End-to-End Intersection Handling using Multi-Agent Deep Reinforcement Learning

Understand

Navigating through intersections is one of the main challenging tasks for an autonomous vehicle.

• However, for the majority of intersections regulated by traffic lights, the problem could be solved by a simple rule-based method in which the autonomous vehicle behavior is closely related to the traffic light states.
• In this work, we focus on the implementation of a system able to navigate through intersections where only traffic signs are provided.
• We propose a multi-agent system using a continuous, model-free Deep Reinforcement Learning algorithm used to train a neural network for predicting both the acceleration and the steering angle at each time step.