Prediction Based Decision Making for Autonomous Highway Driving
Abstract
Autonomous driving decision-making is a challenging task due to the inherent complexity and uncertainty in traffic. For example, adjacent vehicles may change their lane or overtake at any time to pass a slow vehicle or to help traffic flow. Anticipating the intention of surrounding vehicles, estimating their future states and integrating them into the decision-making process of an automated vehicle can enhance the reliability of autonomous driving in complex driving scenarios. This paper proposes a Prediction-based Deep Reinforcement Learning (PDRL) decision-making model that considers the manoeuvre intentions of surrounding vehicles in the decision-making process for highway driving. The model is trained using real traffic data and tested in various traffic conditions through a simulation platform. The results show that the proposed PDRL model improves the decision-making performance compared to a Deep Reinforcement Learning (DRL) model by decreasing collision numbers, resulting in safer driving.
Cite
@article{arxiv.2209.02106,
title = {Prediction Based Decision Making for Autonomous Highway Driving},
author = {Mustafa Yildirim and Sajjad Mozaffari and Luc McCutcheon and Mehrdad Dianati and Alireza Tamaddoni-Nezhad Saber Fallah},
journal= {arXiv preprint arXiv:2209.02106},
year = {2022}
}
Comments
Published at ITSC 2022