逆向行驶：在端到端自动驾驶模型中利用可解释性
Driving the Wrong Way: Leveraging Interpretability in End2End Autonomous Driving Models
AI总结：
研究端到端自动驾驶模型因复杂不透明易产生不良行为的问题，核心方法是集成无监督字典学习作为可解释性模块，主要贡献为揭示决策逻辑、纠正驾驶决策以提升模型性能。
摘要：
端到端学习在自动驾驶中的日益采用增加了模型复杂性和不透明性，带来学习不良或错误行为的风险。在这项工作中，我们将无监督字典学习作为事后可解释性模块集成到先进驾驶模型中，将驾驶行为分解为语义上有意义的概念，同时展示它们对模型驾驶决策的因果影响。我们提出了一个逐步框架，从端到端模型中提取和解释有意义的概念，并将它们与多方面的模型输出相连接，从而揭示预测未来轨迹的潜在决策逻辑。此外，在概念层面的针对性干预使我们能够操纵和纠正驾驶决策，从而在整体驾驶性能上带来可衡量的提升。我们证明了可解释性可有效用于降低模型不透明性、发现错误行为并实现有针对性的缓解，最终提升模型性能。
英文摘要：
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.
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