A Data-Driven Predictive Model for Speed Control in Automotive Safety Applications

Abstract

The importance of transportation to society and daily life makes safe driving an important responsibility to reduce the occurrence of accidents. Among the accident factors, overspeeding led to a maximum probability of road crashes and death. As the number of vehicles increases, manually monitoring the overspeed offense is laborious and time-consuming. The existing context-aware-based speed control strategy is effective but pricey. Therefore, in this article, a cost-effective, compact, and simple design-oriented speed recommendation and control mechanism is designed to avoid accidents caused by excessive speed. The proposed system leverages the potential of the decision tree regression model to do data analysis and find out the recommended speed for a particular location at a specific time. In the implementation process, the trained regression model is stored in the Heroku web server to obtain the speed. To support the recommendation, model of a new dataset is created from the Gray Spot dataset and stored in the Storage bucket of Firebase (Open Source), which comprises the time, location, weather, and speed attributes. The vehicle user receives speed recommendations from the Heroku server for every location; consequently, the speed controller regulates the speed of the motor through a motor driver’s electrical signal. The proposed decision tree regressor achieved 92.3% prediction accuracy during processing with 30 locations. The efficiency of the model showed that this model is a good assistant to car drivers and an optimal solution to reduce accidents on a larger level.