Real-Time Connected Car Services

In recent years, the patterns ofconnected car are tied in with giving driversmore answers for making the journeyconsistent. Vehicles today are outfitted withhigh innovation highlights and in-vehicleavailability. The Integrated Connected VehicleServices is produced to convey an incorporateddriving experience to all vehicle owners, tomake a communication stage for drivers toimpart and share data between vehicles. Thesystem allows to discover nearby vehicles insiderange, giving the driver early notice caution ofcrisis vehicles inside certain range. Moreover,the system likewise enables the driver to sharebasic data which later plots into the maps foralternate drivers to view and plan the journey.The information of transmission between thevehicles are incorporated through firebase cloudservices. Firebase is known as an effective clouddatabase and ready to screen the applicationdevelopment

A Comparability Study on Driver Fatigue Using C#, C++ and Python

Accidents on road are very commonthese days. Most of them are caused by driverfatigueness. Some common causes and symptomshave been identified. One of the main solutionto detect driver fatigue is by analyzing the facialfeatures of the drivers. This paper discusses aboutthe facial features that can be used to detect driverfatigue. Further examples on existing vehiclesafety technology is also discussed. Primarily, thiswork emphasizes on the study of three differentprogramming languages and its compatibilitywhich works best to be integrated with theproposed hardware. Based on the study, theresult is discussed and the suitable programminglanguage is suggested

Comparative Study of Machine Learning Algorithms in Classifying HRV for the Driver’s Physiological Condition

Heart Rate Variability (HRV) may be used as a psychological marker to assess drivers’ states from physiological signals such as an electrocardiogram (ECG), electroencephalogram (EEG), and photoplethysmography (PPG). This paper reviews HRV acquisition methods from drivers and machine learning approaches for driver cardiac health based on HRV classification. The study examines four publicly available ECG datasets and analyzes their HRV features, including time domain, frequency domain, short-term measures, and a combination of time and frequency domains. Eight machine learning classifiers, namely K-Nearest Neighbor, Decision Tree, Naive Bayes, Linear Discriminant Analysis, Support Vector Machine, Random Forest, Gradient Boost, and Adaboost, were used to determine whether the driver's state is normal or abnormal. The results show that K-Nearest Neighbor and Decision Tree classifiers had the highest accuracy at 92.86%. The study concludes by assessing the performance of machine learning algorithms in classifying HRV for the driver's physiological condition using the Man-Whitney U test in terms of accuracy and F1 score. We have statistical evidence to support that the prediction quality is different when HRV analysis applies these three sets: (i) time domain measures or frequency domain measures; (ii) frequency domain measures or short-term measures; and (iii) combining time and frequency domains or only frequency domains