PathGAN: Local Path Planning with Attentive Generative Adversarial Networks

TL;DR

PathGAN is a novel generative adversarial network model that produces multiple plausible driving paths from egocentric images, enhancing autonomous vehicle navigation without reliance on high-definition maps.

Contribution

The paper introduces a new path generation model with an attentive discriminator and a novel architecture that improves path accuracy and diversity, along with a new dataset for autonomous driving.

Findings

Achieves state-of-the-art accuracy on ETRIDriving dataset.

Generates diverse and plausible paths from images.

Outperforms existing methods in path prediction quality.

Abstract

To achieve autonomous driving without high-definition maps, we present a model capable of generating multiple plausible paths from egocentric images for autonomous vehicles. Our generative model comprises two neural networks: the feature extraction network (FEN) and path generation network (PGN). The FEN extracts meaningful features from an egocentric image, whereas the PGN generates multiple paths from the features, given a driving intention and speed. To ensure that the paths generated are plausible and consistent with the intention, we introduce an attentive discriminator and train it with the PGN under generative adversarial networks framework. We also devise an interaction model between the positions in the paths and the intentions hidden in the positions and design a novel PGN architecture that reflects the interaction model, resulting in the improvement of the accuracy and diversity of the generated paths. Finally, we introduce ETRIDriving, a dataset for autonomous driving in which the recorded sensor data are labeled with discrete high-level driving actions, and demonstrate the state-of-the-art performance of the proposed model on ETRIDriving in terms of accuracy and diversity.