A Prioritized Trajectory Planning Algorithm for Connected and Automated Vehicle Mandatory Lane Changes

TL;DR

This paper presents a prioritized, system-optimal algorithm for mandatory lane changes in connected and automated vehicles, improving traffic efficiency and reducing computational time compared to traditional models.

Contribution

It introduces a novel cooperative lane change algorithm that prioritizes vehicles closer to the diverging zone and optimizes overall system travel time.

Findings

Increases traffic network efficiency with higher lane speeds.

Enables earlier lane change decisions for connected vehicles.

Reduces computational time compared to traditional gap acceptance models.

Abstract

We introduce a prioritized system-optimal algorithm for mandatory lane change (MLC) behavior of connected and automated vehicles (CAV) from a dedicated lane. Our approach applies a cooperative lane change that prioritizes the decisions of lane changing vehicles which are closer to the end of the diverging zone (DZ), and optimizes the predicted total system travel time. Our experiments on synthetic data show that the proposed algorithm improves the traffic network efficiency by attaining higher speeds in the dedicated lane and earlier MLC positions while ensuring a low computational time. Our approach outperforms the traditional gap acceptance model.