Innovating Transportation Research through Driving Simulations

Abstract: BeamNG.tech, a powerful driving simulation platform, has gained increasing attention in the transportation field for its ability to simulate realistic vehicle dynamics and complex traffic scenarios. This systematic review aims to explore the various applications, benefits, and challenges of using BeamNG.tech as a tool for driving simulation in transportation research. Following PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines, A comprehensive search of scholarly databases (Science Direct, Scopus, TRR, TRID) was conducted to identify studies that utilize BeamNG.tech for purposes such as traffic safety analysis, autonomous vehicle testing, and infrastructure evaluation. The review highlights the platform’s versatility in replicating real-world driving conditions. BeamNG.tech offers a versatile and cost-effective platform for driving simulation in transportation research, providing realistic simulations and data collection. The platform's ability to simulate hazardous conditions without risk makes it a valuable tool for safety testing. Additionally, the integration of BeamNG.tech with machine learning algorithms could advance the development of predictive models in traffic management. The study also identifies opportunities for enhancing the realism of simulations through improvements in vehicle modeling and environmental factors. However, challenges exist, including computational requirements and the learning curve for new users. Integration with other transportation simulation tools is also discussed. Recommendations for future research are proposed. This review offers insights into the potential of BeamNG.tech to enhance transportation research and provides recommendations for future studies in this evolving field.