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

arXiv: https://arxiv.org/abs/2304.00397 · HTML: https://mlsanity.com/paper/2304.00397/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 20 of its 31 references that are in the map.
- First posted on arXiv 1 Apr 2023.

## Builds on (most cited first, 20 of 20)

- [On the Properties of Neural Machine Translation: Encoder-Decoder Approaches](https://mlsanity.com/paper/1409.1259/) (2014)
- [Convolutional Social Pooling for Vehicle Trajectory Prediction](https://mlsanity.com/paper/1805.06771/) (2018)
- [An LSTM Network for Highway Trajectory Prediction](https://mlsanity.com/paper/1801.07962/) (2018)
- [Multi-Modal Trajectory Prediction of Surrounding Vehicles with Maneuver based LSTMs](https://mlsanity.com/paper/1805.05499/) (2018)
- [Approximate information state for approximate planning and reinforcement learning in partially observed systems](https://mlsanity.com/paper/2010.08843/) (2020)
- [Probabilistic Vehicle Trajectory Prediction over Occupancy Grid Map via Recurrent Neural Network](https://mlsanity.com/paper/1704.07049/) (2017)
- [Sequence-to-Sequence Prediction of Vehicle Trajectory via LSTM Encoder-Decoder Architecture](https://mlsanity.com/paper/1802.06338/) (2018)
- [Medical Dead-ends and Learning to Identify High-risk States and Treatments](https://mlsanity.com/paper/2110.04186/) (2021)
- [GamePlan: Game-Theoretic Multi-Agent Planning with Human Drivers at Intersections, Roundabouts, and Merging](https://mlsanity.com/paper/2109.01896/) (2021)
- [On Team Decision Problems with Nonclassical Information Structures](https://mlsanity.com/paper/2101.10992/) (2021)
- [Separation of Learning and Control for Cyber-Physical Systems](https://mlsanity.com/paper/2107.06379/) (2021)
- [Enhanced Mobility With Connectivity and Automation: A Review of Shared Autonomous Vehicle Systems](https://mlsanity.com/paper/1905.12602/) (2019)
- [Optimal Time Trajectory and Coordination for Connected and Automated Vehicles](https://mlsanity.com/paper/2003.12183/) (2020)
- [Decentralized Optimal Merging Control for Connected and Automated Vehicles](https://mlsanity.com/paper/1809.07916/) (2018)
- [A Research and Educational Robotic Testbed for Real-time Control of Emerging Mobility Systems: From Theory to Scaled Experiments](https://mlsanity.com/paper/2109.05370/) (2021)
- [Approximate Information States for Worst-case Control of Uncertain Systems](https://mlsanity.com/paper/2203.15271/) (2022)
- [A Cooperative Optimal Control Framework for Connected and Automated Vehicles in Mixed Traffic Using Social Value Orientation](https://mlsanity.com/paper/2203.17106/) (2022)
- [Approximate Information States for Worst-Case Control and Learning in Uncertain Systems](https://mlsanity.com/paper/2301.05089/) (2023)
- [A Barrier-Certified Optimal Coordination Framework for Connected and Automated Vehicles](https://mlsanity.com/paper/2203.16418/) (2022)
- [Optimal Weight Adaptation of Model Predictive Control for Connected and Automated Vehicles in Mixed Traffic with Bayesian Optimization](https://mlsanity.com/paper/2210.00700/) (2022)

## Built on by (most cited first, 7 of 7)

- [A Q-learning Approach for Adherence-Aware Recommendations](https://mlsanity.com/paper/2309.06519/) (2023)
- [A Framework for Effective AI Recommendations in Cyber-Physical-Human Systems](https://mlsanity.com/paper/2403.05715/) (2024)
- [Navigating Uncertainty: The Role of Short-Term Trajectory Prediction in Autonomous Vehicle Safety](https://mlsanity.com/paper/2307.05288/) (2023)
- [AI Recommendation Systems for Lane-Changing Using Adherence-Aware Reinforcement Learning](https://mlsanity.com/paper/2504.20187/) (2025)
- [Off-Policy Evaluation for Sequential Persuasion Process with Unobserved Confounding](https://mlsanity.com/paper/2504.01211/) (2025)
- [Model-Based Reinforcement Learning Under Confounding](https://mlsanity.com/paper/2512.07528/) (2025)
- [Cross-fitted Proximal Learning for Model-Based Reinforcement Learning](https://mlsanity.com/paper/2604.05185/) (2026)

## 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.