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Environmental-Driven Approach towards Level 5 Self-Driving.

Mohammad Hurair1, Jaeil Ju1, Junghee Han1

  • 1School of Electronics and Information Engineering, Korea Aerospace University, 76 Hanggongdaehang-ro, Goyang-si 412-791, Gyeonggi-do, Republic of Korea.

Sensors (Basel, Switzerland)
|January 23, 2024
PubMed
Summary
This summary is machine-generated.

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This study introduces an environment-driven framework for fully autonomous vehicles. It manages internal parameters to meet real-time deadlines, ensuring safe operation in complex driving scenarios.

Area of Science:

  • Robotics
  • Artificial Intelligence
  • Computer Science

Background:

  • Advancements in technology are driving the development of fully autonomous vehicles (level 5).
  • Achieving level 5 autonomous driving requires significant research beyond level 4, particularly for safe operation in complex environments.
  • Real-time performance is critical, as sensor systems and machine learning algorithms in autonomous cars must meet strict end-to-end delay deadlines.

Purpose of the Study:

  • To propose a novel framework for autonomous cars to ensure end-to-end delay deadlines.
  • To address the computational intensity of machine learning modules in autonomous driving systems.
  • To maintain driving accuracy while meeting performance requirements.

Main Methods:

  • An environment-driven approach was developed, distinguishing between uncontrollable environmental factors and controllable internal factors (e.g., sensing frequency, image resolution, prediction rate, car speed).
Keywords:
autonomous drivingend-to-end delaylevel 5machine learningrealtime

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  • An admission control module was designed to regulate internal parameters like image resolution and detection period.
  • The framework determines the acceptability of parameters for meeting end-to-end deadlines based on the current environmental scenario.

    • Main Results:

    • The proposed framework successfully manages internal parameters to meet end-to-end deadlines.
    • The system demonstrated its ability to maintain autonomous driving accuracy under various conditions.
    • Verification was performed using both a remote-controlled (RC) car and a simulation environment.

    Conclusions:

    • The environment-driven framework offers a viable solution for ensuring timely performance in autonomous vehicles.
    • Controlling internal parameters based on environmental context is key to achieving reliable level 5 autonomous driving.
    • The approach provides a method for balancing computational load and driving accuracy in complex autonomous systems.