Neural Motion Planning for Autonomous Parking

• URL: http://arxiv.org/abs/2111.06739v2
• Date: Tue, 16 Nov 2021 06:46:22 GMT
• Title: Neural Motion Planning for Autonomous Parking
• Authors: Dongchan Kim and Kunsoo Huh
• Abstract summary: This paper presents a hybrid motion planning strategy that combines a deep generative network with a conventional motion planning method. The proposed method effectively learns the representations of a given state, and shows improvement in terms of algorithm performance.
• Score: 6.1805402105389895
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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