# Bi-Level Control of Weaving Sections in Mixed Traffic Environments with Connected and Automated Vehicles

arXiv: https://arxiv.org/abs/2403.16225 · HTML: https://mlsanity.com/paper/2403.16225/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Mar 2024.

## Built on by (most cited first, 1 of 1)

- [A Systematic Review and Taxonomy of Reinforcement Learning-Model Predictive Control Integration for Linear Systems](https://mlsanity.com/paper/2604.21030/) (2026)

## Abstract (arXiv)

Connected and automated vehicles (CAVs) can be beneficial for improving the operation of highway bottlenecks such as weaving sections. This paper proposes a bi-level control approach based on an upper-level deep reinforcement learning controller and a lower-level model predictive controller to coordinate the lane-changings of a mixed fleet of CAVs and human-driven vehicles (HVs) in weaving sections. The upper level represents a roadside controller that collects vehicular information from the entire weaving section and determines the control weights used in the lower-level controller. The lower level is implemented within each CAV, which takes the control weights from the upper-level controller and generates the acceleration and steering angle for individual CAVs based on the local situation. The lower-level controller further incorporates an HV trajectory predictor, which is capable of handling the dynamic topology of vehicles in weaving scenarios with intensive mandatory lane changes. The case study inspired by a real weaving section in Basel, Switzerland, shows that our method consistently outperforms state-of-the-art benchmarks.