  Following a leading vehicle is a daily but challenging task because it
requires adapting to various traffic conditions and the leading vehicle's
behaviors. However, the question `Does the following vehicle always actively
react to the leading vehicle?' remains open. To seek the answer, we propose a
novel metric to quantify the interaction intensity within the car-following
pairs. The quantified interaction intensity enables us to recognize interactive
and non-interactive car-following scenarios and derive corresponding policies
for each scenario. Then, we develop an interaction-aware switching control
framework with interactive and non-interactive policies, achieving a
human-level car-following performance. The extensive simulations demonstrate
that our interaction-aware switching control framework achieves improved
control performance and data efficiency compared to the unified control
strategies. Moreover, the experimental results reveal that human drivers would
not always keep reacting to their leading vehicle but occasionally take
safety-critical or intentional actions -- interaction matters but not always.
