ParkingE2E: Camera-based End-to-end Parking Network, from Images to Planning

• URL: http://arxiv.org/abs/2408.02061v1
• Date: Sun, 4 Aug 2024 15:20:39 GMT
• Title: ParkingE2E: Camera-based End-to-end Parking Network, from Images to Planning
• Authors: Changze Li, Ziheng Ji, Zhe Chen, Tong Qin, Ming Yang,
• Abstract summary: Traditional parking algorithms are usually implemented using rule-based schemes. Neural-network-based methods tend to be more intuitive and versatile than the rule-based methods. 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 method achieved an average parking success rate of 87.8% across four different real-world garages.
• Score: 7.034120265476802
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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