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<p>The primary goal of artificial intelligence (AI) is to learn, mimic, and ultimately surpass human intelligence. Connectionist AI draws inspiration from human neuroscience and biological mechanisms and has made significant progress in many fields like autonomous driving (AD). However, the performance of AD systems in open, complex, and dynamic environments is still limited. Integrating robust and high-level human intelligence into AI remains a critical trend for its ongoing development and evolution. Inspired by this, this paper reviews Human Intelligence Augmented Artificial Intelligence (HIA-AI) from an AD perspective and offers a taxonomy of related methods, including learning from human demonstrations, tuning from human feedback, integrating from human mechanisms, and abstracting from human knowledge. It discusses current applications and innovations of related methods. Furthermore, this paper examines the characteristics, advantages, and disadvantages of various HIA-AI methods and points out potential future research directions.</p>

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A Survey of Human Intelligence Augmented Artificial Intelligence: An Autonomous Driving Perspective

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年8月20日

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  • Shangwen Li,
  • Kang Yuan,
  • Zihao Tang,
  • Yuanjian Zhang,
  • Peng Ji,
  • Quanbo Ge,
  • Shaoyi Du,
  • Yanjun Huang,
  • Hong Chen

摘要

The primary goal of artificial intelligence (AI) is to learn, mimic, and ultimately surpass human intelligence. Connectionist AI draws inspiration from human neuroscience and biological mechanisms and has made significant progress in many fields like autonomous driving (AD). However, the performance of AD systems in open, complex, and dynamic environments is still limited. Integrating robust and high-level human intelligence into AI remains a critical trend for its ongoing development and evolution. Inspired by this, this paper reviews Human Intelligence Augmented Artificial Intelligence (HIA-AI) from an AD perspective and offers a taxonomy of related methods, including learning from human demonstrations, tuning from human feedback, integrating from human mechanisms, and abstracting from human knowledge. It discusses current applications and innovations of related methods. Furthermore, this paper examines the characteristics, advantages, and disadvantages of various HIA-AI methods and points out potential future research directions.