Intelligent Roundabout Insertion using Deep Reinforcement Learning

TL;DR

This paper introduces a deep reinforcement learning-based maneuver planning system for autonomous vehicles to negotiate busy roundabouts, using a novel D-A3C algorithm and customizable driver behaviors for improved safety and efficiency.

Contribution

It presents a new neural network model trained with Delayed A3C for roundabout negotiation, incorporating customizable driver styles and aggressiveness levels.

Findings

Model successfully predicts safe entry timings into roundabouts.

System accommodates different driver behaviors and aggressiveness levels.

Enhanced maneuver planning in complex traffic scenarios.

Abstract

An important topic in the autonomous driving research is the development of maneuver planning systems. Vehicles have to interact and negotiate with each other so that optimal choices, in terms of time and safety, are taken. For this purpose, we present a maneuver planning module able to negotiate the entering in busy roundabouts. The proposed module is based on a neural network trained to predict when and how entering the roundabout throughout the whole duration of the maneuver. Our model is trained with a novel implementation of A3C, which we will call Delayed A3C (D-A3C), in a synthetic environment where vehicles move in a realistic manner with interaction capabilities. In addition, the system is trained such that agents feature a unique tunable behavior, emulating real world scenarios where drivers have their own driving styles. Similarly, the maneuver can be performed using different aggressiveness levels, which is particularly useful to manage busy scenarios where conservative rule-based policies would result in undefined waits.