面向端到端自动驾驶的可解释决策
Interpretable Decision-Making for End-to-End Autonomous Driving
AI总结：
针对端到端自动驾驶决策难解释的问题，本文提出用稀疏局部化特征图及相应损失增强控制指令预测的可解释性，并在CARLA上取得最高路线完成率与更低违规分数。
摘要：
可信AI是自动驾驶汽车广泛部署的必要条件。尽管端到端方法能够直接从原始数据推导控制指令，但解释这些决策仍具挑战性，尤其是在复杂城市场景中。这主要归因于具有非线性决策边界的极深神经网络，使人们难以掌握AI驱动决策背后的逻辑。本文提出一种在优化自动驾驶控制指令的同时增强可解释性的方法。为解决该问题，我们提出若干损失函数，通过生成稀疏且局部化的特征图来提升模型的可解释性。特征激活使我们能够解释哪些图像区域对预测的控制指令作出贡献。我们对特征提取步骤进行了全面的消融研究，并在CARLA基准上验证了该方法。我们还证明，本方法提升了可解释性，而这与减少违规相关，从而产生更安全、高性能的驾驶模型。值得注意的是，我们的单目、非集成模型超越了CARLA Leaderboard上表现最佳的方法，取得了更低的违规分数和最高的路线完成率，同时确保了可解释性。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
