# Multi-agent Path Finding for Mixed Autonomy Traffic Coordination

arXiv: https://arxiv.org/abs/2409.03881 · HTML: https://mlsanity.com/paper/2409.03881/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 29 references that are in the map.
- First posted on arXiv 5 Sep 2024.

## Builds on (most cited first, 14 of 14)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Proximal Policy Optimization Algorithms](https://mlsanity.com/paper/1707.06347/) (2017)
- [Semi-Supervised Classification with Graph Convolutional Networks](https://mlsanity.com/paper/1609.02907/) (2016)
- [Social GAN: Socially Acceptable Trajectories with Generative Adversarial Networks](https://mlsanity.com/paper/1803.10892/) (2018)
- [TNT: Target-driveN Trajectory Prediction](https://mlsanity.com/paper/2008.08294/) (2020)
- [Multi-Agent Pathfinding: Definitions, Variants, and Benchmarks](https://mlsanity.com/paper/1906.08291/) (2019)
- [Searching with Consistent Prioritization for Multi-Agent Path Finding](https://mlsanity.com/paper/1812.06356/) (2018)
- [Flow: A Modular Learning Framework for Mixed Autonomy Traffic](https://mlsanity.com/paper/1710.05465/) (2017)
- [Unified Automatic Control of Vehicular Systems with Reinforcement Learning](https://mlsanity.com/paper/2208.00268/) (2022)
- [Conflict-based Search for Multi-Robot Motion Planning with Kinodynamic Constraints](https://mlsanity.com/paper/2207.00576/) (2022)
- [Quick Multi-Robot Motion Planning by Combining Sampling and Search](https://mlsanity.com/paper/2203.00315/) (2022)
- [Controllability Analysis and Optimal Control of Mixed Traffic Flow with Human-driven and Autonomous Vehicles](https://mlsanity.com/paper/2002.02099/) (2020)
- [Cooperative Formation of Autonomous Vehicles in Mixed Traffic Flow: Beyond Platooning](https://mlsanity.com/paper/2009.04254/) (2020)
- [Multi-agent Path Finding for Cooperative Autonomous Driving](https://mlsanity.com/paper/2402.00334/) (2024)

## Built on by (most cited first, 1 of 1)

- [Learning-guided Prioritized Planning for Lifelong Multi-Agent Path Finding in Warehouse Automation](https://mlsanity.com/paper/2603.23838/) (2026)

## Abstract (arXiv)

In the evolving landscape of urban mobility, the prospective integration of Connected and Automated Vehicles (CAVs) with Human-Driven Vehicles (HDVs) presents a complex array of challenges and opportunities for autonomous driving systems. While recent advancements in robotics have yielded Multi-Agent Path Finding (MAPF) algorithms tailored for agent coordination task characterized by simplified kinematics and complete control over agent behaviors, these solutions are inapplicable in mixed-traffic environments where uncontrollable HDVs must coexist and interact with CAVs. Addressing this gap, we propose the Behavior Prediction Kinematic Priority Based Search (BK-PBS), which leverages an offline-trained conditional prediction model to forecast HDV responses to CAV maneuvers, integrating these insights into a Priority Based Search (PBS) where the A* search proceeds over motion primitives to accommodate kinematic constraints. We compare BK-PBS with CAV planning algorithms derived by rule-based car-following models, and reinforcement learning. Through comprehensive simulation on a highway merging scenario across diverse scenarios of CAV penetration rate and traffic density, BK-PBS outperforms these baselines in reducing collision rates and enhancing system-level travel delay. Our work is directly applicable to many scenarios of multi-human multi-robot coordination.