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Information enters the brain through encoding, which is the input of information into the memory system. Once sensory information is received from the environment, the brain labels or codes it. The information is then organized with similar information and connected to existing concepts. Encoding occurs through automatic processing and effortful processing.
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Empowering safer socially sensitive autonomous vehicles using human-plausible cognitive encoding.

Hongliang Lu1,2, Meixin Zhu3, Chao Lu4

  • 1Intelligent Transportation Thrust, Systems Hub, The Hong Kong University of Science and Technology (Guangzhou), Guangzhou 511453, Guangdong, China.

Proceedings of the National Academy of Sciences of the United States of America
|May 19, 2025
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Summary

Autonomous vehicles (AVs) can now make more ethical decisions. A new scheme improves safety by considering individual road user impacts and their collective effect, especially for vulnerable users.

Keywords:
autonomous vehicleethical decision-makinghuman-plausible cognitive encodingsocially sensitive behavior

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Area of Science:

  • Robotics
  • Artificial Intelligence
  • Neuroscience
  • Ethics

Background:

  • Autonomous vehicles (AVs) are increasingly common, necessitating ethical decision-making capabilities.
  • Current AVs lack social sensitivity, failing to consider the distinct yet interrelated impacts of multiple road users.
  • Integrating ethical considerations into AV operation is crucial for safe and socially acceptable deployment.

Purpose of the Study:

  • To propose a novel scheme for AV ethical decision-making that incorporates social concern and human-plausible cognitive encoding.
  • To enable AVs to differentiate risk assessment based on road user categories.
  • To achieve a holistic ethical decision-making process by encoding individual impacts into a collective behavioral belief.

Main Methods:

  • Developed a scheme combining social concern and cognitive encoding for AV ethical decision-making.
  • Assessed individual road user impact based on risk and categorized users.
  • Holistically encoded independent impacts into a behavioral belief to support ethical decisions.
  • Evaluated the scheme using two thousand benchmark scenarios from CommonRoad.

Main Results:

  • The proposed scheme led to safer and more ethical AV decisions.
  • Overall risk was reduced by 26.3%, with a 22.9% decrease for vulnerable road users.
  • In accident scenarios, self-protection improved by 8.3%, overall road user protection by 17.6%, and vulnerable road user protection by 51.7%.

Conclusions:

  • The human-inspired scheme renders AVs socially sensitive, addressing ethical challenges in driving.
  • The approach enables differentiated consideration of road users and a holistic view of collective impact.
  • This work paves the way for more responsible and human-like ethical decision-making in autonomous vehicles.