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Modules and Techniques for Motion Planning: An Industrial Perspective.

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Summary
This summary is machine-generated.

This study details the development and field testing of a complete autonomous driving system. The research optimizes data fusion and path planning using parallel computing for real-time, accurate navigation.

Keywords:
autonomous drivingmany-core architecturespath planningsensor data and map manipulationsoftware engineering

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Area of Science:

  • Automotive Engineering
  • Robotics
  • Computer Science

Background:

  • Autonomous driving systems are a key research area in the automotive industry.
  • Significant challenges remain in methodology and practical application for self-driving technology.
  • Industrial implementation requires robust solutions for data processing, communication, and computation.

Purpose of the Study:

  • To present an industrial experience in building and testing a complete autonomous driving system.
  • To address critical issues in data fusion, map manipulation, and inter-component communication.
  • To propose efficient strategies for real-time path planning and state-space exploration.

Main Methods:

  • Development of a full autonomous driving system from sensors to vehicle control.
  • Implementation of data fusion and map manipulation techniques for reality representation.
  • Focus on communication and synchronization between data-fusion, path-planner, CPU, GPU, and CUDA kernels.
  • Adoption of parallel many-core devices (CUDA-based GPGPU) for computational acceleration.
  • Utilization of rapid exploration of random trees for state-space exploration along reference paths.
  • Testing with a controller and vehicle simulator in real-world scenarios.

Main Results:

  • Proposed representation strategies and approximation techniques yield significant savings in memory and transfer times with minimal accuracy loss.
  • CUDA-based GPGPU implementation effectively reduces the computational burden for path planning.
  • Experiments demonstrate the generation of reasonable paths for standard maneuvers in real-time.
  • Reported relative errors and computation times for various settings.

Conclusions:

  • The developed autonomous driving system demonstrates practical applicability and real-time performance.
  • Efficient data handling and parallel processing are crucial for successful autonomous vehicle implementation.
  • The proposed methods offer a viable approach for real-time path generation in complex driving scenarios.