Neural Motion Planning for Autonomous Parking

TL;DR

This paper introduces a neural hybrid motion planning algorithm for autonomous parking that combines deep generative models with traditional methods to improve efficiency in complex environments.

Contribution

It proposes a neural Hybrid A* algorithm guided by a CVAE to enhance path planning efficiency in autonomous parking scenarios.

Findings

Improved planning efficiency over traditional methods.

Effective learning of feasible trajectories from demonstrations.

Enhanced representation of parking environment states.

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.