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A Conceptual Multi-Layer Framework for the Detection of Nighttime Pedestrian in Autonomous Vehicles Using Deep

Muhammad Shoaib Farooq1, Haris Khalid1, Ansif Arooj2

  • 1Department of Computer Science, School of Systems and Technology, University of Management and Technology, Lahore 54000, Pakistan.

Entropy (Basel, Switzerland)
|January 21, 2023
PubMed
Summary

This study introduces a Deep Reinforcement Learning (DRL) framework using Scale Invariant Faster Region-based Convolutional Neural Networks (SIFRCNN) for nighttime pedestrian detection. The system improves autonomous vehicle safety by accurately identifying pedestrians in challenging low-light conditions.

Keywords:
advanced driving systemautonomous vehicledeep learningintelligent driving systemneural networkreinforcement learning

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Area of Science:

  • Computer Vision
  • Artificial Intelligence
  • Autonomous Systems

Background:

  • Autonomous vehicles face significant challenges in detecting pedestrians, particularly at night, which is crucial for preventing accidents.
  • Existing Advanced Driver-Assistance Systems (ADAS) often lack robust nighttime pedestrian detection capabilities.
  • Accurate pedestrian activity recognition is a complex computer vision task, especially in adverse lighting.

Purpose of the Study:

  • To propose a novel framework for efficient and accurate nighttime pedestrian detection for autonomous vehicles.
  • To enhance the safety of autonomous driving by improving the vehicle's ability to perceive pedestrians in low-light conditions.
  • To leverage Deep Reinforcement Learning (DRL) and advanced Convolutional Neural Networks (CNNs) for superior pedestrian detection.

Main Methods:

  • A framework combining Deep Reinforcement Learning (DRL) with Scale Invariant Faster Region-based Convolutional Neural Networks (SIFRCNN) was developed.
  • The Scale Invariant Faster Region-based Convolutional Neural Network (SIFRCNN) incorporates Region Proposal Network (RPN) computation to reduce detection time.
  • Reinforcement Learning (RL) was employed to optimize Q-values and train the system for maximizing rewards based on environmental states.

Main Results:

  • The proposed SIFRCNN framework achieved near-real-time object detection from road images.
  • Experimental results on KAIST, City Person, and Caltech datasets demonstrated an average improvement of 2.3% in reducing the miss rate of nighttime pedestrian detection.
  • The system effectively trains autonomous vehicles as agents to learn from their environment and maximize rewards.

Conclusions:

  • The developed SIFRCNN framework offers a significant advancement in nighttime pedestrian detection for autonomous vehicles.
  • This approach enhances vehicle perception and decision-making capabilities in complex, low-light driving scenarios.
  • The integration of DRL and SIFRCNN provides a robust solution for improving road safety and preventing pedestrian-vehicle collisions.