Demos for LCC in mixed traffic

In this project, we present a few demos of LCC in mixed traffic systems

Leading Cruise Control (LCC)

Leading Cruise Control is a general control framework for connected and autonomous vehicles (CAVs) in mixed traffic flow, where human-driven vehicles (HDVs) also exist. A schematic diagram is shown below. The blue arrows represent the communication topology of the CAV, while the purple arrows illustrate the interaction direction in HDVs' dynamics. The blue vehicles, gray vehicles and yellow vehicles represent CAVs, HDVs and the head vehicle, respectively.

Two special cases of LCC are Car-Driving LCC (CF-LCC) and Free-Driving LCC (FD-LCC). (Demo scenario: there are ten HDVs following the CAV, which only responds to the motion of the two HDVs directly behind)

Features

The CAV maintains car-following operations, adapting to the states of its preceding vehicles, and it also aims to lead the motion of its following vehicles. Specifically, by controlling of the CAV, LCC aims for both of the following two objectives:

1. attenuate downstream traffic perturbations;
2. smooth upstream traffic flow.

Python Implementations

For the Python implementation, please download the requirements.txt file for the necessary packages. Then, run the command pip install -r Python_Implementation/requirements.txt in the terminal.

Publications

1. Wang, J., Zheng, Y., Chen, C., Xu, Q., & Li, K. (2020). Leading Cruise Control in Mixed Traffic Flow: System Modeling, Controllability, and String Stability. arXiv preprint arXiv:2012.04313. link
2. Wang, J., Zheng, Y., Chen, C., Xu, Q., & Li, K. (2020). Leading cruise control in mixed traffic flow. 59th IEEE Conference on Decision and Control, 2020. link

See here for presentation slides.

Contacts

Relavent project: mixed-traffic (modeling and control of mixed traffic flow).