# Coordinating CAV Swarms at Intersections with a Deep Learning Model

arXiv: https://arxiv.org/abs/2211.05297 · HTML: https://mlsanity.com/paper/2211.05297/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 43 references that are in the map.
- First posted on arXiv 10 Nov 2022.

## Builds on (most cited first, 13 of 13)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Asynchronous Methods for Deep Reinforcement Learning](https://mlsanity.com/paper/1602.01783/) (2016)
- [Mastering Atari, Go, Chess and Shogi by Planning with a Learned Model](https://mlsanity.com/paper/1911.08265/) (2019)
- [Pointer Networks](https://mlsanity.com/paper/1506.03134/) (2015)
- [Neural Combinatorial Optimization with Reinforcement Learning](https://mlsanity.com/paper/1611.09940/) (2016)
- [Reinforcement Learning for Solving the Vehicle Routing Problem](https://mlsanity.com/paper/1802.04240/) (2018)
- [Flow: A Modular Learning Framework for Mixed Autonomy Traffic](https://mlsanity.com/paper/1710.05465/) (2017)
- [Seq2Slate: Re-ranking and Slate Optimization with RNNs](https://mlsanity.com/paper/1810.02019/) (2018)
- [Cooperative Driving at Unsignalized Intersections Using Tree Search](https://mlsanity.com/paper/1902.01024/) (2019)
- [UAV Trajectory Planning in Wireless Sensor Networks for Energy Consumption Minimization by Deep Reinforcement Learning](https://mlsanity.com/paper/2108.00354/) (2021)
- [Distributed Cooperative Driving in Multi-Intersection Road Networks](https://mlsanity.com/paper/2104.10313/) (2021)
- [A Bi-Level Cooperative Driving Strategy Allowing Lane Changes](https://mlsanity.com/paper/1912.11495/) (2019)
- [A Grouping Based Cooperative Driving Strategy for CAVs Merging Problems](https://mlsanity.com/paper/1804.01250/) (2018)

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

- [Language-Driven Policy Distillation for Cooperative Driving in Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/2410.24152/) (2024)

## Abstract (arXiv)

Connected and automated vehicles (CAVs) are viewed as a special kind of robots that have the potential to significantly improve the safety and efficiency of traffic. In contrast to many swarm robotics studies that are demonstrated in labs by employing a small number of robots, CAV studies aims to achieve cooperative driving of unceasing robot swarm flows. However, how to get the optimal passing order of such robot swarm flows even for a signal-free intersection is an NP-hard problem (specifically, enumerating based algorithm takes days to find the optimal solution to a 20-CAV scenario). Here, we introduce a novel cooperative driving algorithm (AlphaOrder) that combines offline deep learning and online tree searching to find a near-optimal passing order in real-time. AlphaOrder builds a pointer network model from solved scenarios and generates near-optimal passing orders instantaneously for new scenarios. Furthermore, our approach provides a general approach to managing preemptive resource sharing between swarm robotics (e.g., scheduling multiple automated guided vehicles (AGVs) and unmanned aerial vehicles (UAVs) at conflicting areas