Human-like Driving: A Comprehensive Survey from Depth and Breadth

Recent years have witnessed a burgeoning paradigm shift in transportation, in which autonomous vehicles (AVs) are reshaping conventional commuting modes with an overwhelming momentum. Yet we are not ready to embrace this change fully. The ”unfamiliar” behavior of AVs is rendering themselves so unpredictable for conventional road users that the public distrusts or even deliberately refuses them, which many severe conflicts could surge accordingly. Therefore, human-like driving is proposed to help AVs seamlessly integrate into such long-standing transportation by behaving like real drivers. However, a comprehensive survey about human-like driving is still lacking today. In this paper, we explore current human-like driving studies from two brand-new perspectives: 1) depth, which focuses on the affordance of humans themselves to answer where human-like driving stems from, and 2) breadth, which grasps a comprehensive practical roadmap through the lens of technological actualization. In doing so, we can inform potential developers with a holistic picture of how to develop human-like driving and what aspects should be considered. This will contribute to a deeper understanding of human-like driving technologies in the future. This illuminates an in-depth discussion in human-like driving—the ‘reality alignment’ between AVs and human drivers. To the end, we underscore a multi-disciplinary avenue that holds great promise for reaching this target.

Recommended citation: H. Lu, J. Yang, Y. Liu, S. Shen, H. Liu, X. Zheng, and H. Yang, “Human-like driving: A comprehensive survey from depth and breadth,” Artificial Intelligence for Transportation, vol. 3, p. 100033, 2025. https://www.sciencedirect.com/science/article/pii/S305086062500033X