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Weakly Supervised Reinforcement Learning for Autonomous Highway Driving via Virtual Safety Cages.

Sampo Kuutti1, Richard Bowden2, Saber Fallah1

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Summary
This summary is machine-generated.

This study introduces rule-based safety cages to enhance reinforcement learning for autonomous vehicle control. These cages improve safety, training speed, and policy performance, even with suboptimal parameters.

Keywords:
advanced driver assistanceautonomous vehiclesmachine learningneural networksreinforcement learningsafetyvehicle control

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Area of Science:

  • Autonomous Systems
  • Artificial Intelligence
  • Control Theory

Background:

  • Neural networks and reinforcement learning (RL) are increasingly used for autonomous vehicle control.
  • The 'black box' nature of RL policies hinders trust and deployment in safety-critical applications.
  • Lack of interpretability in RL control policies is a major barrier for autonomous vehicle adoption.

Purpose of the Study:

  • To develop a reinforcement learning approach for autonomous vehicle longitudinal control.
  • To enhance safety and interpretability using rule-based safety cages as weak supervision.
  • To improve training convergence and the safety of the final control policy.

Main Methods:

  • Implemented a reinforcement learning agent for autonomous vehicle longitudinal control.
  • Integrated rule-based safety cages to provide weak supervision and enhance safety.
  • Compared RL models with and without safety cages, and with optimal vs. constrained parameters.

Main Results:

  • Weak supervision from safety cages consistently improved exploration safety, convergence speed, and model performance.
  • Safety cages enabled learning a safe driving policy even when RL alone failed with constrained parameters.
  • The rule-based supervisory controller offers interpretability, facilitating traditional validation and verification.

Conclusions:

  • Rule-based safety cages are effective for enhancing RL-based autonomous vehicle control.
  • This approach addresses the interpretability and safety challenges of deploying RL in autonomous vehicles.
  • The method allows for safe policy learning even under suboptimal or constrained model conditions.