Incorporating lane-change prediction into energy-efficient speed control of connected autonomous vehicles at intersections

TL;DR

This paper introduces a lane-change prediction integrated into a speed control strategy for connected autonomous vehicles, significantly improving energy efficiency at intersections by anticipating lane changes 4-6 seconds ahead.

Contribution

It develops a novel traffic flow model that predicts lane changes and incorporates this into an energy-efficient speed control framework for CAVs.

Findings

Up to 13% energy savings achieved.

Prediction of lane changes 4-6 seconds in advance improves control.

Enhanced traffic flow modeling with lane change considerations.

Abstract

Connected and autonomous vehicles (CAVs) possess the capability of perception and information broadcasting with other CAVs and connected intersections. Additionally, they exhibit computational abilities and can be controlled strategically, offering energy benefits. One potential control strategy is real-time speed control, which adjusts the vehicle speed by taking advantage of broadcasted traffic information, such as signal timings. However, the optimal control is likely to increase the gap in front of the controlled CAV, which induces lane changing by other drivers. This study proposes a modified traffic flow model that aims to predict lane-changing occurrences and assess the impact of lane changes on future traffic states. The primary objective is to improve energy efficiency. The prediction model is based on a cell division platform and is derived considering the additional flow during lane changing. An optimal control strategy is then developed, subject to the predicted trajectory generated for the preceding vehicle. Lane change prediction estimates future speed and gap of vehicles, based on predicted traffic states. The proposed framework outperforms the non-lane change traffic model, resulting in up to 13% energy savings when lane changing is predicted 4-6 seconds in advance.