Inferring Driver Behavior Profiles Using Digital Twins in Simulated Environments

Understanding driver behavior is crucial for enhancing road safety and improving fuel efficiency. However, collecting data on these behaviors in real-world settings is challenging due to vehicle complexity and high instrumentation costs. Driving simulators offer a viable alternative by creating environments that replicate real-life situations. This study explores the use of digital twins in simulated environments, specifically in Euro Truck Simulator 2 (ETS2), to infer driver behavior using virtual sensors. A case study was conducted where a driver simulated routes under two driving conditions: cautious and aggressive. The telemetry data collected during these simulations were analyzed to identify behavioral patterns and assess fuel consumption efficiency. The results demonstrated that digital twins enable real-time capture of driver behavior information, revealing significant differences between driving styles. Analysis of the accumulated data and the radar area soft sensor indicated that cautious driving practices are associated with greater fuel efficiency.