Simulator-Based Framework to Incorporate Driving Heterogeneity via a Biobehavioral Extension to the Intelligent Driver Model

Mathematical models of car-following, lane changing, and gap acceptance are extensively used to describe behavioral variability when driving. Car-following, in relation to the intelligent driver model (IDM), was the primary component of this research. This research was targeted toward developing a framework to incorporate driver behavioral variability using methodologies adapted from the cognitive and physiological sciences. The main goal was to fuse driving variables such as preferred gap, speed, jerk, and acceleration, together with continuous biobehavioral variables of engagement level, mental workload, situation awareness, and other static driver properties (i.e., age, experience, and driving history), to introduce biobehavioral heterogeneity into the IDM. Incorporating non-conventional tasks during car-following, such as distracted driving, was also a key component of this research. Additionally, assessing the effectiveness of incorporating group-based biobehavioral and driving performance traits rather than individual-level traits into the IDM was also explored. Ninety drivers were recruited to validate the framework using a fixed-base simulator. The scenarios were designed to capture the performance and cognitive parameters when subject to varying task complexities. A biobehavioral extension to the IDM was developed by grouping individual driving performance and behavioral traits. The extended model was successfully validated and found to introduce driving heterogeneity while enhancing the IDM's car-following modeling accuracy for the sample population. The established methods also serve as a key step toward the inclusion of individual/group-level traits that not only consider driving performance but also harvest cognitive and biological processes that directly affect car-following.

Automated summary

Mathematical models are used to describe behavioral variability in driving, with car-following being the primary focus. This research aimed to incorporate driver behavioral variability into the intelligent driver model (IDM) by combining driving variables and continuous biobehavioral variables. Factors such as distracted driving and the use of group-level traits were also explored. The framework was validated using a simulator and found to enhance the IDM's accuracy in car-following.