PathGAN: Local Path Planning with Generative Adversarial Networks

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.

Regularizing Neural Networks for Future Trajectory Prediction via Inverse Reinforcement Learning

Predicting distant future trajectories of agents in a dynamic scene is not an easy problem because the future trajectory of an agent is affected by not only his/her past trajectory but also the scene contexts. To tackle this problem, we propose a model based on recurrent neural networks (RNNs) and a novel method for training the model. The proposed model is based on an encoder-decoder architecture where the encoder encodes inputs (past trajectories and scene context information) while the decoder produces a trajectory from the context vector given by the encoder. We train the networks of the proposed model to produce a future trajectory, which is the closest to the true trajectory, while maximizing a reward from a reward function. The reward function is also trained at the same time to maximize the margin between the rewards from the ground-truth trajectory and its estimate. The reward function plays the role of a regularizer for the proposed model so the trained networks are able to better utilize the scene context information for the prediction task. We evaluated the proposed model on several public datasets. Experimental results show that the prediction performance of the proposed model is much improved by the regularization, which outperforms the-state-of-the-arts in terms of accuracy.