predicting the next action on the UI can help decrease the risk of distractions. To predict the next user action, knowledge about the UI interaction behavior is necessary.
In this work, a descriptive analysis is conducted on a large naturlistic dataset which has not been matched in size by other related work in the field of automotive Human Machine Interface (HMI) research. The analysis is conducted to gain insight into the interaction behavior of the drivers. This behavior is related to several driving task conditions such as occupancy, speed and drive mode.
The results of the descriptive analysis are used to implement several prediction models to predict the next user action. Among the models, the Long Short-Term Memory (LSTM)-network achieved a validation accuracy of 87%. This work shows the potential for analysing UI interaction behavior and leveraging this for prediction purposes. It could help in forming a foundation for future adaptive UI work.
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In this work, a descriptive analysis is conducted on a large naturlistic dataset which has not been matched in size by other related work in the field of automotive Human Machine Interface (HMI) research. The analysis is conducted to gain insight into the interaction behavior of the drivers. This behavior is related to several driving task conditions such as occupancy, speed and drive mode.
The results of the descriptive analysis are used to implement several prediction models to predict the next user action. Among the models, the Long Short-Term Memory (LSTM)-network achieved a validation accuracy of 87%. This work shows the potential for analysing UI interaction behavior and leveraging this for prediction purposes. It could help in forming a foundation for future adaptive UI work.
...
The main task during operating an automotive vehicle is driving. Nowadays, distractions form a potential risk of claiming the workload necessary for the driving task. Interacting with the User Interface (UI) of the vehicle can be such a distraction. Predicting the next action on the UI can help decrease the risk of distractions. To predict the next user action, knowledge about the UI interaction behavior is necessary.
In this work, a descriptive analysis is conducted on a large naturlistic dataset which has not been matched in size by other related work in the field of automotive Human Machine Interface (HMI) research. The analysis is conducted to gain insight into the interaction behavior of the drivers. This behavior is related to several driving task conditions such as occupancy, speed and drive mode.
The results of the descriptive analysis are used to implement several prediction models to predict the next user action. Among the models, the Long Short-Term Memory (LSTM)-network achieved a validation accuracy of 87%. This work shows the potential for analysing UI interaction behavior and leveraging this for prediction purposes. It could help in forming a foundation for future adaptive UI work.
In this work, a descriptive analysis is conducted on a large naturlistic dataset which has not been matched in size by other related work in the field of automotive Human Machine Interface (HMI) research. The analysis is conducted to gain insight into the interaction behavior of the drivers. This behavior is related to several driving task conditions such as occupancy, speed and drive mode.
The results of the descriptive analysis are used to implement several prediction models to predict the next user action. Among the models, the Long Short-Term Memory (LSTM)-network achieved a validation accuracy of 87%. This work shows the potential for analysing UI interaction behavior and leveraging this for prediction purposes. It could help in forming a foundation for future adaptive UI work.
An important durability question is whether military off-road vehicles can cope with the fatigue damage from extreme random, complex, and non-stationary loads for the design life of up to 30 years. An accurate life cycle fatigue damage estimation is essential for military off-road vehicles as the lives of military personnel can depend on a durable vehicle in future use. In addition, minimizing costly data acquisition time leads to a faster validation of a prototype vehicle, which in turn means the vehicle is taken into service faster. Hence, this study aimed to optimize existing time-domain load extrapolation methods and compare them in fatigue damage to frequency-domain models specifically for military off-road vehicles while minimizing data acquisition time and costs. An error below 5% between the fatigue damage estimations and the validation is deemed satisfactory.
Two time-domain and six frequency-domain fatigue damage models were applied on a varying fraction of the training dataset in a case study using off-road data from the Dutch Army. From 270 km of unique off-road strain measurements two subsets were created, i.e. a training subset of 90 km and a validation subset of 180 km. These measurements represent the actual use in all operating conditions, yet still lack the load cycles from extreme events which rarely occur. All eight models were optimized to fractions of the training set and compared to the fatigue damage of the full training set and validation data.
None of the models performed satisfactory( i.e.<5% error) if less than 36 km or 40% of the training dataset is used. Fatigue damage was underestimated up to 30% compared to the full training dataset. Above 36 km or 40% of the training set the error in fatigue damage estimations stabilizes indicating the training data contains sufficient information regarding rare and exteme loads. Yet, only one time-domain and one frequency-domain model achieved an error below 5% between the estimation on the training data and the validation data.
This shows that for a tail heavy off-road load distribution in the time-domain or wideband PSD in the frequency-domain only a selection of models provide accurate results when the measured signal contains sufficient extreme loads. The results give the Dutch Army a fatigue damage estimation method which provides good accuracy whilst minimizing the required data acquisition. However, it is recommended to expand the model to validate the results for other types of military off-road vehicles, a broad range of material parameters, and user profiles. ...
