  Motion planning is an essential element of the modular architecture of
autonomous vehicles, serving as a bridge between upstream perception modules
and downstream low-level control signals. Traditional motion planners were
initially designed for specific Automated Driving Functions (ADFs), yet the
evolving landscape of highly automated driving systems (ADS) requires motion
for a wide range of ADFs, including unforeseen ones. This need has motivated
the development of the ``hybrid" approach in the literature, seeking to enhance
motion planning performance by combining diverse techniques, such as
data-driven (learning-based) and logic-driven (analytic) methodologies. Recent
research endeavours have significantly contributed to the development of more
efficient, accurate, and safe hybrid methods for Tactical Decision Making (TDM)
and Trajectory Generation (TG), as well as integrating these algorithms into
the motion planning module. Owing to the extensive variety and potential of
hybrid methods, a timely and comprehensive review of the current literature is
undertaken in this survey article. We classify the hybrid motion planners based
on the types of components they incorporate, such as combinations of
sampling-based with optimization-based/learning-based motion planners. The
comparison of different classes is conducted by evaluating the addressed
challenges and limitations, as well as assessing whether they focus on TG
and/or TDM. We hope this approach will enable the researchers in this field to
gain in-depth insights into the identification of current trends in hybrid
motion planning and shed light on promising areas for future research.
