Background: The detection of driver fatigue as a cause of sleepiness is a key technology capable of preventing fatal accidents. This research uses a fatigue-related sleepiness detection algorithm based on the analysis of the pulse rate variability generated by the heartbeat and validates the proposed method by comparing it with an objective indicator of sleepiness (PERCLOS). Methods: changes in alert conditions affect the autonomic nervous system (ANS) and therefore heart rate variability (HRV), modulated in the form of a wave and monitored to detect long-term changes in the driver’s condition using real-time control. Results: the performance of the algorithm was evaluated through an experiment carried out in a road vehicle. In this experiment, data was recorded by three participants during different driving sessions and their conditions of fatigue and sleepiness were documented on both a subjective and objective basis. The validation of the results through PERCLOS showed a 63% adherence to the experimental findings. Conclusions: the present study confirms the possibility of continuously monitoring the driver’s status through the detection of the activation/deactivation states of the ANS based on HRV. The proposed method can help prevent accidents caused by drowsiness while driving.
The velocity change Δ V of a vehicle subject to a collision, widely recognized as an efficient crash severity indicator, is a typical ‘a posteriori’ parameter, not generally known until the crash phase has been reconstructed. Δ V is the result of a combination of factors, regarding the impact velocities of the colliding vehicles and the geometry of the impact (as eccentricity, etc.): for this reason, its value alone gives no clear indications on the actions which can be undertaken to reduce crash severity. This feature is particularly critical in some application fields, for example, in case of advanced driver assistance systems assessment in different accident scenarios. This work proposes the disaggregation of Δ V into two different ‘a priori’ parameters to assess crash severity of an impact before its occurrence: the crash momentum index, representing the impact configuration, and the closing velocity projected along the principal direction of force ( Vr_pdof), as an index of the kinetic energy exchanged between the two vehicles. It is preliminarily shown how the proposed parameters can be calculated using established procedures – as momentum-based analysis – in a predictive (‘a priori’) approach. It is also evidenced how crash momentum index, Vr_pdof and the velocity change Δ V are in relation. To illustrate the procedure by means of examples, binary logistic regression on accident data is applied to correlate crash momentum index and Δ V to injury risk at Maximum Abbreviated Injury Scale level higher than 2. The use of crash momentum index as an additional severity index allows an improved correlation with injury risk, for the dataset used, in case of front and near side impacts. The use of the plane Vr_pdof– crash momentum index, on which curves at constant injury risk are drawn, provides clear indications on the possible strategies to reduce injury risk, as shown by generic examples to which the predictive procedure is applied.
DRIVE IN 2 is an automotive research project within the field of Intelligent Transportation Systems, especially Advanced Driving Assistance Systems (ADAS). The project originates from the idea that the development of new ADAS and evaluation of their effect have to take drivers into account, as well as their behavior while driving: the benefits of adopting new in-vehicle technologies depend also on their adoption and usage by drivers. To this aim, the project develops a Driver-In-the-Loop framework to position observation of the drivers at the center of the research activities. Observations are carried out by coupling different research tools, namely instrumented vehicle and driving simulators. The premise and methodological framework of the research project are presented and discussed. Some preliminary activities with particular reference to validating the driving simulation environment are also described.
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