这篇论文教你怎么用可解释性找自动驾驶模型的bug，还能动手修好它，比单纯黑盒调参靠谱多了。
本文提出将无监督字典学习作为后验可解释性模块，集成到端到端自动驾驶模型中。该方法将驾驶行为分解为语义可解释的概念，并展示其对轨迹预测决策的因果影响。通过概念级定向干预，可以纠正错误的驾驶行为，带来可测量的性能提升。实验在多个驾驶场景下验证了该框架的有效性。
Driving the Wrong Way: Leveraging Interpretability in End2End Autonomous Driving Models
The increasing adoption of end-to-end learning for autonomous driving introduces increased model complexity and opacity, raising the risk of learning undesired or erroneous behavior. In this work, we integrate unsupervised dictionary learning as a post hoc interpretability module within state-of-the-art driving models to decompose driving behavior into semantically meaningful concepts while demonstrating their causal influence on the model's driving decisions. We propose a stepwise framework for extracting and interpreting meaningful concepts from the end-to-end model and connecting them to the multifaceted model outputs, thereby revealing the underlying decision-making logic for the prediction of future trajectories. Furthermore, targeted interventions at the concept level allow us to manipulate and correct driving decisions, resulting in measurable improvements in overall driving performance. We thus demonstrate how interpretability can effectively be used to reduce model opacity, uncover erroneous behavior, and enable targeted mitigation, ultimately boosting model performance.