Adaptive Leading Cruise Control in Mixed Traffic Considering Human Behavioral Diversity

TL;DR

This paper develops an adaptive cruise control strategy for connected vehicles that considers human driver diversity, leading to improved energy efficiency in mixed traffic through real-time learning and optimization.

Contribution

It introduces a unified optimization framework that accounts for human behavioral diversity and uses reinforcement learning to enhance energy efficiency in mixed traffic.

Findings

Holistic energy efficiency improved by 4.38% on average.

CAVs reduce energy consumption of HDVs by avoiding unnecessary acceleration and braking.

Real-time learning predicts human driver behavior to optimize vehicle control.

Abstract

This paper presents an adaptive leading cruise control strategy for the connected and automated vehicle (CAV) and first considers its impact on the following human-driven vehicle (HDV) with diverse driving characteristics in the unified optimization framework for improved holistic energy efficiency. The car-following behaviors of HDV are statistically calibrated using the Next Generation Simulation dataset. In a typical single-lane car-following scenario where CAVs and HDVs share the road, the longitudinal speed control of CAVs can substantially reduce the energy consumption of the following HDV by avoiding unnecessary acceleration and braking. Moreover, apart from the objectives including car-following safety and traffic efficiency, the energy efficiencies of both CAV and HDV are incorporated into the reward function of reinforcement learning. The specific driving pattern of the following HDV is learned in real-time from historical speed information to predict its acceleration and power consumption in the optimization horizon. A comprehensive simulation is conducted to statistically verify the positive impacts of CAV on the holistic energy efficiency of the mixed traffic flow with uncertain and diverse human driving behaviors. Simulation results indicate that the holistic energy efficiency is improved by 4.38% on average.