This chapter presents a benchmark problem for the “indirect” control of human-driven vehicles. In other words, it deviates from the typical challenges tackled by autonomous driving systems and instead explores control stimuli for human drivers, aimed at evoking their unconscious responses as they manually operate their vehicles. Inside the highway tunnel, an array of lights is strategically positioned to provide drivers with a visual stimulus known as “pace-making-lights” (PMLs), which guides the manual vehicle operation. The central control challenge revolves around designing the dynamics of the PMLs to improve the tracking performance of the human-driven vehicle to reach the desired speed. This benchmark problem includes ten driver models constructed based on data obtained from human subject experiments. When integrated with the vehicle dynamics model, these driver models simulate how acceleration and deceleration respond to the behavior of the PMLs and preceding vehicles. The incorporation of these ten human-driven vehicle models in the control benchmark enables the evaluation of the performance of advanced control methods such as model predictive control, robust control, and more.