mlsanityData to 14 Oct 2026

Decentralized Traffic Flow Optimization Through Intrinsic Motivation

arXiv 2505.11520 ↗First posted 8 May 2025physics.soc-ph cs.AIcited by 1

Not on the map. Why?

Abstract (arXiv)

Traffic congestion has long been an ubiquitous problem that is exacerbating with the rapid growth of megacities. In this proof-of-concept work we study intrinsic motivation, implemented via the empowerment principle, to control autonomous car behavior to improve traffic flow. In standard models of traffic dynamics, self-organized traffic jams emerge spontaneously from the individual behavior of cars, affecting traffic over long distances. Our novel car behavior strategy improves traffic flow while still being decentralized and using only locally available information without explicit coordination. Decentralization is essential for various reasons, not least to be able to absorb robustly substantial levels of uncertainty. Our scenario is based on the well-established traffic dynamics model, the Nagel-Schreckenberg cellular automaton. In a fraction of the cars in this model, we substitute the default behavior by empowerment, our intrinsic motivation-based method. This proposed model significantly improves overall traffic flow, mitigates congestion, and reduces the average traffic jam time.

Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 10 of its 40 references that are in the map. First posted on arXiv 8 May 2025.

Builds on

10 of 40 references.

References · 10

  1. 2019Dynamics-Aware Unsupervised Discovery of Skillscited by 313
  2. 2016Hierarchical Deep Reinforcement Learning: Integrating Temporal Abstraction and Intrinsic Motivationcited by 286
  3. 2016Variational Intrinsic Controlcited by 267
  4. 2020AvE: Assistance via Empowermentcited by 27
  5. 2021Scalable Multiagent Driving Policies For Reducing Traffic Congestioncited by 11
  6. 2014Changing the Environment Based on Empowerment as Intrinsic Motivationcited by 10
  7. 2020Efficient Empowerment Estimation for Unsupervised Stabilizationcited by 9
  8. 2022Intrinsic Motivation in Dynamical Control Systemscited by 8
  9. 2019Intrinsically motivated collective motioncited by 7
  10. 2021Learning a Robust Multiagent Driving Policy for Traffic Congestion Reductioncited by 4

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2026: 1.

1 paper.

  1. 2026Emergence of Physical Intelligence via Controllable Information Productioncited by 1

Similar, not cited

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  1. 2025Self-Regulating Cars: Automating Traffic Control in Free Flow Road Networks
  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. 2025Large-Scale Mixed-Traffic and Intersection Control using Multi-agent Reinforcement Learning
  5. 2025Robust Single-Agent Reinforcement Learning for Regional Traffic Signal Control Under Demand Fluctuations
  6. 2025Self-Interest and Systemic Benefits: Emergence of Collective Rationality in Mixed Autonomy Traffic Through Deep Reinforcement Learning
  7. 2026Momentum Based Reward Design for Low Emission Traffic Signal Control
  8. 2025Integrated Strategy for Urban Traffic Optimization: Prediction, Adaptive Signal Control, and Distributed Communication via Messaging ↗