mlsanityData to 14 Oct 2026

Adaptive Road Configurations for Improved Autonomous Vehicle-Pedestrian Interactions using Reinforcement Learning

arXiv 2303.12289 ↗First posted 22 Mar 2023cs.LG cs.AI cs.ROcited by 1

On the map: End-to-end autonomous driving → Cooperative and V2X driving

Abstract (arXiv)

The deployment of Autonomous Vehicles (AVs) poses considerable challenges and unique opportunities for the design and management of future urban road infrastructure. In light of this disruptive transformation, the Right-Of-Way (ROW) composition of road space has the potential to be renewed. Design approaches and intelligent control models have been proposed to address this problem, but we lack an operational framework that can dynamically generate ROW plans for AVs and pedestrians in response to real-time demand. Based on microscopic traffic simulation, this study explores Reinforcement Learning (RL) methods for evolving ROW compositions. We implement a centralised paradigm and a distributive learning paradigm to separately perform the dynamic control on several road network configurations. Experimental results indicate that the algorithms have the potential to improve traffic flow efficiency and allocate more space for pedestrians. Furthermore, the distributive learning algorithm outperforms its centralised counterpart regarding computational cost (49.55%), benchmark rewards (25.35%), best cumulative rewards (24.58%), optimal actions (13.49%) and rate of convergence. This novel road management technique could potentially contribute to the flow-adaptive and active mobility-friendly streets in the AVs era.

Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Placed in Cooperative and V2X driving, one of 9 directions in End-to-end autonomous driving. Builds on 9 of its 72 references that are in the map. First posted on arXiv 22 Mar 2023.

Builds on

9 of 72 references.

References · 9

  1. 2015Continuous control with deep reinforcement learningcited by 3,407
  2. 2016OpenAI Gymcited by 1,736
  3. 2017A Brief Survey of Deep Reinforcement Learningcited by 483
  4. 2021Explanations in Autonomous Driving: A Surveycited by 81
  5. 2019Learning Convex Optimization Control Policiescited by 31
  6. 2017Deep Deterministic Policy Gradient for Urban Traffic Light Controlcited by 30
  7. 2017Combining Model-Based and Model-Free Updates for Trajectory-Centric Reinforcement Learningcited by 27
  8. 2019Reinforcement Learning for Portfolio Managementcited by 12
  9. 2016Combining Deep Reinforcement Learning and Safety Based Control for Autonomous Drivingcited by 9

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2026: 1.

1 paper.

  1. 2026DeCoR: Design and Control Co-Optimization for Urban Streets Using Reinforcement Learningcited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2025Joint Pedestrian and Vehicle Traffic Optimization in Urban Environments using Reinforcement Learning
  2. 2025Impact of Collective Behaviors of Autonomous Vehicles on Urban Traffic Dynamics: A Multi-Agent Reinforcement Learning Approach
  3. 2025Optimizing Efficiency of Mixed Traffic through Reinforcement Learning: A Topology-Independent Approach and Benchmark
  4. 2024Learning to Change: Choreographing Mixed Traffic Through Lateral Control and Hierarchical Reinforcement Learning
  5. 2025Dynamic Configuration of On-Street Parking Spaces using Multi Agent Reinforcement Learning
  6. 2022Adaptive Decision Making at the Intersection for Autonomous Vehicles Based on Skill Discovery
  7. 2025Learning to Sample: Reinforcement Learning-Guided Sampling for Autonomous Vehicle Motion Planning
  8. 2024Queue-based Eco-Driving at Roundabouts with Reinforcement Learning