  Autonomous parking is a crucial task in the intelligent driving field.
Traditional parking algorithms are usually implemented using rule-based
schemes. However, these methods are less effective in complex parking scenarios
due to the intricate design of the algorithms. In contrast,
neural-network-based methods tend to be more intuitive and versatile than the
rule-based methods. By collecting a large number of expert parking trajectory
data and emulating human strategy via learning-based methods, the parking task
can be effectively addressed. In this paper, we employ imitation learning to
perform end-to-end planning from RGB images to path planning by imitating human
driving trajectories. The proposed end-to-end approach utilizes a target query
encoder to fuse images and target features, and a transformer-based decoder to
autoregressively predict future waypoints. We conducted extensive experiments
in real-world scenarios, and the results demonstrate that the proposed method
achieved an average parking success rate of 87.8% across four different
real-world garages. Real-vehicle experiments further validate the feasibility
and effectiveness of the method proposed in this paper.
