# A Cooperative Optimal Control Framework for Connected and Automated Vehicles in Mixed Traffic Using Social Value Orientation

arXiv: https://arxiv.org/abs/2203.17106 · HTML: https://mlsanity.com/paper/2203.17106/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 31 Mar 2022.

## Built on by (most cited first, 4 of 4)

- [Connected and Automated Vehicles in Mixed-Traffic: Learning Human Driver Behavior for Effective On-Ramp Merging](https://mlsanity.com/paper/2304.00397/) (2023)
- [Approximate Information States for Worst-Case Control and Learning in Uncertain Systems](https://mlsanity.com/paper/2301.05089/) (2023)
- [Learning-based social coordination to improve safety and robustness of cooperative autonomous vehicles in mixed traffic](https://mlsanity.com/paper/2211.11963/) (2022)
- [Socially-Aware Autonomous Driving: Inferring Yielding Intentions for Safer Interactions](https://mlsanity.com/paper/2504.20004/) (2025)

## Abstract (arXiv)

In this paper, we develop a socially cooperative optimal control framework to address the motion planning problem for connected and automated vehicles (CAVs) in mixed traffic using social value orientation (SVO) and a potential game approach. In the proposed framework, we formulate the interaction between a CAV and a human-driven vehicle (HDV) as a simultaneous game where each vehicle minimizes a weighted sum of its egoistic objective and a cooperative objective. The SVO angles are used to quantify preferences of the vehicles toward the egoistic and cooperative objectives. Using the potential game approach, we propose a single objective function for the optimal control problem whose weighting factors are chosen based on the SVOs of the vehicles. We prove that a Nash equilibrium can be obtained by minimizing the proposed objective function. To estimate the SVO angle of the HDV, we develop a moving horizon estimation algorithm based on maximum entropy inverse reinforcement learning. The effectiveness of the proposed approach is demonstrated by numerical simulations of a vehicle merging scenario.