Driver’s black box: a system for driver risk assessment using machine learning and fuzzy logic

Risky driving behaviors can cause accidents, which may result in major material and moral damages. Due to the increase in road accidents, it has become an important issue to identify risky driving behaviors and reward people who drive safely. With the development of technology, it is now possible to model driving behavior through advanced sensors integrated into embedded systems. In this study, we modeled four major risky driving behaviors and created driver profiles using data obtained from accelerometer and gyroscope sensors and applying widely used machine learning algorithms in behavior analysis, including the C4.5 Decision Tree, Random Forest, Artificial Neural Network, Support-Vector Machine, K-Nearest Neighbor, Naive Bayes, and K-Star algorithms. Risky driving behaviors and their risk levels were evaluated in accordance with the expert opinions of traffic officers, and driver risk was modeled using the fuzzy logic method. The applied machine learning algorithms were compared using common validation metrics such as accuracy, f-measure, precision, and recall. In our experiments, the K-Star algorithm was the most successful algorithm, with 100% accuracy. As a result, a highly accurate, low-cost system which acts as the driver’s black box was developed. The system can be integrated into vehicles and it can record the driver’s behaviors and identify the risky ones. It can also open up new horizons for insurance companies to utilize usage-based policies, in which customers who drive safely are rewarded with lower car insurance premiums, encouraging others to do the same.