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Related Experiment Videos

Ethics-Aware Safe Reinforcement Learning for Rare-Event Risk Control in Interactive Urban Driving.

Dianzhao Li, Ostap Okhrin

    IEEE Transactions on Pattern Analysis and Machine Intelligence
    |June 29, 2026
    PubMed
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    This study introduces EthicAR, a Safe Reinforcement Learning (Safe RL) framework for autonomous vehicles. EthicAR enhances ethical decision-making to significantly reduce collisions and protect vulnerable road users.

    Area of Science:

    • Autonomous Systems
    • Robotics
    • Artificial Intelligence

    Background:

    • Autonomous vehicles (AVs) promise fewer traffic fatalities and improved efficiency.
    • Ethical reasoning is crucial for AV adoption, especially for protecting vulnerable road users (VRUs).
    • Current AV systems often lack robust ethical frameworks for complex scenarios.

    Purpose of the Study:

    • To develop and evaluate a hierarchical Safe Reinforcement Learning (Safe RL) framework, named EthicAR, for autonomous driving.
    • To integrate ethics-aware cost signals into AV decision-making processes.
    • To enhance the safety and ethical accountability of AVs in human-mixed traffic.

    Main Methods:

    • A hierarchical Safe RL framework augmenting standard driving objectives with ethics-aware cost signals.

    Related Experiment Videos

  • A composite ethical risk cost combining collision probability and harm severity for decision-level training.
  • Risk-sensitive prioritized experience replay and Temporal Cost Aggregation (TCA) for improved sample efficiency and tail-risk mitigation.
  • Polynomial trajectory generation with PID and Stanley controllers for execution-level control.

    • Main Results:

    • EthicAR decreased collision rates by 20-45% compared to baseline methods in simulations.
    • Task success rates and comfort metrics were maintained within 5-10% of baselines.
    • The framework demonstrated effective protection for vulnerable road users in diverse scenarios.

    Conclusions:

    • The proposed EthicAR framework offers a viable approach to embedding ethical reasoning in autonomous driving.
    • Combining formal control theory and data-driven learning advances ethically accountable autonomy.
    • This work provides a benchmark for Safe RL with ethics-aware objectives in real-world traffic simulations.