Neural Motion Planning for Autonomous Parking
This work addresses path planning efficiency for autonomous parking systems, representing an incremental improvement over existing methods.
The paper tackles the efficiency limitations of conventional motion planning methods like A* and Hybrid A* for autonomous parking by introducing a hybrid strategy that combines a conditional variational autoencoder (CVAE) with a neural Hybrid A* algorithm, resulting in improved algorithm performance.
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.