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Heterogeneous CACC Coexistence: Simulation, Analysis, and Modeling

Lorenzo Ghiro, Marco Franceschini, Renato Lo Cigno, Michele Segata

TL;DR

The paper investigates the safety and performance implications of heterogeneous CACC coexistence by simulating mixed platoons formed from three established CACCs (Ploeg L, PATH P, GSBL G) alongside a conventional ACC baseline. It analyzes microscopic safety in isolated mixed platoons and macroscopic outcomes on a high-density ring road, assuming reliable V2X communication at 10 Hz and an actuation lag modeled as a first-order low-pass with a pole at $0.5\text{s}$. The study demonstrates that some CACC mixtures can operate robustly while others exhibit safety, comfort, or efficiency limitations, underscoring the need for careful system design and a formal framework to model heterogeneous platoons. The results motivate developing theoretical models for heterogeneous controller interactions and guide progressive, safe deployment of mixed CACC capabilities in real traffic.

Abstract

The design of Cooperative Adaptive Cruise Control (CACC) algorithms for vehicle platooning has been extensively investigated, leading to a wide range of approaches with different requirements and performance. Most existing studies evaluate these algorithms under the assumption of homogeneous platoons, i.e., when all platoon members adopt the same CACC. However, market competition is likely to result in vehicles from different manufacturers implementing distinct CACCs. This raises fundamental questions about whether heterogeneous vehicles can safely cooperate within a platoon and what performance can be achieved. To date, these questions have received little attention, as heterogeneous platoons are difficult to model and analyze. In this work, we introduce the concept of mixed platoons, i.e., platoons made of vehicles running heterogeneous CACCs, and we study their performance through simulation-based experiments. We consider mixtures of three well-established CACCs from the literature. In the first part of the paper, we study a single mixed platoon in isolation to understand the microscopic effects on safety: we evaluate the performance of various CACC-mixtures across speed change and emergency braking scenarios. In the second part, we examine a high-density ring-road scenario to assess macroscopic impacts on safety, comfort, and traffic throughput, especially comparing throughput results with those obtained from vehicles controlled by a standard Adaptive Cruise Control (ACC) or by human drivers. Our findings highlight that some combinations of CACCs can operate robustly and safely, while others exhibit critical limitations in safety, comfort, or efficiency. These results emphasize the need for careful system design and the development of theoretical frameworks for modeling heterogeneous platoons.

Heterogeneous CACC Coexistence: Simulation, Analysis, and Modeling

TL;DR

The paper investigates the safety and performance implications of heterogeneous CACC coexistence by simulating mixed platoons formed from three established CACCs (Ploeg L, PATH P, GSBL G) alongside a conventional ACC baseline. It analyzes microscopic safety in isolated mixed platoons and macroscopic outcomes on a high-density ring road, assuming reliable V2X communication at 10 Hz and an actuation lag modeled as a first-order low-pass with a pole at . The study demonstrates that some CACC mixtures can operate robustly while others exhibit safety, comfort, or efficiency limitations, underscoring the need for careful system design and a formal framework to model heterogeneous platoons. The results motivate developing theoretical models for heterogeneous controller interactions and guide progressive, safe deployment of mixed CACC capabilities in real traffic.

Abstract

The design of Cooperative Adaptive Cruise Control (CACC) algorithms for vehicle platooning has been extensively investigated, leading to a wide range of approaches with different requirements and performance. Most existing studies evaluate these algorithms under the assumption of homogeneous platoons, i.e., when all platoon members adopt the same CACC. However, market competition is likely to result in vehicles from different manufacturers implementing distinct CACCs. This raises fundamental questions about whether heterogeneous vehicles can safely cooperate within a platoon and what performance can be achieved. To date, these questions have received little attention, as heterogeneous platoons are difficult to model and analyze. In this work, we introduce the concept of mixed platoons, i.e., platoons made of vehicles running heterogeneous CACCs, and we study their performance through simulation-based experiments. We consider mixtures of three well-established CACCs from the literature. In the first part of the paper, we study a single mixed platoon in isolation to understand the microscopic effects on safety: we evaluate the performance of various CACC-mixtures across speed change and emergency braking scenarios. In the second part, we examine a high-density ring-road scenario to assess macroscopic impacts on safety, comfort, and traffic throughput, especially comparing throughput results with those obtained from vehicles controlled by a standard Adaptive Cruise Control (ACC) or by human drivers. Our findings highlight that some combinations of CACCs can operate robustly and safely, while others exhibit critical limitations in safety, comfort, or efficiency. These results emphasize the need for careful system design and the development of theoretical frameworks for modeling heterogeneous platoons.

Paper Structure

This paper contains 5 sections, 7 equations, 1 figure, 1 table.

Table of Contents

  1. Introduction
  2. Related Work
  3. CACCs Considered
  4. Controllers Included in the Study
  5. Mixed Platoons Formation

Figures (1)

  • Figure 1: Examples of control topology in platoons of five vehicles: Topology 1 $\{-, \texttt{L}\xspace, \texttt{L}\xspace, \texttt{L}\xspace, \texttt{L}\xspace \}$; Topology 2 $\{-, \texttt{P}\xspace, \texttt{P}\xspace, \texttt{P}\xspace, \texttt{P}\xspace \}$; Topology 3 $\{-, \texttt{P}\xspace, \texttt{L}\xspace, \texttt{P}\xspace, \texttt{P}\xspace \}$; Topology 4 $\{-, \texttt{P}\xspace, \texttt{G}\xspace, \texttt{G}\xspace, \texttt{P}\xspace \}$.