Cognitive behavior model replicates road user response timing in naturalistic rear-end traffic conflicts

Driver response timing in critical traffic situations has traditionally been studied using controlled experiments with clearly defined stimuli. However, these approaches often fail to capture the complexity of real-world scenarios. We present a validation study of the driveBOT cognitive architecture, comparing its response timing to human driver responses from the Strategic Highway Research Program 2 (SHRP2) naturalistic driving study. While driveBOT implements general principles of human behavior modeling, we evaluate its performance specifically in rear-end conflicts against the naturalistic data.Our statistical validation demonstrates that driveBOT successfully reproduces human response characteristics in these scenarios without requiring explicit stimulus definitions. The model generates responses that closely match the naturalistic driving data in the relationship between situation kinematics and driver response timing. These results suggest that driveBOT's cognitive architecture captures fundamental aspects of human driving behavior in critical situations.The validated driveBOT model provides a practical tool for simulating driver response timing, with direct applications in vehicle safety system development and risk assessment. This work demonstrates how computational cognitive architectures can bridge the gap between controlled experimental findings and naturalistic driving behavior.