  Human trajectory forecasting in crowds, at its core, is a sequence prediction
problem with specific challenges of capturing inter-sequence dependencies
(social interactions) and consequently predicting socially-compliant multimodal
distributions. In recent years, neural network-based methods have been shown to
outperform hand-crafted methods on distance-based metrics. However, these
data-driven methods still suffer from one crucial limitation: lack of
interpretability. To overcome this limitation, we leverage the power of
discrete choice models to learn interpretable rule-based intents, and
subsequently utilise the expressibility of neural networks to model
scene-specific residual. Extensive experimentation on the interaction-centric
benchmark TrajNet++ demonstrates the effectiveness of our proposed architecture
to explain its predictions without compromising the accuracy.
