论文标题
可解释的深度跟踪
Interpretable Deep Tracking
论文作者
论文摘要
想象一下，撞车是自动驾驶汽车的乘客。您不想知道为什么会发生吗？ 3D检测，多对象跟踪和运动预测中的当前端到端可优化的深度神经网络（DNN）几乎没有解释他们如何做出决策。为了帮助弥合这一差距，我们设计了一个可端到端的优化多对象跟踪体系结构和培训协议，其灵感来自最近提出的交换干预培训方法（IIT）。通过列举不同的跟踪决策和相关的推理程序，我们可以培训单个网络，以通过IIT来理解可能的决策。每个网络的决定都可以通过与对齐方式进行训练的高级结构因果模型（SCM）来解释。此外，我们提出的模型学会了对这些结果进行排名，从而利用端到端培训中深度学习的希望，同时固有地解释。
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.
