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End-to-End Automated Lane-Change Maneuvering Considering Driving Style Using a Deep Deterministic Policy Gradient

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Summary

This study introduces a deep deterministic policy gradient (DDPG) algorithm for automated lane changing using lidar data. The method effectively balances safety, comfort, and efficiency, adapting to different driving styles and traffic conditions.

Keywords:
automated lane changedeep deterministic policy gradientdriving styleintelligent vehiclereinforcement learning

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Area of Science:

  • Robotics and Autonomous Systems
  • Artificial Intelligence
  • Transportation Engineering

Background:

  • Automated lane changing is crucial for vehicle autonomy but challenging in unpredictable environments.
  • Existing rule-based systems lack adaptability to real-world complexities.
  • Reinforcement learning offers a promising approach for developing more robust automated driving systems.

Purpose of the Study:

  • To develop an end-to-end automated lane-changing method using deep deterministic policy gradient (DDPG) and lidar data.
  • To incorporate safety, comfort, and efficiency considerations into the lane-changing policy.
  • To investigate the influence of different driving styles on the lane-changing process.

Main Methods:

  • Utilized a deep deterministic policy gradient (DDPG) algorithm for lane-changing control.
  • Defined state space using lidar-derived lane boundary and vehicle proximity data.
  • Defined continuous action space with steering wheel angle and longitudinal acceleration.
  • Designed a reward function incorporating collision avoidance, driving style, comfort (jerk, angular velocity), and efficiency (speed, lane-change time).
  • Developed a simulation environment using Pyglet for training and evaluation.

Main Results:

  • Trained autonomous vehicles demonstrated the ability to learn automated lane-changing policies considering safety, comfort, and efficiency.
  • The DDPG-based method showed robustness across varying speeds and traffic densities.
  • Autonomous vehicles successfully reflected distinct driving style differences during lane changes.
  • The system achieved effective combined lateral and longitudinal control.

Conclusions:

  • The proposed DDPG-based method provides an effective approach for automated lane changing using lidar data.
  • The approach successfully integrates safety, comfort, and efficiency, while accommodating diverse driving styles.
  • The developed system exhibits strong robustness in complex traffic scenarios, paving the way for more advanced autonomous driving capabilities.