Context-free Self-Conditioned GAN for Trajectory Forecasting

TL;DR

A context-free unsupervised approach based on a self-conditioned GAN to learn different modes from 2D trajectories to solve the problem of trajectory forecasting, which shows that each mode indicates a different behavioral moving pattern in the discriminator’s feature space.

Abstract

In this paper, we present a context-free unsupervised approach based on a self-conditioned GAN to learn different modes from 2D trajectories. Our intuition is that each mode indicates a different behavioral moving pattern in the discriminator's feature space. We apply this approach to the problem of trajectory forecasting. We present three different training settings based on self-conditioned GAN, which produce better forecasters. We test our method in two data sets: human motion and road agents. Experimental results show that our approach outperforms previous context-free methods in the least representative supervised labels while performing well in the remaining labels. In addition, our approach outperforms globally in human motion, while performing well in road agents.

Figures (5)

• Figure 1: Proposed framework's overview. First, self-conditioned GAN learns the different modes of the input data. Then, this information is used as training settings (via soft-assumptions) to improve the prediction in specific modes.
• Figure 2: Self-conditioned GAN for identifying meaningful clusters, which represent different modes ($\boldsymbol{m}$) of the input trajectory data. It comprises two branches: the discriminator ($D$) and the generator ($G$). During the former's training (upper part), the inputs are fake or real samples, $\boldsymbol{{\hat{F}}}$ and $\boldsymbol{R}$, respectively. Then, the encoder extracts features from those inputs. These features become semantically meaningful during training, so they are reclustered from time to time. Finally, a classifier produces the likelihood, $\boldsymbol{s}$, of the respective input being a sample from the real distribution. The second branch (lower part) depicts the generator's training. It takes as input the observed trajectories, $\boldsymbol{X}$, concatenated with the respective modes, $\boldsymbol{m}$. Before this, the clustering algorithm provides the respective modes based on the discriminator's encoder features from the entire and real trajectory, $\boldsymbol{R}$. Thereafter, the generator's encoder extracts features from the input. Finally, these features plus the latent variable, $\boldsymbol{z}$, are decoded, yielding the predicted trajectory, $\boldsymbol{{\hat{Y}}}$.
• Figure 3: Example of trajectory forecasting in the THÖR (left) and the Argoverse (right) data sets for complex trajectories, being $X$ the observed track and $Y$ and $\hat{Y}$ the ground truth and the predictions, respectively. We can see how our approach gets closer to the ground truth in these cases.
• Figure 4: Entire trajectories randomly sampled from THÖR (left) and Argoverse (right) data sets, where crosses mark the starting point of each track. In THÖR, trajectories from cluster 9 go from right to left, while tracks from cluster 10 go from left to right. In Argoverse, trajectories from cluster 0 are much longer than the ones from cluster 18.
• Figure 5: Test set examples of trajectories generated from the self-conditioned GAN with four different labels in both data sets (THÖR on the left and Argoverse on the right). Both cases show that the right condition (represented by $\hat{Y}_{Ours}$) yields trajectories closer to the ground truth when compared to other three different modes ($m_{1}$, $m_{2}$, and $m_{3}$).