Vehicles Control: Collision Avoidance using Federated Deep Reinforcement
  Learning
Badr Ben Elallid Amine Abouaomar Nabil Benamar Abdellatif Kobbane
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.