mlsanityData to 14 Oct 2026

Learning to Change: Choreographing Mixed Traffic Through Lateral Control and Hierarchical Reinforcement Learning

arXiv 2403.14879 ↗First posted 21 Mar 2024cs.RO cs.MAcited by 1

On the map: Deep reinforcement learning methods → RL for autonomous driving and traffic

Abstract (arXiv)

The management of mixed traffic that consists of robot vehicles (RVs) and human-driven vehicles (HVs) at complex intersections presents a multifaceted challenge. Traditional signal controls often struggle to adapt to dynamic traffic conditions and heterogeneous vehicle types. Recent advancements have turned to strategies based on reinforcement learning (RL), leveraging its model-free nature, real-time operation, and generalizability over different scenarios. We introduce a hierarchical RL framework to manage mixed traffic through precise longitudinal and lateral control of RVs. Our proposed hierarchical framework combines the state-of-the-art mixed traffic control algorithm as a high level decision maker to improve the performance and robustness of the whole system. Our experiments demonstrate that the framework can reduce the average waiting time by up to 54% compared to the state-of-the-art mixed traffic control method. When the RV penetration rate exceeds 60%, our technique consistently outperforms conventional traffic signal control programs in terms of the average waiting time for all vehicles at the intersection.

Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Placed in RL for autonomous driving and traffic, one of 15 directions in Deep reinforcement learning methods. Builds on 19 of its 34 references that are in the map. First posted on arXiv 21 Mar 2024.

Builds on

19 of 34 references.

References · 19

  1. 2017Proximal Policy Optimization Algorithmscited by 15,215
  2. 2016Semi-Supervised Classification with Graph Convolutional Networkscited by 11,361
  3. 2013Playing Atari with Deep Reinforcement Learningcited by 3,274
  4. 2017Multi-Agent Actor-Critic for Mixed Cooperative-Competitive Environmentscited by 1,558
  5. 2022A Review of Safe Reinforcement Learning: Methods, Theory and Applicationscited by 181
  6. 2017Flow: A Modular Learning Framework for Mixed Autonomy Trafficcited by 73
  7. 2018A Reinforcement Learning Based Approach for Automated Lane Change Maneuverscited by 46
  8. 2021Deep Multi-agent Reinforcement Learning for Highway On-Ramp Merging in Mixed Trafficcited by 41
  9. 2023Can ChatGPT Enable ITS? The Case of Mixed Traffic Control via Reinforcement Learningcited by 20
  10. 2018Simulation to Scaled City: Zero-Shot Policy Transfer for Traffic Control via Autonomous Vehiclescited by 19
  11. 2021Reinforcement Learning for Mixed Autonomy Intersectionscited by 16
  12. 2019ADAPS: Autonomous Driving Via Principled Simulationscited by 15
7 more
  1. 2023Learning to Control and Coordinate Mixed Traffic Through Robot Vehicles at Complex and Unsignalized Intersectionscited by 12
  2. 2023Large-scale Mixed Traffic Control Using Dynamic Vehicle Routing and Privacy-Preserving Crowdsourcingcited by 12
  3. 2023Mixed Traffic Control and Coordination from Pixelscited by 10
  4. 2020A DRL-based Multiagent Cooperative Control Framework for CAV Networks: a Graphic Convolution Q Networkcited by 9
  5. 2023EnduRL: Enhancing Safety, Stability, and Efficiency of Mixed Traffic Under Real-World Perturbations Via Reinforcement Learningcited by 9
  6. 2017Optimal Control of Connected and Automated Vehicles at Roundabouts: An Investigation in a Mixed-Traffic Environmentcited by 3
  7. 2023Long-term Microscopic Traffic Simulation with History-Masked Multi-agent Imitation Learningcited by 3

Built on by

Cited by year

Citing papers per year: 2024: 1.

1 paper.

  1. 2024CARL: Congestion-Aware Reinforcement Learning for Imitation-based Perturbations in Mixed Traffic Controlcited by 7

Similar, not cited

Close in wording, no citation either way

  1. 2025Optimizing Efficiency of Mixed Traffic through Reinforcement Learning: A Topology-Independent Approach and Benchmark
  2. 2024Neighbor-Aware Reinforcement Learning for Mixed Traffic Optimization in Large-scale Networks
  3. 2025Large-Scale Mixed-Traffic and Intersection Control using Multi-agent Reinforcement Learning
  4. 2024Reinforcement Learning for Adaptive Traffic Signal Control: Turn-Based and Time-Based Approaches to Reduce Congestion
  5. 2025Joint Pedestrian and Vehicle Traffic Optimization in Urban Environments using Reinforcement Learning
  6. 2025Multi-Objective Reinforcement Learning for Large-Scale Mixed Traffic Control
  7. 2022Offline Reinforcement Learning for Road Traffic Control
  8. 2025A Hierarchical Deep Reinforcement Learning Framework for Traffic Signal Control with Predictable Cycle Planning