Deep learning-based models are widely deployed in autonomous driving areas, especially the increasingly noticed end-to-end solutions. However, the black-box property of these models raises concerns about their trustworthiness and safety for autonomous driving, and how to debug the causality has become a pressing concern. Despite some existing research on the explainability of autonomous driving, there is currently no systematic solution to help researchers debug and identify the key factors that lead to the final predicted action of end-to-end autonomous driving. In this work, we propose a comprehensive approach to explore and analyze the causality of end-to-end autonomous driving. First, we validate the essential information that the final planning depends on by using controlled variables and counterfactual interventions for qualitative analysis. Then, we quantitatively assess the factors influencing model decisions by visualizing and statistically analyzing the response of key model inputs. Finally, based on the comprehensive study of the multi-factorial end-to-end autonomous driving system, we have developed a strong baseline and a tool for exploring causality in the close-loop simulator CARLA. It leverages the essential input sources to obtain a well-designed model, resulting in highly competitive capabilities. As far as we know, our work is the first to unveil the mystery of end-to-end autonomous driving and turn the black box into a white one. Thorough close-loop experiments demonstrate that our method can be applied to end-to-end autonomous driving solutions for causality debugging. Code will be available at https://github.com/bdvisl/DriveInsight.

翻译：基于深度学习的模型在自动驾驶领域得到广泛应用，尤其是日益受到关注的端到端解决方案。然而，这些模型的黑箱特性引发了对其在自动驾驶中可信度与安全性的担忧，如何调试其因果关系已成为紧迫问题。尽管已有一些关于自动驾驶可解释性的研究，但目前尚无系统化的解决方案来帮助研究人员调试并识别导致端到端自动驾驶最终预测行为的关键因素。本研究提出了一种综合方法来探索和分析端到端自动驾驶的因果关系。首先，我们通过控制变量和反事实干预进行定性分析，验证最终规划所依赖的核心信息。随后，通过可视化及统计分析关键模型输入的响应，定量评估影响模型决策的因素。最后，基于对多因素端到端自动驾驶系统的综合研究，我们在闭环模拟器CARLA中开发了一个强大的基准模型和因果关系探索工具。该工具利用核心输入源构建了精心设计的模型，从而获得了极具竞争力的性能。据我们所知，本研究首次揭示了端到端自动驾驶的内在机制，将黑箱转化为白箱。详尽的闭环实验表明，我们的方法可应用于端到端自动驾驶解决方案的因果关系调试。代码将在 https://github.com/bdvisl/DriveInsight 公开。