PathGAN: Local Path Planning with Generative Adversarial Networks

Dooseop Choi, Seung-jun Han, Kyoungwook Min, Jeongdan Choi

Published 2020-07-08Version 1

Targeting autonomous driving without High-Definition maps, we present a model capable of generating multiple plausible paths from sensory inputs for autonomous vehicles. Our generative model comprises two neural networks, Feature Extraction Network (FEN) and Path Generation Network (PGN). FEN extracts meaningful features from input scene images while PGN generates multiple paths from the features given a driving intention and speed. To make paths generated by PGN both be plausible and match the intention, we introduce a discrimination network and train it with PGN under generative adversarial networks (GANs) framework. Besides, to further increase the accuracy and diversity of the generated paths, we encourage PGN to capture intentions hidden in the positions in the paths and let the discriminator evaluate how realistic the sequential intentions are. Finally, we introduce ETRIDriving, the dataset for autonomous driving where the recorded sensory data is labeled with discrete high-level driving actions, and demonstrate the-state-of-the-art performances of the proposed model on ETRIDriving in terms of the accuracy and diversity.