Autonomous driving has achieved significant milestones in research and development over the last decade. There is increasing interest in the field as the deployment of self-operating vehicles promises safer and more ecologically friendly transportation systems. With the rise of computationally powerful artificial intelligence (AI) techniques, autonomous vehicles can sense their environment with high precision, make safe real-time decisions, and operate reliably without human intervention. However, intelligent decision-making in autonomous cars 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, the AI systems of autonomous vehicles also need to explain how their decisions are constructed in order to be regulatory compliant across many jurisdictions. Our study sheds comprehensive light on the development of explainable artificial intelligence (XAI) approaches for autonomous vehicles. In particular, we make the following contributions. First, we provide a thorough overview of the state-of-the-art studies on XAI for autonomous driving. We then propose an XAI framework that considers the societal and legal requirements for the explainability of autonomous driving systems. Finally, as future research directions, we provide several XAI approaches that can improve operational safety and transparency to support public approval of autonomous driving technology by regulators and engaged stakeholders.

翻译：自动驾驶技术在过去十年间在研究与开发领域取得了重要里程碑。随着自主驾驶车辆承诺提供更安全、更环保的交通系统，该领域的研究兴趣日益增长。借助计算能力强大的人工智能技术，自动驾驶车辆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，当前技术水平的自动驾驶汽车中智能决策过程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，自动驾驶车辆的人工智能系统还需解释其决策构建过程，以满足多个司法管辖区的监管合规要求。本研究全面探讨了面向自动驾驶车辆的可解释人工智能方法发展。具体而言，我们做出以下贡献：首先，全面概述了当前面向自动驾驶的可解释人工智能研究现状；其次，提出了考虑社会与法律要求的自动驾驶系统可解释性框架；最后，作为未来研究方向，我们提供了若干可解释人工智能方法，这些方法能够提升操作安全性与透明度，以支持监管机构及利益相关方对自动驾驶技术的公众认可。