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Simulation of Automotive Radar Point Clouds in Standardized Frameworks
Simulation of Automotive Radar Point Clouds in Standardized Frameworks
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The simulation of the vehicle's environmental sensors, the so-called sensor simulation, is crucial for testing and validating autonomous driving. Automobile manufacturers are increasingly focusing on a standardized architecture with a high level of abstraction. In order to simulate the sensors, such as radar sensors, most realistically on a point cloud level, data-based methods are used in many cases. In general, and specifically in case of radar sensors, there are still challenges to be faced.…
  • Leidėjas:
  • ISBN-10: 3736975368
  • ISBN-13: 9783736975361
  • Formatas: 14.8 x 21 x 0.7 cm, minkšti viršeliai
  • Kalba: Anglų

Simulation of Automotive Radar Point Clouds in Standardized Frameworks (el. knyga) (skaityta knyga) | knygos.lt

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The simulation of the vehicle's environmental sensors, the so-called sensor simulation, is crucial for testing and validating autonomous driving. Automobile manufacturers are increasingly focusing on a standardized architecture with a high level of abstraction. In order to simulate the sensors, such as radar sensors, most realistically on a point cloud level, data-based methods are used in many cases. In general, and specifically in case of radar sensors, there are still challenges to be faced. Therefore, four research questions are addressed: Is it possible to generate synthetic training data for data-based models? Which statistical approaches are suitable to simulate radar point clouds and how shall their learning capacities be evaluated? Is there a modeling approach to circumvent the disadvantages of statistical modeling? How to tackle the statistical nature of radar sensors during validation?

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  • Autorius: Thomas Eder
  • Leidėjas:
  • ISBN-10: 3736975368
  • ISBN-13: 9783736975361
  • Formatas: 14.8 x 21 x 0.7 cm, minkšti viršeliai
  • Kalba: Anglų

The simulation of the vehicle's environmental sensors, the so-called sensor simulation, is crucial for testing and validating autonomous driving. Automobile manufacturers are increasingly focusing on a standardized architecture with a high level of abstraction. In order to simulate the sensors, such as radar sensors, most realistically on a point cloud level, data-based methods are used in many cases. In general, and specifically in case of radar sensors, there are still challenges to be faced. Therefore, four research questions are addressed: Is it possible to generate synthetic training data for data-based models? Which statistical approaches are suitable to simulate radar point clouds and how shall their learning capacities be evaluated? Is there a modeling approach to circumvent the disadvantages of statistical modeling? How to tackle the statistical nature of radar sensors during validation?

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