---
title: Car-Following-Models/CACC
permalink: /Car-Following-Models/CACC/
---

## Overview

The integrated CACC car-following model is based on the work of Milanés
& Shladover [\[1\]](#references), Xiao, Wang & van Arem [\[2\]](#references) and Xiao, Wang, Schakel &
van Arem [\[3\]](#references), wherein the developed control law in the ACC control
algorithm is explicitly divided into three modes: (i) speed (or
cruising) control, (ii) gap-closing control and (iii) gap control. A
fourth mode (i.e. collision avoidance mode) has been introduced within
the project [TransAID](https://www.transaid.eu).

## Speed control mode

The speed control mode is designed to maintain the pre-defined by the
driver desired speed and is activated when there are no preceding
vehicles in the range covered by the sensors or when the time-gap is
larger than 2 s.

## Gap control mode

The gap control mode aims to maintain a constant time gap between the
CACC-equipped vehicle and its predecessor and is activated when the gap
and speed deviations (with respect to the preceding vehicle) are
concurrently smaller than 0.2 m and 0.1 m/s, respectively.

## Gap-closing control mode

The gap-closing controller enables the smooth transition from speed
control mode to gap control mode and is triggered when the time-gap is
less than 1.5 s. If the time-gap is between 1.5 s and 2 s, the
CACC-equipped vehicle retains the previous control strategy to provide
hysteresis in the control loop and perform a smooth transfer between the
two strategies.

## Collision avoidance control mode

The collision avoidance mode prevents rear-end collisions when safety
critical conditions prevail. This mode is activated when the time-gap is
less than 1.5 s and the gap deviation is negative.

## Notes

- The implemented model can be found in [{{SUMO}}/src/microsim/cfmodels/MSCFModel_CACC.cpp]({{Source}}src/microsim/cfmodels/MSCFModel_CACC.cpp).
- This part of SUMO was developed and extended within the project
  [TransAID](https://www.transaid.eu).
- The model is primarily intended for use in specific traffic
  situations.

## References

1.  Milanés, V., & Shladover, S. E. (2014). Modeling cooperative and
    autonomous adaptive cruise control dynamic responses using
    experimental data. Transportation Research Part C: Emerging
    Technologies, 48, pp. 285–300.
    <https://doi.org/10.1016/j.trc.2014.09.001>
2.  Xiao, L., Wang, M., & van Arem, B. (2017). Realistic Car-Following
    Models for Microscopic Simulation of Adaptive and Cooperative
    Adaptive Cruise Control Vehicles. Transportation Research Record:
    Journal of the Transportation Research Board, 2623, pp. 1–9.
    <https://doi.org/10.3141/2623-01>
3.  Xiao, L., Wang, M., Schakel, W., & van Arem, B. (2018). Unravelling
    effects of cooperative adaptive cruise control deactivation on
    traffic flow characteristics at merging bottlenecks. Transportation
    Research Part C: Emerging Technologies, 96, 380–397.
    <https://doi.org/10.1016/j.trc.2018.10.008>