  In autonomous driving, predicting the behavior (turning left, stopping, etc.)
of target vehicles is crucial for the self-driving vehicle to make safe
decisions and avoid accidents. Existing deep learning-based methods have shown
excellent and accurate performance, but the black-box nature makes it
untrustworthy to apply them in practical use. In this work, we explore the
interpretability of behavior prediction of target vehicles by an Episodic
Memory implanted Neural Decision Tree (abbrev. eMem-NDT). The structure of
eMem-NDT is constructed by hierarchically clustering the text embedding of
vehicle behavior descriptions. eMem-NDT is a neural-backed part of a
pre-trained deep learning model by changing the soft-max layer of the deep
model to eMem-NDT, for grouping and aligning the memory prototypes of the
historical vehicle behavior features in training data on a neural decision
tree. Each leaf node of eMem-NDT is modeled by a neural network for aligning
the behavior memory prototypes. By eMem-NDT, we infer each instance in behavior
prediction of vehicles by bottom-up Memory Prototype Matching (MPM) (searching
the appropriate leaf node and the links to the root node) and top-down Leaf
Link Aggregation (LLA) (obtaining the probability of future behaviors of
vehicles for certain instances). We validate eMem-NDT on BLVD and LOKI
datasets, and the results show that our model can obtain a superior performance
to other methods with clear explainability. The code is available at
https://github.com/JWFangit/eMem-NDT.
