SlowGuess/NeurIPS_2023_OCR huggingface dataset:
Table 6 demonstrates that training on the full mixture of observation masks somewhat reduces the predictive performance of DJINN when compared to the model trained exclusively on the predictive task. However, the diversity of trajectories measured using MFD [40] increases when training on the more diverse distribution.
Figure 6: Additional generated traffic scenes from the INTERACTION validation set. Each row demonstrates samples generated using a different observation mask.
Table 6: Comparison of trajectory forecasting performance for models trained with varying observation distributions. Trajectory metrics are measured using 6 samples per scene on the INTERACTION validation set.
Observations	minADE	minFDE	Scene minADE	Scene minFDE	MFD
Predictive	0.21	0.49	0.35	0.91	2.33
Mixture	0.26	0.63	0.45	1.17	3.11
D.2 Effect of Reduced Sampling Steps
The continuous time training procedure of DJINN enables test-time variation in the number of sampling steps. Table 7 outlines the effect of reducing the number of sampling steps from the 50 steps which are used in all other experiments.
Table 7 shows that reducing the number of sampling steps results in modest trajectory forecasting performance reductions up to 20 sampling steps across all metrics. Using 10 steps severely impacts the quality of sampled scenes across all metrics. As sampling time scales linearly with the number of sampling steps, reducing the number of sampling steps allows for a performance runtime tradeoff.
Table 7: Trajectory forecasting performance versus the number of timesteps used in the diffusion sampling procedure. Trajectory forecasting performance is measured using 6 samples per scene on the INTERACTION validation set.
Diffusion Steps	minADE	minFDE	Scene minADE	Scene minFDE
10	0.28	0.64	0.45	1.135
20	0.22	0.51	0.37	0.95
30	0.22	0.50	0.36	0.92
40	0.21	0.50	0.35	0.92
50	0.21	0.49	0.35	0.92
D.3 Model Runtime
We compare DJINN's runtime to SceneTransformer [30], varying the input size as measured by the number of agents in the scene. Runtimes are measured across 1000 samples on a GeForce RTX 2070 Mobile GPU.
Table 8: Average scenario generation time for DJINN and Scene Transformer across varying scene sizes.
Agent Count	Scene Transformer	DJINN - 50 Steps	DJINN - 25 Steps
8	0.0126s	0.574s	1.15s
16	0.0140s	0.611s	1.24s
32	0.017s	0.844s	1.69s
64	0.026s	1.40s	2.89s