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Knowledge Distillation-Enhanced Behavior Transformer for Decision-Making of Autonomous Driving.

Rui Zhao1, Yuze Fan1, Yun Li2

  • 1College of Automotive Engineering, Jilin University, Changchun 130025, China.

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|January 11, 2025
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Summary
This summary is machine-generated.

This study introduces KD-BeT, a new framework for autonomous driving behavior decision-making. It enhances Reinforcement Learning (RL) using Transformers and knowledge distillation for improved safety and efficiency.

Keywords:
autonomous drivingbehavior transformerdecision-makingimitation learningknowledge distillationreinforcement learning

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Area of Science:

  • Robotics
  • Artificial Intelligence
  • Machine Learning

Background:

  • Autonomous driving relies on behavior decision-making, bridging perception and control.
  • Imitation Learning (IL) and Reinforcement Learning (RL) are key approaches, but RL faces challenges in complex environments due to limited reasoning and sample efficiency.

Purpose of the Study:

  • To propose an innovative Knowledge Distillation-Enhanced Behavior Transformer (KD-BeT) framework.
  • To leverage Transformer's contextual reasoning for sequential decision-making in autonomous driving.
  • To improve RL's training efficiency and performance in complex driving scenarios.

Main Methods:

  • Introduced a Behavior Transformer as the policy network in RL, utilizing observation-action history.
  • Employed a teacher-student paradigm: a teacher model trained via IL, followed by knowledge distillation to accelerate RL.
  • Applied the KD-BeT framework to autonomous driving behavior decision-making.

Main Results:

  • KD-BeT demonstrated fast convergence and high asymptotic performance during training.
  • Outperformed state-of-the-art methods in CARLA NoCrash benchmark tests for traffic efficiency and driving safety.
  • Validated the effectiveness of knowledge distillation in enhancing RL for autonomous driving.

Conclusions:

  • The KD-BeT framework offers a novel and effective solution for autonomous driving behavior decision-making.
  • Successfully combined Transformer architecture with knowledge distillation to overcome RL limitations.
  • Achieved superior performance in traffic efficiency and driving safety, paving the way for real-world applications.