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PD Controller: Design01:26

PD Controller: Design

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In automotive engineering, car suspension systems often employ Proportional Derivative (PD) controllers to enhance performance. PD controllers are utilized to adjust the damping force in response to road conditions. A controller, acting as an amplifier with a constant gain, demonstrates proportional control, with output directly mirroring input.
Designing a continuous-data controller requires selecting and linking components like adders and integrators, which are fundamental in Proportional,...
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SDC-Net: End-to-End Multitask Self-Driving Car Camera Cocoon IoT-Based System.

Sensors (Basel, Switzerland)ยท2022
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SDC-Net++: End-to-End Crash Detection and Action Control for Self-Driving Car Deep-IoT-Based System.

Mohammed Abdou Tolba1, Hanan Ahmed Kamal1

  • 1Department of Electronics and Communications Engineering, Faculty of Engineering, Cairo University, Cairo 12613, Egypt.

Sensors (Basel, Switzerland)
|June 27, 2024
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Summary

This study enhances the SDC-Net system for self-driving cars (SDCs) by enabling accident location detection. The improved SDC-Net++ system accurately identifies crash sites and shares this critical information via IoT, improving connected vehicle safety.

Keywords:
IoTautomatic emergency brakingautonomous drivingcamera-cocooncomputer visioncrash detectiondeep learningmultitask learningpath planningsystem

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Area of Science:

  • Computer Vision
  • Artificial Intelligence
  • Internet of Things (IoT)

Background:

  • Current self-driving car systems (SDCs) often lack the ability to precisely identify accident locations.
  • Existing deep learning models like SDC-Net can classify crash scenes but not pinpoint their exact location.
  • Collaboration between SDCs and IoT is crucial for advanced safety features.

Purpose of the Study:

  • To enhance the SDC-Net system for precise accident location identification.
  • To improve information sharing among connected vehicles regarding accident sites.
  • To develop a more robust system for autonomous driving safety.

Main Methods:

  • Replaced the classification network in SDC-Net with a detection network.
  • Adapted the CARLA simulator dataset to include vehicle bounding boxes for training.
  • Modified the IoT-based information sharing to include accident coordinates.
  • Developed the SDC-Net++ system for enhanced control actions and information dissemination.

Main Results:

  • The SDC-Net++ system successfully identifies accident locations and outputs relevant control actions.
  • Multitask networks with Bird's Eye View (BEV) input representations significantly outperformed other configurations.
  • SDC-Net++ demonstrated superior performance over SDC-Net in precision, recall, F1-score, accuracy, and reduced Mean Squared Error (MSE).

Conclusions:

  • The enhanced SDC-Net++ system provides accurate accident localization and improves safety for connected autonomous vehicles.
  • Multitask learning combined with BEV input is highly effective for accident detection and response.
  • The integration of IoT for real-time accident information sharing is vital for future SDC safety.