Integrating Human Panic Factor in Intelligent Driver Model

• Published in: 2020 3rd International Conference on Advancements in Computational Sciences (ICACS) pp. 1 - 6
• Main Authors: Tanveer, Hifsa, Mubasher, Mian Muhammad, Jaffry, Syed Waqar
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01-02-2020
• Subjects: acceleration; car-following; driving behavior; human factor; panic; speed choice
• DOI: 10.1109/ICACS47775.2020.9055947

This study aims to explore the effects of human panic factor on drivers' driving behavior. Most of the car following models focus on idealistic situations aiming for perfection, traffic psychology, however, suggests that emotions do play a significant role in drivers' behavior which in result effect their driving and decision making. Therefore, it is necessary to incorporate human factors in car following models for better realistic results in driving situations where external task demand increases (for example, poor weather conditions like fog, or making up to a meeting in time). Despite the fact that car following models have sublime appreciation in literature, none of them has focused on incorporating human panic factor in these models. Although some work is being done on understanding panic factor in drivers which helps us to understand their driving behaviors and effect on acceleration under panic situations, but this work is limited to statistical approach. This study is intended to fill this void by reviewing literature and making latest advancements by integrating human panic factor in Intelligent Driver Model (IDM). We attempted to integrate human panic factor in IDM, and simulation-based results verified our assumptions for the enhanced version of IDM. The enhanced version of model namely P-IDM models the acceleration behavior of drivers under panic condition, and reproduces acceleration as intended.