CogMod: Simulating Human Information Processing Limitation While Driving
2022 IEEE INTELLIGENT VEHICLES SYMPOSIUM (IV)(2022)
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).
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Key words
cognitive processing time,CogMod,human driver behavior model,QN-MHP,human cognition,human information processing limitation,cognitive architectures,queueing network-model human processor,adaptive control of thought - rational,ACT-R,automated vehicles,AVs
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