  Automated driving has the potential to revolutionize personal, public, and
freight mobility. Beside accurately perceiving the environment, automated
vehicles must plan a safe, comfortable, and efficient motion trajectory. To
promote safety and progress, many works rely on modules that predict the future
motion of surrounding traffic. Modular automated driving systems commonly
handle prediction and planning as sequential, separate tasks. While this
accounts for the influence of surrounding traffic on the ego vehicle, it fails
to anticipate the reactions of traffic participants to the ego vehicle's
behavior. Recent models increasingly integrate prediction and planning in a
joint or interdependent step to model bi-directional interactions. To date, a
comprehensive overview of different integration principles is lacking. We
systematically review state-of-the-art deep learning-based prediction and
planning, and focus on integrated prediction and planning models. Different
facets of the integration ranging from model architecture and model design to
behavioral aspects are considered and related to each other. Moreover, we
discuss the implications, strengths, and limitations of different integration
principles. By pointing out research gaps, describing relevant future
challenges, and highlighting trends in the research field, we identify
promising directions for future research.
