ParkingE2E: Camera-based End-to-end Parking Network, from Images to Planning
<|reference_start|>ParkingE2E: Camera-based End-to-end Parking Network, from Images to Planning: 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.<|reference_end|>
@article{li2024parkinge2e:,
    title={ParkingE2E: Camera-based End-to-end Parking Network, from Images to
  Planning},
    author={Changze Li, Ziheng Ji, Zhe Chen, Tong Qin, Ming Yang},
    journal={arXiv preprint arXiv:2408.02061},
    year={2024},
    archivePrefix={arXiv},
    eprint={2408.02061},
    primaryClass={cs.CV cs.AI cs.RO}
}