  To achieve complete autonomous vehicles, it is crucial for autonomous
vehicles to communicate and interact with their surrounding vehicles.
Especially, since the lane change scenarios do not have traffic signals and
traffic rules, the interactions between vehicles need to be considered for the
autonomous vehicles. To address this issue, we propose a cooperative and
interaction-aware decision-making algorithm for autonomous vehicles that
stochastically considers the future behavior of surrounding vehicles based on
actual driving data. The algorithm is designed for both lane changing and lane
keeping vehicles, and effectively considers interaction by using an interaction
model based on relative information between vehicles with fewer states. To
design the decision-making, the interaction model is defined as Markov decision
process, and stochastic dynamic programming is used to solve the Markov
decision process. We validate the effectiveness of our proposed algorithm in
lane change scenarios that require interaction. Our results demonstrate that
the proposed algorithm enables cooperative and interaction-aware
decision-making while accommodating various driving styles. Additionally, by
comparing it with other methods, such as the intelligent driver model and game
theory-based decision-making, we validate the safety and comfortable
decision-making of our proposed algorithm. Furthermore, through driving with a
human-driven vehicle, it is confirmed that the proposed decision-making enables
to cooperatively and effectively drive with humans.
