Evaluation of the Energy Efficiency in a Mixed Traffic with Automated Vehicles and Human Controlled Vehicles

TL;DR

This study evaluates how mixed traffic with human-controlled vehicles affects the energy efficiency of connected and automated vehicles, highlighting the importance of interaction modeling for fuel economy improvements.

Contribution

It introduces a novel lane changing model based on naturalistic data to simulate human driver behavior in mixed traffic scenarios involving CAVs.

Findings

Proper handling of cut-in vehicles improves CAV fuel economy

Human driver behavior significantly impacts CAV energy consumption

Simulation results demonstrate potential for energy efficiency gains

Abstract

The energy efficiency of Connected and Automated Vehicles (CAVs) is significantly influenced by surrounding road users. This paper presents the evaluation of energy efficiency of CAVs in a mixed traffic interacted with human controlled vehicles. To simulate the interaction between the CAVs and the cut-in vehicles controlled by human drivers near the intersection, a lane changing model is proposed to emulate the politeness and patience characteristics of the human driver. The proposed lane changing model is then calibrated based on over 100,000 naturalistic lane changing events collected by the University of Michigan Safety Pilot Model Deployment Program. A case study on simulation of the cut-in scenario is carried out to demonstrate the human driver's lane changing sensitivity under different driving trajectories of a frontal CAV and the influence on the energy consumption of the CAV due to the cut-in vehicle is evaluated. The simulation results indicate that the fuel economy of the CAV can be substantially improved if its surrounding cut-in vehicles can be well handled.