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A Comprehensive Eco-Driving Strategy for CAVs with Microscopic Traffic Simulation Testing Evaluation.

Ozgenur Kavas-Torris1, Levent Guvenc1

  • 1Automated Driving Lab, Department of Mechanical and Aerospace Engineering, The Ohio State University, Columbus, OH 43210, USA.

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A new Eco-Driving strategy for Connected and Autonomous Vehicles (CAVs) optimizes fuel economy by managing multiple driving modes. This system achieved a 6.41% fuel saving in simulations without collisions.

Keywords:
dynamic programmingeco-drivingecological cooperative adaptive cruise controltraffic simulationvelocity trajectory

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Area of Science:

  • * Intelligent Transportation Systems
  • * Automotive Engineering
  • * Sustainable Mobility

Background:

  • * Growing adoption of Connected and Autonomous Vehicles (CAVs) necessitates efficient energy management strategies.
  • * Existing Eco-Driving approaches often lack comprehensive integration of multiple driving modes and advanced communication.

Purpose of the Study:

  • * To present a comprehensive deterministic Eco-Driving strategy for CAVs.
  • * To enhance fuel economy through simultaneous optimization of speed profiles across various driving modes.
  • * To ensure safe and smooth transitions between modes using a High-Level controller.

Main Methods:

  • * Development of a deterministic Eco-Driving controller for CAVs incorporating Vehicle-to-Infrastructure (V2I) and Vehicle-to-Vehicle (V2V) communication.
  • * Implementation of multiple driving modes with simultaneous speed profile calculations based on individual constraints.
  • * Utilization of a High-Level (HL) controller for seamless mode transitions.
  • * Validation through microscopic traffic simulations to quantify fuel economy improvements.

Main Results:

  • * The HL controller demonstrated significant fuel economy improvements compared to baseline driving modes.
  • * The strategy ensured collision-free operation between the ego CAV and other traffic vehicles.
  • * Microscopic traffic simulations showed a 6.41% fuel economy improvement for the CAV under the proposed Eco-Driving strategy.
  • * The system effectively adapted the CAV's driving mode under changing environmental and traffic constraints.

Conclusions:

  • * The presented comprehensive deterministic Eco-Driving strategy effectively enhances fuel economy in CAVs.
  • * The integration of V2I and V2V communication, coupled with intelligent mode management, is crucial for efficient autonomous driving.
  • * The strategy offers a viable solution for sustainable transportation by reducing fuel consumption in autonomous vehicles.