Database of Simulated Driver Behaviors Using the SUMO Simulator

Classifying the driving styles is of particular interest for enhancing road safety in smart cities. The vehicle can assist the driver by providing advice to increase awareness of potential dangers. Accordingly, dissuasive measures, such as adjusting insurance costs, can be implemented. The service is called Pay-As-You-Drive insurance (PAYD), and to address it, the paper introduces a method for constructing a database of simulated driver behaviors using the Simulation of Urban MObility  Simulation of Urban MObility (SUMO) simulator. Three levels of driver behavior (slow, normal, and dangerous) are generated using the Intelligent Driver Model car-following, with parameters adjusted accordingly. The simulation takes place on the Miami city map, extracted from a real-world map, encompassing various road types and traffic signs. The control interface ‘TraCI' is employed to collect data from the simulated vehicles and to trigger alerts based on violations committed by the drivers. These alerts are then used to train four machine learning (ML) models, for labeling the driving style. The four ML models are Gradient Boosted Decision Trees, K-Nearest Neighbors, Multi-layer Perceptron, and Support Vector Machines. The paper demonstrates the feasibility and accuracy of the proposed AlertDang Driver Profiling method, providing the code and dataset on GitHub. Additionally, it discusses the advantages and limitations of the simulation-based approach and suggests potential future directions.