Recent advancements in autonomous driving have seen a paradigm shift towards end-to-end learning paradigms, which map sensory inputs directly to driving actions, thereby enhancing the robustness and adaptability of autonomous vehicles. However, these models often sacrifice interpretability, posing significant challenges to trust, safety, and regulatory compliance. To address these issues, we introduce DRIVE -- Dependable Robust Interpretable Visionary Ensemble Framework in Autonomous Driving, a comprehensive framework designed to improve the dependability and stability of explanations in end-to-end unsupervised autonomous driving models. Our work specifically targets the inherent instability problems observed in the Driving through the Concept Gridlock (DCG) model, which undermine the trustworthiness of its explanations and decision-making processes. We define four key attributes of DRIVE: consistent interpretability, stable interpretability, consistent output, and stable output. These attributes collectively ensure that explanations remain reliable and robust across different scenarios and perturbations. Through extensive empirical evaluations, we demonstrate the effectiveness of our framework in enhancing the stability and dependability of explanations, thereby addressing the limitations of current models. Our contributions include an in-depth analysis of the dependability issues within the DCG model, a rigorous definition of DRIVE with its fundamental properties, a framework to implement DRIVE, and novel metrics for evaluating the dependability of concept-based explainable autonomous driving models. These advancements lay the groundwork for the development of more reliable and trusted autonomous driving systems, paving the way for their broader acceptance and deployment in real-world applications.

翻译：近期自动驾驶领域的研究呈现出向端到端学习范式转变的趋势，该范式将感知输入直接映射为驾驶动作，从而提升了自动驾驶车辆的鲁棒性与适应性。然而，此类模型往往以牺牲可解释性为代价，对信任建立、安全保障及法规遵从构成了重大挑战。为解决这些问题，我们提出了DRIVE——自动驾驶中可靠、鲁棒、可解释的预见性集成框架，这是一个旨在提升端到端无监督自动驾驶模型解释的可靠性与稳定性的综合框架。我们的工作特别针对在"概念网格化驾驶"模型中观察到的固有稳定性问题，这些问题削弱了其解释与决策过程的可信度。我们定义了DRIVE的四个关键属性：一致可解释性、稳定可解释性、一致输出与稳定输出。这些属性共同确保了解释在不同场景与扰动下保持可靠与鲁棒。通过大量实证评估，我们证明了该框架在提升解释稳定性与可靠性方面的有效性，从而解决了现有模型的局限性。我们的贡献包括：对DCG模型中可靠性问题的深入分析、DRIVE框架及其基本属性的严格定义、实现DRIVE的框架体系，以及用于评估基于概念的可解释自动驾驶模型可靠性的新度量标准。这些进展为开发更可靠、更可信的自动驾驶系统奠定了基础，为其在现实应用中获得更广泛接受与部署铺平了道路。