Human Trajectory Prediction via Neural Social Physics
Jiangbei Yue, Dinesh Manocha, and He Wang
Existing approaches generally fall into model-based and model-free methods. Model-
based methods tend to possess good explainability. However, they are less effective in
data fitting. Model-free methods based on deep learning excel at data fitting, but lack
explainability. Our paper proposes neural social physics that can explain pedestrian
behaviors and retain good data-fitting capabilities to predict human trajectories by
combining model-based and model-free methods.
•A new neural differentiable equation model for trajectory prediction and analysis.
•A new mechanism to combine explicit models with neural networks for prediction.
•The NSP model performs well in: prediction, generalization and explainability
𝒅𝒕 𝒕 = 𝒇𝜽,𝝓 𝒕, 𝒒 𝒕 , 𝜴 𝒕 , 𝒒𝑻, 𝑬 + 𝜶𝝓(𝒕, 𝒒𝒕:𝒕−𝑴)
𝒒 𝒕 + 𝜟𝒕 ≈ 𝒒 𝒕 + ሶ
𝒒 𝒕 ∆𝒕 = 𝒑 𝒕
𝒑 𝒕 + ∆𝒕 ሶ
𝒑 𝒕 + 𝜶(𝒕, 𝒒𝒕:𝒕−𝑴 )
Dataset S-GAN Sophie PECNet Y-net NSP
ETH 0.81/1.52 0.70/1.43 0.54/0.87 0.28/0.33 0.25/0.24
Hotel 0.72/1.61 0.76/1.67 0.18/0.24 0.10/0.14 0.09/0.13
Univ 0.60/1.26 0.54/1.24 0.35/0.60 0.24/0.41 0.21/0.38
Zara1 0.34/0.69 0.30/0.63 0.22/0.39 0.17/0.27 0.16/0.27
Zara2 0.42/0.84 0.38/0.78 0.17/0.30 0.13/0.22 0.12/0.20
AVG 0.58/1.18 0.54/1.15 0.29/0.48 0.18/0.27 0.17/0.24
SDD 27.2/41.4 16.3/29.4 10.0/15.9 7.9/11.9 6.5/10.6
Prediction accuracy on public datasets. ADE/FDE
Interpretability of Prediction
Collisions in unseen scenarios
Red is observed. Green is our prediction. Black is the ground-truth. Blue is
𝑔𝑜𝑎𝑙,𝐹𝑐𝑜𝑙 and 𝐹
𝑒𝑛𝑣 are shown as yellow, blue and black arrows.