A Comprehensive Eco-Driving Strategy for Connected and Autonomous Vehicles (CAVs) with Microscopic Traffic Simulation Testing Evaluation

TL;DR

This paper proposes a comprehensive eco-driving strategy for connected and autonomous vehicles that optimizes fuel efficiency through multiple driving modes and a high-level controller, validated via microscopic traffic simulation.

Contribution

It introduces a novel deterministic eco-driving controller with V2I and V2V capabilities, ensuring smooth mode transitions and significant fuel savings in simulated traffic environments.

Findings

Significant fuel economy improvements demonstrated in simulations.

Smooth transitions between driving modes without collisions.

Effective integration of V2I and V2V algorithms for eco-driving.

Abstract

In this paper, a comprehensive Eco-Driving strategy for CAVs is presented. In this setup, multiple driving modes calculate speed profiles ideal for their own set of constraints simultaneously to save fuel as much as possible, while a High Level (HL) controller ensures smooth transitions between the driving modes for Eco-Driving. This Eco-Driving deterministic controller for an ego CAV was equipped with Vehicle-to-Infrastructure (V2I) and Vehicle-to-Vehicle (V2V) algorithms. Simulation results are used to show that the HL controller ensures significant fuel economy improvement as compared to baseline driving modes with no collisions between the ego CAV and traffic vehicles while the driving mode of the ego CAV was set correctly under changing constraints.