Human-in-the-Loop AI Models for Trustworthy Autonomous Driving Systems

Abstract

Trustworthiness in autonomous driving systems is paramount for ensuring safety, reliability, and user acceptance. While advances in AI and machine learning have significantly improved autonomous vehicle perception and decision-making, challenges remain related to transparency, robustness, and ethical concerns. Human-in-the-loop (HITL) AI models offer a promising approach by integrating human oversight, feedback, and intervention directly into the autonomous driving pipeline. This integration enables continuous learning, error correction, and decision validation, thereby enhancing system trustworthiness.

 This paper explores the design and implementation of HITL AI models tailored for autonomous driving systems. We propose a framework that allows human operators to interact with the AI system at critical decision points, such as ambiguous perception outputs or complex navigation scenarios. The framework incorporates real-time feedback loops, active learning mechanisms, and interpretable AI techniques to facilitate human understanding and influence over AI decisions.

 We evaluate the proposed HITL system using simulated and real-world driving scenarios, demonstrating improved safety outcomes and increased system transparency. Results indicate that human intervention can significantly reduce false positives/negatives in object detection and classification while enhancing the adaptability of the AI models to novel situations. Additionally, the inclusion of interpretable explanations increases operator confidence and trust in the system’s decisions.

 This work underscores the importance of combining human expertise with AI capabilities to build autonomous driving systems that are not only effective but also trustworthy. Future work will focus on optimizing human-AI collaboration, scaling HITL approaches across fleet operations, and developing standardized protocols for human intervention in autonomous vehicle control.