  Imagine experiencing a crash as the passenger of an autonomous vehicle.
Wouldn't you want to know why it happened? Current end-to-end optimizable deep
neural networks (DNNs) in 3D detection, multi-object tracking, and motion
forecasting provide little to no explanations about how they make their
decisions. To help bridge this gap, we design an end-to-end optimizable
multi-object tracking architecture and training protocol inspired by the
recently proposed method of interchange intervention training (IIT). By
enumerating different tracking decisions and associated reasoning procedures,
we can train individual networks to reason about the possible decisions via
IIT. Each network's decisions can be explained by the high-level structural
causal model (SCM) it is trained in alignment with. Moreover, our proposed
model learns to rank these outcomes, leveraging the promise of deep learning in
end-to-end training, while being inherently interpretable.

Computer Vision and Pattern Recognition;Artificial Intelligence;Machine Learning