# Vehicles Control: Collision Avoidance using Federated Deep Reinforcement Learning

arXiv: https://arxiv.org/abs/2308.02614 · HTML: https://mlsanity.com/paper/2308.02614/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 18 references that are in the map.
- First posted on arXiv 4 Aug 2023.

## Builds on (most cited first, 4 of 4)

- [OpenAI Gym](https://mlsanity.com/paper/1606.01540/) (2016)
- [Federated Deep Reinforcement Learning for Open RAN Slicing in 6G Networks](https://mlsanity.com/paper/2206.11328/) (2022)
- [Federated Learning for Water Consumption Forecasting in Smart Cities](https://mlsanity.com/paper/2301.13036/) (2023)
- [A Deep Reinforcement Learning Approach for Service Migration in MEC-enabled Vehicular Networks](https://mlsanity.com/paper/2201.11831/) (2022)

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

- [Efficient Collaborations through Weight-Driven Coalition Dynamics in Federated Learning Systems](https://mlsanity.com/paper/2401.12356/) (2024)
- [Deep Reinforcement Learning for Autonomous Vehicle Intersection Navigation](https://mlsanity.com/paper/2310.08595/) (2023)
- [Privacy-Preserving Offloading for Large Language Models in 6G Vehicular Networks](https://mlsanity.com/paper/2509.05320/) (2025)

## Abstract (arXiv)

In the face of growing urban populations and the escalating number of vehicles on the roads, managing transportation efficiently and ensuring safety have become critical challenges. To tackle these issues, the development of intelligent control systems for vehicles is paramount. This paper presents a comprehensive study on vehicle control for collision avoidance, leveraging the power of Federated Deep Reinforcement Learning (FDRL) techniques. Our main goal is to minimize travel delays and enhance the average speed of vehicles while prioritizing safety and preserving data privacy. To accomplish this, we conducted a comparative analysis between the local model, Deep Deterministic Policy Gradient (DDPG), and the global model, Federated Deep Deterministic Policy Gradient (FDDPG), to determine their effectiveness in optimizing vehicle control for collision avoidance. The results obtained indicate that the FDDPG algorithm outperforms DDPG in terms of effectively controlling vehicles and preventing collisions. Significantly, the FDDPG-based algorithm demonstrates substantial reductions in travel delays and notable improvements in average speed compared to the DDPG algorithm.