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Autonomous Driving Control Based on the Technique of Semantic Segmentation.

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Advanced Driver Assistance Systems (ADAS) have limitations. This study uses Deep Reinforcement Learning (DRL) in the CARLA simulator to achieve autonomous driving, outperforming traditional methods in complex scenarios.

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Area of Science:

  • Artificial Intelligence
  • Robotics
  • Computer Vision

Background:

  • Current Advanced Driver Assistance Systems (ADAS) are limited to simple driving scenarios.
  • Driver intervention is required in emergencies, posing safety risks.
  • The development of fully autonomous vehicles is a key research goal.

Purpose of the Study:

  • To develop and evaluate autonomous driving control using Deep Reinforcement Learning (DRL).
  • To compare the performance of different DRL models, specifically Deep Deterministic Policy Gradient (DDPG) and Recurrent Deterministic Policy Gradient (RDPG).
  • To investigate the impact of sensor input, namely Red-Green-Blue (RGB) cameras and semantic segmentation cameras, on autonomous driving performance.

Main Methods:

  • Implementation of autonomous driving control within the CARLA simulator.
  • Utilizing RGB and semantic segmentation camera feeds as input for DRL models.
  • Training and evaluating DDPG and RDPG algorithms with a custom reward mechanism.
  • Testing model performance on driving scenarios not encountered during training.

Main Results:

  • Recurrent Deterministic Policy Gradient (RDPG) strategies successfully completed driving missions in unseen scenarios.
  • The use of semantic segmentation cameras significantly improved the efficiency of the RDPG control strategy.
  • Deep Deterministic Policy Gradient (DDPG) models showed limitations in handling novel driving situations.

Conclusions:

  • RDPG demonstrates superior capability for robust autonomous driving control compared to DDPG.
  • Semantic segmentation enhances the effectiveness of DRL-based autonomous driving systems.
  • This research contributes to the advancement of AI-driven autonomous vehicle technology.