Two time-domain and six frequency-domain fatigue damage models were applied on a varying fraction of the training dataset in a case study using off-road data from the Dutch Army. From 270 km of unique off-road strain measurements two subsets were created, i.e. a training subset of 90 km and a validation subset of 180 km. These measurements represent the actual use in all operating conditions, yet still lack the load cycles from extreme events which rarely occur. All eight models were optimized to fractions of the training set and compared to the fatigue damage of the full training set and validation data.
None of the models performed satisfactory( i.e.<5% error) if less than 36 km or 40% of the training dataset is used. Fatigue damage was underestimated up to 30% compared to the full training dataset. Above 36 km or 40% of the training set the error in fatigue damage estimations stabilizes indicating the training data contains sufficient information regarding rare and exteme loads. Yet, only one time-domain and one frequency-domain model achieved an error below 5% between the estimation on the training data and the validation data.
This shows that for a tail heavy off-road load distribution in the time-domain or wideband PSD in the frequency-domain only a selection of models provide accurate results when the measured signal contains sufficient extreme loads. The results give the Dutch Army a fatigue damage estimation method which provides good accuracy whilst minimizing the required data acquisition. However, it is recommended to expand the model to validate the results for other types of military off-road vehicles, a broad range of material parameters, and user profiles. ...
An important durability question is whether military off-road vehicles can cope with the fatigue damage from extreme random, complex, and non-stationary loads for the design life of up to 30 years. An accurate life cycle fatigue damage estimation is essential for military off-road vehicles as the lives of military personnel can depend on a durable vehicle in future use. In addition, minimizing costly data acquisition time leads to a faster validation of a prototype vehicle, which in turn means the vehicle is taken into service faster. Hence, this study aimed to optimize existing time-domain load extrapolation methods and compare them in fatigue damage to frequency-domain models specifically for military off-road vehicles while minimizing data acquisition time and costs. An error below 5% between the fatigue damage estimations and the validation is deemed satisfactory.
Two time-domain and six frequency-domain fatigue damage models were applied on a varying fraction of the training dataset in a case study using off-road data from the Dutch Army. From 270 km of unique off-road strain measurements two subsets were created, i.e. a training subset of 90 km and a validation subset of 180 km. These measurements represent the actual use in all operating conditions, yet still lack the load cycles from extreme events which rarely occur. All eight models were optimized to fractions of the training set and compared to the fatigue damage of the full training set and validation data.
None of the models performed satisfactory( i.e.<5% error) if less than 36 km or 40% of the training dataset is used. Fatigue damage was underestimated up to 30% compared to the full training dataset. Above 36 km or 40% of the training set the error in fatigue damage estimations stabilizes indicating the training data contains sufficient information regarding rare and exteme loads. Yet, only one time-domain and one frequency-domain model achieved an error below 5% between the estimation on the training data and the validation data.
This shows that for a tail heavy off-road load distribution in the time-domain or wideband PSD in the frequency-domain only a selection of models provide accurate results when the measured signal contains sufficient extreme loads. The results give the Dutch Army a fatigue damage estimation method which provides good accuracy whilst minimizing the required data acquisition. However, it is recommended to expand the model to validate the results for other types of military off-road vehicles, a broad range of material parameters, and user profiles.
Two time-domain and six frequency-domain fatigue damage models were applied on a varying fraction of the training dataset in a case study using off-road data from the Dutch Army. From 270 km of unique off-road strain measurements two subsets were created, i.e. a training subset of 90 km and a validation subset of 180 km. These measurements represent the actual use in all operating conditions, yet still lack the load cycles from extreme events which rarely occur. All eight models were optimized to fractions of the training set and compared to the fatigue damage of the full training set and validation data.
None of the models performed satisfactory( i.e.<5% error) if less than 36 km or 40% of the training dataset is used. Fatigue damage was underestimated up to 30% compared to the full training dataset. Above 36 km or 40% of the training set the error in fatigue damage estimations stabilizes indicating the training data contains sufficient information regarding rare and exteme loads. Yet, only one time-domain and one frequency-domain model achieved an error below 5% between the estimation on the training data and the validation data.
This shows that for a tail heavy off-road load distribution in the time-domain or wideband PSD in the frequency-domain only a selection of models provide accurate results when the measured signal contains sufficient extreme loads. The results give the Dutch Army a fatigue damage estimation method which provides good accuracy whilst minimizing the required data acquisition. However, it is recommended to expand the model to validate the results for other types of military off-road vehicles, a broad range of material parameters, and user profiles.