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Leveraging LiDAR-Based Simulations to Quantify the Complexity of the Static Environment for Autonomous Vehicles in

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  • 1Department of Civil and Environmental Engineering, Faculty of Engineering, University of Alberta, Edmonton, AB T6G 1H9, Canada.

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Autonomous vehicles (AVs) face increased environmental complexity due to roadside features, impacting processing needs. Heavy rain significantly elevates AV processing demands, highlighting infrastructure needs for safe navigation.

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Area of Science:

  • Environmental Science
  • Robotics
  • Computer Science

Background:

  • Autonomous vehicles (AVs) require sophisticated environmental perception for safe navigation.
  • Understanding environmental complexity is crucial for AV system design and performance evaluation.

Purpose of the Study:

  • To quantify the impact of environmental factors on the complexity of scenes encountered by autonomous vehicles.
  • To develop a framework for estimating real-time data processing requirements for AVs.

Main Methods:

  • Utilized virtual simulations (VISTA simulator) to model vehicle-environment interactions.
  • Dissected roadways into relevant road features (RRFs) and full environment (FE) to analyze scene complexity.
  • Quantified processing requirements based on environmental variables like roadside features, road geometry, and weather conditions.

Main Results:

  • Roadside features increased environmental complexity by up to 400%.
  • Adding a lane increased processing requirements by 12.3-16.5%; crest curves caused 4.2% data loss, while sag curves increased complexity by 7%.
  • Heavy rain increased AV processing demands by 240% compared to normal conditions.

Conclusions:

  • Environmental complexity significantly impacts AV processing demands, with roadside features and adverse weather being major contributors.
  • Findings provide critical data for AV developers to optimize designs and for agencies to plan infrastructure.
  • Addressing environmental complexity is essential for the safe and effective deployment of autonomous vehicles.