CogMod: Simulating Human Information Processing Limitation While Driving

Abdul Jawad, Jim Whitehead

Abstract: We develop a human driver behavior model (CogMod) based on two complementary cognitive architectures; Queueing Network-Model Human Processor (QN-MHP) and Adaptive Control of Thought - Rational (ACT-R), to represent human cognition while driving. The proposed model can integrate different task-specific analytical driver models under a similar cognitive procedure. The model can simulate variable cognitive processing ability, resulting in different stopping distances in a scenario where the front vehicle brakes sharply when it enters a trigger distance. We evaluate the model based on the distribution of stopping distance with varying cognitive processing time. This approach is useful for modeling non-ego vehicles in scenario-based testing of automated vehicles (AVs).