@article{ref1,
title="Modified DDPG car-following model with a real-world human driving experience with CARLA simulator",
journal="Transportation research part C: emerging technologies",
year="2023",
author="Li, Dianzhao and Okhrin, Ostap",
volume="147",
number="",
pages="e103987-e103987",
abstract="In the autonomous driving field, fusion of human knowledge into Deep Reinforcement Learning (DRL) is often based on the human demonstration recorded in a simulated environment. This limits the generalization and the feasibility of application in real-world traffic. We propose a two-stage DRL method to train a car-following agent, that modifies the policy by leveraging the real-world human driving experience and achieves performance superior to the pure DRL agent. Training a DRL agent is done within CARLA framework with Robot Operating System (ROS). For evaluation, we designed different driving scenarios to compare the proposed two-stage DRL car-following agent with other agents. After extracting the &quot;good&quot; behavior from the human driver, the agent becomes more efficient and reasonable, which makes this autonomous agent more suitable to Human-Robot Interaction (HRI) traffic.<p /> <p>Language: en</p>",
language="en",
issn="0968-090X",
doi="10.1016/j.trc.2022.103987",
url="http://dx.doi.org/10.1016/j.trc.2022.103987"
}