# Multi-agent Path Finding for Cooperative Autonomous Driving

arXiv: https://arxiv.org/abs/2402.00334 · HTML: https://mlsanity.com/paper/2402.00334/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 32 references that are in the map.
- First posted on arXiv 1 Feb 2024.

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

- [CVXPY: A Python-Embedded Modeling Language for Convex Optimization](https://mlsanity.com/paper/1603.00943/) (2016)
- [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)
- [Lifelong Multi-Agent Path Finding in Large-Scale Warehouses](https://mlsanity.com/paper/2005.07371/) (2020)
- [Flow: A Modular Learning Framework for Mixed Autonomy Traffic](https://mlsanity.com/paper/1710.05465/) (2017)
- [Priority Inheritance with Backtracking for Iterative Multi-agent Path Finding](https://mlsanity.com/paper/1901.11282/) (2019)
- [Improving LaCAM for Scalable Eventually Optimal Multi-Agent Pathfinding](https://mlsanity.com/paper/2305.03632/) (2023)
- [Unified Automatic Control of Vehicular Systems with Reinforcement Learning](https://mlsanity.com/paper/2208.00268/) (2022)
- [Improving Continuous-time Conflict Based Search](https://mlsanity.com/paper/2101.09723/) (2021)
- [Cooperative Driving at Unsignalized Intersections Using Tree Search](https://mlsanity.com/paper/1902.01024/) (2019)
- [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)
- [Comparison of Cooperative Driving Strategies for CAVs at Signal-Free Intersections](https://mlsanity.com/paper/2010.03148/) (2020)
- [A Decentralized Optimal Control Framework for Connected Automated Vehicles at Urban Intersections with Dynamic Resequencing](https://mlsanity.com/paper/1809.00262/) (2018)

## Built on by (most cited first, 3 of 3)

- [Learning-guided Prioritized Planning for Lifelong Multi-Agent Path Finding in Warehouse Automation](https://mlsanity.com/paper/2603.23838/) (2026)
- [Multi-agent Path Finding for Mixed Autonomy Traffic Coordination](https://mlsanity.com/paper/2409.03881/) (2024)
- [Bi-level Mean Field: Dynamic Grouping for Large-Scale MARL](https://mlsanity.com/paper/2505.06706/) (2025)

## Abstract (arXiv)

Anticipating possible future deployment of connected and automated vehicles (CAVs), cooperative autonomous driving at intersections has been studied by many works in control theory and intelligent transportation across decades. Simultaneously, recent parallel works in robotics have devised efficient algorithms for multi-agent path finding (MAPF), though often in environments with simplified kinematics. In this work, we hybridize insights and algorithms from MAPF with the structure and heuristics of optimizing the crossing order of CAVs at signal-free intersections. We devise an optimal and complete algorithm, Order-based Search with Kinematics Arrival Time Scheduling (OBS-KATS), which significantly outperforms existing algorithms, fixed heuristics, and prioritized planning with KATS. The performance is maintained under different vehicle arrival rates, lane lengths, crossing speeds, and control horizon. Through ablations and dissections, we offer insight on the contributing factors to OBS-KATS's performance. Our work is directly applicable to many similarly scaled traffic and multi-robot scenarios with directed lanes.