Explainable AI for Safe and Trustworthy Autonomous Driving: A Systematic
  Review
Anton Kuznietsov Balint Gyevnar Cheng Wang Steven Peters Stefano V. Albrecht
Artificial Intelligence (AI) shows promising applications for the perception
and planning tasks in autonomous driving (AD) due to its superior performance
compared to conventional methods. However, inscrutable AI systems exacerbate
the existing challenge of safety assurance of AD. One way to mitigate this
challenge is to utilize explainable AI (XAI) techniques. To this end, we
present the first comprehensive systematic literature review of explainable
methods for safe and trustworthy AD. We begin by analyzing the requirements for
AI in the context of AD, focusing on three key aspects: data, model, and
agency. We find that XAI is fundamental to meeting these requirements. Based on
this, we explain the sources of explanations in AI and describe a taxonomy of
XAI. We then identify five key contributions of XAI for safe and trustworthy AI
in AD, which are interpretable design, interpretable surrogate models,
interpretable monitoring, auxiliary explanations, and interpretable validation.
Finally, we propose a modular framework called SafeX to integrate these
contributions, enabling explanation delivery to users while simultaneously
ensuring the safety of AI models.