Reinforcement Learning Based Safe Decision Making for Highway Autonomous Driving

TL;DR

This paper introduces a deep reinforcement learning approach for safe decision-making in autonomous highway driving, ensuring collision avoidance and handling unobservable environmental states to improve navigation safety.

Contribution

It presents a novel RL-based method that guarantees collision-free decisions and accounts for unobservable states caused by unpredictable agents in autonomous driving.

Findings

The method ensures collision-free navigation in simulations.

It effectively manages unobservable environmental states.

The approach accelerates learning by guaranteeing safety constraints.

Abstract

In this paper, we develop a safe decision-making method for self-driving cars in a multi-lane, single-agent setting. The proposed approach utilizes deep reinforcement learning (RL) to achieve a high-level policy for safe tactical decision-making. We address two major challenges that arise solely in autonomous navigation. First, the proposed algorithm ensures that collisions never happen, and therefore accelerate the learning process. Second, the proposed algorithm takes into account the unobservable states in the environment. These states appear mainly due to the unpredictable behavior of other agents, such as cars, and pedestrians, and make the Markov Decision Process (MDP) problematic when dealing with autonomous navigation. Simulations from a well-known self-driving car simulator demonstrate the applicability of the proposed method