  This paper presents a hybrid motion planning strategy that combines a deep
generative network with a conventional motion planning method. Existing
planning methods such as A* and Hybrid A* are widely used in path planning
tasks because of their ability to determine feasible paths even in complex
environments; however, they have limitations in terms of efficiency. To
overcome these limitations, a path planning algorithm based on a neural
network, namely the neural Hybrid A*, is introduced. This paper proposes using
a conditional variational autoencoder (CVAE) to guide the search algorithm by
exploiting the ability of CVAE to learn information about the planning space
given the information of the parking environment. A non-uniform expansion
strategy is utilized based on a distribution of feasible trajectories learned
in the demonstrations. The proposed method effectively learns the
representations of a given state, and shows improvement in terms of algorithm
performance.
