High-level Decisions from a Safe Maneuver Catalog with Reinforcement Learning for Safe and Cooperative Automated Merging

TL;DR

This paper introduces a scalable reinforcement learning approach for automated merging that ensures safety, cooperates with other drivers, and adapts to different environments, improving driving efficiency and comfort.

Contribution

It presents a safe, scalable RL decision-making pipeline that predicts high-level decisions and maintains safety guarantees in merging scenarios.

Findings

The RL agent can identify cooperative drivers from vehicle history.

Generated maneuvers are collision-free and safe.

The approach improves merging speed and comfort.

Abstract

Reinforcement learning (RL) has recently been used for solving challenging decision-making problems in the context of automated driving. However, one of the main drawbacks of the presented RL-based policies is the lack of safety guarantees, since they strive to reduce the expected number of collisions but still tolerate them. In this paper, we propose an efficient RL-based decision-making pipeline for safe and cooperative automated driving in merging scenarios. The RL agent is able to predict the current situation and provide high-level decisions, specifying the operation mode of the low level planner which is responsible for safety. In order to learn a more generic policy, we propose a scalable RL architecture for the merging scenario that is not sensitive to changes in the environment configurations. According to our experiments, the proposed RL agent can efficiently identify cooperative drivers from their vehicle state history and generate interactive maneuvers, resulting in faster and more comfortable automated driving. At the same time, thanks to the safety constraints inside the planner, all of the maneuvers are collision free and safe.