  Cooperative Adaptive Cruise Control (CACC) often requires human takeover for
tasks such as exiting a freeway. Direct human takeover can pose significant
risks, especially given the close-following strategy employed by CACC, which
might cause drivers to feel unsafe and execute hard braking, potentially
leading to collisions. This research aims to develop a CACC takeover controller
that ensures a smooth transition from automated to human control. The proposed
CACC takeover maneuver employs an indirect human-machine shared control
approach, modeled as a Stackelberg competition where the machine acts as the
leader and the human as the follower. The machine guides the human to respond
in a manner that aligns with the machine's expectations, aiding in maintaining
following stability. Additionally, the human reaction function is integrated
into the machine's predictive control system, moving beyond a simple
"prediction-planning" pipeline to enhance planning optimality. The controller
has been verified to i) enable a smooth takeover maneuver of CACC; ii) ensure
string stability within a specific Operational Design Domain (ODD) when human
control authority is below 32.7%; iii) enhance both perceived and actual safety
through machine interventions; and iv) reduce the impact on upstream traffic by
up to 60%.
