Currently, global transportation is increasingly focused on autonomous vehicular systems. However, training traffic systems to rely fully on autonomy remains challenging, as evidenced by numerous Tesla Autopilot accidents. Motivated by this, our study addresses a critical aspect: the driver’s sensitivity in autonomous vehicles, which plays a key role in enhancing their performance. We developed a novel driver sensitivity function responding to real-time traffic dynamics, formulated in two parts: (i) sensitivity depending on the headway gap between the focal and preceding vehicle, and (ii) sensitivity varying with the velocity difference, quantified using the preceding vehicle’s taillight signals.

For the first part, we applied a straightforward formulation inspired by the Optimal Velocity model. Driver awareness increases as the headway shrinks to prevent collisions and decreases with increasing headway due to reduced collision risk. In the second part, sensitivity is modeled using the taillight phenomenon, activated by vehicle acceleration or deceleration. Here, sensitivity escalates with both positive velocity differences, when the preceding vehicle is faster, and negative differences, when the focal vehicle is faster, helping to fill gaps and prevent collisions.

We performed a linear analysis using neutral stability theory, revealing a critical stability line distinct from conventional traffic models. A nonlinear analysis of the headway-based sensitivity produced flow patterns describable by the mKdV wave equation. Finally, numerical simulations were conducted to visualize the internal flow field structures, validating the model’s performance under realistic traffic conditions.

This study demonstrates that incorporating driver sensitivity through velocity and headway dynamics provides a robust framework for improving autonomous vehicle operation, enhancing safety and traffic stability while offering new insights for future autonomous traffic system designs.