Distributed Maneuver Planning with Connected and Automated Vehicles for Boosting Traffic Efficiency

TL;DR

This paper introduces a distributed predictive control framework for connected and automated vehicles that enhances traffic efficiency, reduces energy consumption, and improves lane utilization through explicit coordination in mixed traffic environments.

Contribution

It proposes a novel two-dimensional maneuver planner with distributed implementation, enabling coordinated control of CAVs in mixed traffic at various penetration levels.

Findings

Improved traffic flow and lane utilization in simulations.

Reduced energy consumption with higher CAV penetration.

Outperforms baseline and benchmark planners in various scenarios.

Abstract

Connected and automated vehicles (CAVs) have the potential to improve traffic throughput and achieve a more efficient utilization of the available roadway infrastructure. They also have the potential to reduce energy consumption through traffic motion harmonization, even when operating in mixed traffic with other human-driven vehicles. The key to realizing these potentials are coordinated control schemes that can be implemented in a distributed manner with the CAVs. In this paper, we propose a distributed predictive control framework that features a two-dimensional maneuver planner incorporating explicit coordination constraints between connected vehicles operating in mixed traffic at various penetration levels. The framework includes a distributed implementation of a reference speed assigner that estimates local traffic speed from on-board measurements and communicated information. We present an extensive evaluation of the proposed framework in traffic micro-simulations at various CAV penetrations from traffic flow, energy use, and lane utilization points of view. Results are compared to a baseline scenario with no CAVs, as well as, a benchmark one-dimensional planner.