mlsanityData to 7 Oct 2026

Connected and Automated Vehicles in Mixed-Traffic: Learning Human Driver Behavior for Effective On-Ramp Merging

arXiv 2304.00397 ↗First posted 1 Apr 2023cs.LG cs.AI cs.SYcited by 7

Not on the map. Why?

Abstract (arXiv)

Highway merging scenarios featuring mixed traffic conditions pose significant modeling and control challenges for connected and automated vehicles (CAVs) interacting with incoming on-ramp human-driven vehicles (HDVs). In this paper, we present an approach to learn an approximate information state model of CAV-HDV interactions for a CAV to maneuver safely during highway merging. In our approach, the CAV learns the behavior of an incoming HDV using approximate information states before generating a control strategy to facilitate merging. First, we validate the efficacy of this framework on real-world data by using it to predict the behavior of an HDV in mixed traffic situations extracted from the Next-Generation Simulation repository. Then, we generate simulation data for HDV-CAV interactions in a highway merging scenario using a standard inverse reinforcement learning approach. Without assuming a prior knowledge of the generating model, we show that our approximate information state model learns to predict the future trajectory of the HDV using only observations. Subsequently, we generate safe control policies for a CAV while merging with HDVs, demonstrating a spectrum of driving behaviors, from aggressive to conservative. We demonstrate the effectiveness of the proposed approach by performing numerical simulations.

Key facts. Cited by 7 papers in the map (data through 7 Oct 2026). Builds on 20 of its 31 references that are in the map. First posted on arXiv 1 Apr 2023.

Builds on

20 of 31 references.

References · 20

  1. 2014On the Properties of Neural Machine Translation: Encoder-Decoder Approachescited by 1,303
  2. 2018Convolutional Social Pooling for Vehicle Trajectory Predictioncited by 221
  3. 2018An LSTM Network for Highway Trajectory Predictioncited by 81
  4. 2018Multi-Modal Trajectory Prediction of Surrounding Vehicles with Maneuver based LSTMscited by 77
  5. 2020Approximate information state for approximate planning and reinforcement learning in partially observed systemscited by 67
  6. 2017Probabilistic Vehicle Trajectory Prediction over Occupancy Grid Map via Recurrent Neural Networkcited by 56
  7. 2018Sequence-to-Sequence Prediction of Vehicle Trajectory via LSTM Encoder-Decoder Architecturecited by 55
  8. 2021Medical Dead-ends and Learning to Identify High-risk States and Treatmentscited by 28
  9. 2021GamePlan: Game-Theoretic Multi-Agent Planning with Human Drivers at Intersections, Roundabouts, and Mergingcited by 16
  10. 2021On Team Decision Problems with Nonclassical Information Structurescited by 10
  11. 2021Separation of Learning and Control for Cyber-Physical Systemscited by 8
  12. 2019Enhanced Mobility With Connectivity and Automation: A Review of Shared Autonomous Vehicle Systemscited by 7
8 more
  1. 2020Optimal Time Trajectory and Coordination for Connected and Automated Vehiclescited by 5
  2. 2021A Research and Educational Robotic Testbed for Real-time Control of Emerging Mobility Systems: From Theory to Scaled Experimentscited by 4
  3. 2022Approximate Information States for Worst-case Control of Uncertain Systemscited by 4
  4. 2022A Cooperative Optimal Control Framework for Connected and Automated Vehicles in Mixed Traffic Using Social Value Orientationcited by 4
  5. 2018Decentralized Optimal Merging Control for Connected and Automated Vehiclescited by 3
  6. 2023Approximate Information States for Worst-Case Control and Learning in Uncertain Systemscited by 3
  7. 2022A Barrier-Certified Optimal Coordination Framework for Connected and Automated Vehiclescited by 2
  8. 2022Optimal Weight Adaptation of Model Predictive Control for Connected and Automated Vehicles in Mixed Traffic with Bayesian Optimizationcited by 2

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 2; 2024: 1; 2025: 3; 2026: 1.

7 papers.

  1. 2023A Q-learning Approach for Adherence-Aware Recommendationscited by 4
  2. 2024A Framework for Effective AI Recommendations in Cyber-Physical-Human Systemscited by 4
  3. 2023Navigating Uncertainty: The Role of Short-Term Trajectory Prediction in Autonomous Vehicle Safetycited by 3
  4. 2025AI Recommendation Systems for Lane-Changing Using Adherence-Aware Reinforcement Learningcited by 3
  5. 2025Off-Policy Evaluation for Sequential Persuasion Process with Unobserved Confoundingcited by 1
  6. 2025Model-Based Reinforcement Learning Under Confoundingcited by 1
  7. 2026Cross-fitted Proximal Learning for Model-Based Reinforcement Learningcited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2024CARL: Congestion-Aware Reinforcement Learning for Imitation-based Perturbations in Mixed Traffic Control
  2. 2025Resolve Highway Conflict in Multi-Autonomous Vehicle Controls with Local State Attention
  3. 2020A Survey on Autonomous Vehicle Control in the Era of Mixed-Autonomy: From Physics-Based to AI-Guided Driving Policy Learning
  4. 2024Roadside Units Assisted Localized Automated Vehicle Maneuvering: An Offline Reinforcement Learning Approach
  5. 2021Multi-agent Reinforcement Learning for Cooperative Lane Changing of Connected and Autonomous Vehicles in Mixed Traffic
  6. 2018Probabilistic Prediction of Interactive Driving Behavior via Hierarchical Inverse Reinforcement Learning
  7. 2023Towards Socially Responsive Autonomous Vehicles: A Reinforcement Learning Framework with Driving Priors and Coordination Awareness
  8. 2026Aligning Microscopic Vehicle and Macroscopic Traffic Statistics: Reconstructing Driving Behavior from Partial Data