FDSDS: A Fuzzy-Based Driver Stress Detection System for VANETs Considering Interval Type-2 Fuzzy Logic and Its Performance Evaluation

Vehicular Ad Hoc Networks (VANETs) enable Vehicle-to-Vehicle (V2V) and Vehicle-to-Infrastructure (V2I) communications for enhancing road safety. However, reliable driver stress assessment remains challenging due to noisy sensing, inter-driver variability, and context dynamics. This paper proposes a Fuzzy-based Driver Stress Detection System (FDSDS) that employs an Interval Type-2 Fuzzy Logic System (IT2FLS) to model uncertainty. The FDSDS considers four complementary inputs—Heart Rate Variability (HRV), Galvanic Skin Response (GSR), Steering Angle Variation (SAV), and Traffic Density (TD)—to estimate Driver Stress Level (DSL). Extensive simulations (14,641 test points) show monotonic associations between DSL and the inputs, which reveal that physiological indicators dominate average influence (finite-difference sensitivity: GSR 0.357, SAV 0.239, TD 0.239, HRV 0.235). Under severe physiological conditions (HRV = 0.1, GSR = 0.9), the system consistently outputs high stress (mean DSL = 0.813; range 0.622–0.958), while favorable physiological conditions (HRV = 0.9, GSR = 0.1) yield low stress even in challenging traffic (range 0.044–0.512). The IT2FLS uncertainty bands widen for intermediate conditions, aligning with the inherent ambiguity of moderate stress states. These results indicate that combining physiological, behavioral, and environmental factors with IT2FLS yields interpreted, uncertainty-aware stress estimates suitable for real-time VANET applications.