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.

翻译：在自动驾驶中，预测目标车辆的行为（如左转、停车等）对于保证自车做出安全决策并避免事故至关重要。现有基于深度学习方法已展现出卓越且精确的性能，但其黑箱特性导致实际应用中缺乏可信度。本研究通过植入情景记忆的神经决策树（简称eMem-NDT）探索目标车辆行为预测的可解释性。eMem-NDT的结构通过对车辆行为描述的文本嵌入进行层次聚类构建。通过将深度模型的soft-max层替换为eMem-NDT，该模型作为预训练深度学习模型的神经支持部分，在神经决策树上对训练数据中历史车辆行为特征的记忆原型进行分组与对齐。eMem-NDT的每个叶节点由神经网络建模，用于对齐行为记忆原型。通过eMem-NDT，我们采用自底向上的记忆原型匹配方法（搜索合适叶节点及其与根节点的连接路径）和自上而下的叶节点链接聚合方法（获取特定实例下车辆未来行为的概率）来推断车辆行为预测中的每个实例。我们在BLVD与LOKI数据集上验证了eMem-NDT，结果表明该模型在保持清晰可解释性的同时，性能优于其他方法。代码已开源至https://github.com/JWFangit/eMem-NDT。