  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.
