让自动驾驶决策可解释，提升安全性和透明度。
Interpretable Decision-Making for End-to-End Autonomous Driving
- 设计稀疏局部特征图的损失函数，增强模型可解释性。
- 在CARLA上实现最低违规分数和最高路线完成率。
- 单目非集成模型优于榜单领先方法，兼顾性能与可解释。
可信AI对自动驾驶的大规模部署至关重要。尽管端到端方法能直接从原始数据生成控制指令，但在复杂城市场景中解释这些决策仍具挑战性，主要源于深度神经网络的非线性决策边界，难以理解AI决策背后的逻辑。本文提出一种方法，在优化控制指令的同时提升可解释性。通过设计促进稀疏与局部特征激活的损失函数，使模型生成可解释的特征图，明确指示图像中哪些区域影响了预测的控制命令。我们在CARLA基准上进行详尽消融实验，并验证方法有效性。结果表明，该方法提升了可解释性，且与降低违规行为相关，实现了更安全、高性能的驾驶模型。值得注意的是，我们的单目非集成模型在CARLA排行榜上以更低的违规分数和最高的路线完成率超越现有最优方法，同时确保决策可解释。
原文摘要 · Abstract (English)
Trustworthy AI is mandatory for the broad deployment of autonomous vehicles. Although end-to-end approaches derive control commands directly from raw data, interpreting these decisions remains challenging, especially in complex urban scenarios. This is mainly attributed to very deep neural networks with non-linear decision boundaries, making it challenging to grasp the logic behind AI-driven decisions. This paper presents a method to enhance interpretability while optimizing control commands in autonomous driving. To address this, we propose loss functions that promote the interpretability of our model by generating sparse and localized feature maps. The feature activations allow us to explain which image regions contribute to the predicted control command. We conduct comprehensive ablation studies on the feature extraction step and validate our method on the CARLA benchmarks. We also demonstrate that our approach improves interpretability, which correlates with reducing infractions, yielding a safer, high-performance driving model. Notably, our monocular, non-ensemble model surpasses the top-performing approaches from the CARLA Leaderboard by achieving lower infraction scores and the highest route completion rate, all while ensuring interpretability.
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