Explainable AI for Safe and Trustworthy Autonomous Driving: A Systematic Review
This work tackles the problem of ensuring safety and trust in AI-driven autonomous vehicles for developers and regulators, but it is incremental as it synthesizes existing XAI methods into a framework.
The authors conducted a systematic review to address the challenge of safety assurance in autonomous driving by leveraging explainable AI (XAI) techniques, proposing a modular framework called SafeX to integrate XAI contributions for enhanced safety and trustworthiness.
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.