Autonomous driving has achieved significant milestones in research and development over the last two decades. There is increasing interest in the field as the deployment of autonomous vehicles (AVs) promises safer and more ecologically friendly transportation systems. With the rapid progress in computationally powerful artificial intelligence (AI) techniques, AVs can sense their environment with high precision, make safe real-time decisions, and operate reliably without human intervention. However, intelligent decision-making in such vehicles is not generally understandable by humans in the current state of the art, and such deficiency hinders this technology from being socially acceptable. Hence, aside from making safe real-time decisions, AVs must also explain their AI-guided decision-making process in order to be regulatory compliant across many jurisdictions. Our study sheds comprehensive light on the development of explainable artificial intelligence (XAI) approaches for AVs. In particular, we make the following contributions. First, we provide a thorough overview of the state-of-the-art and emerging approaches for XAI-based autonomous driving. We then propose a conceptual framework that considers the essential elements for explainable end-to-end autonomous driving. Finally, we present XAI-based prospective directions and emerging paradigms for future directions that hold promise for enhancing transparency, trustworthiness, and societal acceptance of AVs.

翻译：自动驾驶在过去二十年中在研发领域取得了重要里程碑。随着自动驾驶汽车（AVs）有望实现更安全、更环保的交通系统，该领域的兴趣日益增长。随着计算能力强大的人工智能（AI）技术的迅速发展，AVs能够高精度感知环境、做出安全实时决策，并在无需人类干预的情况下可靠运行。然而，当前技术水平下，此类车辆的智能决策通常难以被人类理解，这一缺陷阻碍了该技术获得社会认可。因此，除了做出安全实时决策外，AVs还必须解释其AI引导的决策过程，以满足多个司法管辖区的合规要求。我们的研究全面揭示了面向AVs的可解释人工智能（XAI）方法的发展。具体而言，我们做出以下贡献：首先，我们全面概述了基于XAI的自动驾驶的最新与新兴方法。接着，我们提出了一个概念框架，考虑了可解释端到端自动驾驶的关键要素。最后，我们提出了基于XAI的未来方向和有前景的新范式，这些方向有望增强AVs的透明度、可信度及社会接受度。