Context-free Self-Conditioned GAN for Trajectory Forecasting

Tiago Rodrigues de Almeida; Eduardo Gutierrez Maestro; Oscar Martinez Mozos

cs.LGMar 9, 2026

Context-free Self-Conditioned GAN for Trajectory Forecasting

Authors: Tiago Rodrigues de Almeida, Eduardo Gutierrez Maestro, Oscar Martinez Mozos

Organizations: AASS, Örebro University, Sweden

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 & tables

Panel of Figure 1 — Fig. 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.

Panel of Figure 2 — Fig. 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, $\boldsymbol{{\hat{Y}}}$ .

		Baselines		Ours
Data set	Labels (# samples)	LSTM [ 22 ]	CF VAN GAN [ 14 ]	CF VAN GAN + wL2	CF VAN GAN + wB	CF VAN GAN +wL2 + wB
THÖR	workers (413)	0.695 1.064	$0.642\pm 0.006$ $1.033\pm 0.005$	$0.629\pm 0.005$ $1.009\pm 0.014$	$0.644\pm 0.012$ $1.044\pm 0.028$	$\mathbf{0.625\pm 0.009}$ $\mathbf{1.006\pm 0.019}$
	visitors (1379)	0.664 1.139	$0.660\pm 0.001$ $\mathbf{1.105\pm 0.090}$	$\mathbf{0.657\pm 0.003}$ $1.107\pm 0.007$	$0.668\pm 0.005$ $1.124\pm 0.018$	$0.657\pm 0.003$ $1.113\pm 0.013$
	inspector (260)	0.796 1.582	$0.735\pm 0.007$ $1.474\pm 0.019$	$0.736\pm 0.008$ $\mathbf{1.473\pm 0.013}$	$\mathbf{0.729\pm 0.013}$ $1.479\pm 0.049$	$0.734\pm 0.003$ $1.476\pm 0.015$
Argoverse	others (526)	1.864 3.029	$1.815\pm 0.031$ $2.969\pm 0.034$	$1.799\pm 0.007$ $2.944\pm 0.022$	$\mathbf{1.789\pm 0.012}$ $2.927\pm 0.020$	$1.801\pm 0.027$ $\mathbf{2.919\pm 0.032}$
	av (2600)	1.512 2.278	$\mathbf{1.467\pm 0.007}$ $\mathbf{2.269\pm 0.023}$	$1.482\pm 0.009$ $2.292\pm 0.010$	$1.480\pm 0.003$ $2.282\pm 0.006$	$1.493\pm 0.010$ $2.298\pm 0.028$

TABLE I: Intra-classes ADE/FDE metrics (in meters) in the test sets.

		Baselines		Ours
Data set	Cluster ID (# samples)	LSTM [ 22 ]	CF VAN GAN [ 14 ]	CF VAN GAN + wL2	CF VAN GAN + wB	CF VAN GAN +wL2 + wB
THÖR	9 (23)	1.203 2.456	$1.120\pm 0.025$ $2.758\pm 0.082$	$1.054\pm 0.048$ $2.505\pm 0.134$	$1.124\pm 0.038$ $2.811\pm 0.136$	$\mathbf{1.039\pm 0.055}$ $\mathbf{2.505\pm 0.105}$
THÖR	0 (1003)	0.325 0.447	$\mathbf{0.311\pm 0.003}$ $\mathbf{0.403\pm 0.010}$	$0.321\pm 0.004$ $0.419\pm 0.015$	$0.317\pm 0.007$ $0.416\pm 0.021$	$0.315\pm 0.002$ $0.424\pm 0.017$
Argoverse	10 (16)	7.394 19.075	$7.184\pm 0.178$ $18.402\pm 0.415$	$7.105\pm 0.123$ $18.233\pm 0.297$	$7.122\pm 0.055$ $18.276\pm 0.113$	$\mathbf{7.047\pm 0.088}$ $\mathbf{18.128\pm 0.194}$
	18 (1542)	0.912 1.148	$0.809\pm 0.016$ $1.100\pm 0.017$	$0.807\pm 0.010$ $1.088\pm 0.027$	$0.805\pm 0.007$ $1.079\pm 0.012$	$\mathbf{0.795\pm 0.008}$ $\mathbf{1.055\pm 0.030}$

TABLE II: ADE/FDE metrics (in meters) for 2 clusters of the test set.

	Baselines		Ours
Data set	LSTM [ 22 ]	CF VAN GAN [ 14 ]	CF VAN GAN + wL2	CF VAN GAN + wB	CF VAN GAN +wL2 + wB
THÖR	$0.685$ $1.163$	$0.663\pm 0.002$ $1.123\pm 0.019$	$\mathbf{0.658\pm 0.003}$ $\mathbf{1.119\pm 0.009}$	$0.668\pm 0.003$ $\mathbf{1.119\pm 0.009}$	$\mathbf{0.658\pm 0.002}$ $1.122\pm 0.013$
Argoverse	$1.948$ $3.330$	$\mathbf{1.912\pm 0.012}$ $\mathbf{3.298\pm 0.014}$	$1.915\pm 0.012$ $3.307\pm 0.017$	$1.915\pm 0.009$ $3.310\pm 0.018$	$1.923\pm 0.010$ $3.307\pm 0.014$

TABLE III: ADE/FDE metrics (in meters) in the test sets.

Data set	cGAN	Ours
THÖR	$0.657\pm 0.003$ $1.114\pm 0.011$	$\mathbf{0.591\pm 0.014}$ $\mathbf{0.937\pm 0.022}$
Argoverse	$1.927\pm 0.016$ $3.323\pm 0.030$	$\mathbf{1.785\pm 0.014}$ $\mathbf{2.887\pm 0.038}$

TABLE IV: ADE/FDE metrics (in meters) in the test sets for ideal models. These can be seen as lower bounds.

Fig. 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.

Fig. 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.

Fig. 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}

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Trajectory prediction forecasts how people, vehicles, robots, and other dynamic systems will move from observed histories and context.

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MoCAR: Motion-code Coordinate-aware AutoRegression for Continuous Trajectory Forecasting

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Institute of Cartography and Geoinformatics Leibniz University Hannover, 30167 Hannover, Germany · Faculty of Geo-Information Science and Earth Observation University of Twente, 7522 NH Enschede, The Netherlands

Trajectory Generation Trajectory Representation Learning Multimodal Trajectory Prediction Trajectory Prediction

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Autoregressive generation is natural for language, where predicted tokens can be directly reused as the next prediction state, but trajectory forecasting lacks such a clean token: motion is continuous, multimodal, and expressed in local coordinate frames that evolve with the predicted trajectory. We present MoCAR (Motion-code Coordinate-aware AutoRegression), a decoder-only framework that casts trajectory forecasting as next-code prediction in a coordinate-aware continuous latent space. MoCAR learns a continuous motion-code space from endpoint-normalized trajectory segments, where each code jointly captures local trajectory geometry and the reference-frame transition induced by that segment. Historical motion codes are used as a teacher-forced prefix, future codes are generated autoregressively under temporal, map, agent, and mode interactions, and predicted codes persist in latent memory while decoded endpoints update the local scene context. This enables rollout without trajectory-space re-tokenization, trajectory queries, goal candidates, or proposal-and-refinement pipelines. On Argoverse (AV) benchmarks, MoCAR achieves top-tier performance with a simple single-stage architecture, transfers strongly from AV2 to AV1 in zero-shot evaluation, and improves on turn-heavy scenarios. Ablations confirm that the learned continuous motion-code space, latent alignment, weak KL regularization, and joint tokenizer-predictor optimization are essential for stable latent autoregression.

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