Vehicles Control: Collision Avoidance using Federated Deep Reinforcement Learning

TL;DR

This paper explores how federated deep reinforcement learning can improve vehicle collision avoidance, reduce delays, and increase speed while maintaining data privacy in urban transportation systems.

Contribution

It introduces the application of federated deep reinforcement learning (FDRL) for vehicle control, demonstrating its superiority over traditional models in collision avoidance and efficiency.

Findings

FDDPG outperforms DDPG in collision prevention

FDRL reduces travel delays significantly

FDRL improves average vehicle speed

Abstract

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.