@article {490,
	title = {Modeling Human Car-Following Behavior from Demonstration with Recurrent Neural Networks},
	year = {2020},
	abstract = {<p>The validity of simulation testing for autonomous vehicles depends on the ability to accurately simulate human driving behavior. This project seeks to train a model on an individual{\textquoteright}s driving data, and to test the ability of the model to predict trajectories that replicate the driver{\textquoteright}s style by using the model in a realistic simulated environment. Specifically, we deployed Recurrent Neural Network (RNN) modeling techniques to create a black-box model of an individual{\textquoteright}s driving behavior. We use our RNN-trained model to simulate a human-driven vehicle in the Robot Operating System (ROS) based CAT Vehicle simulator for autonomous vehicle validation. We hope this work is a step to improve testing environments for validating human behavior replicating car-following models and thereby improve testing environments for autonomous vehicles in general.</p>
},
	keywords = {car-following models, driving behavior, recurrent neural network, simulation, trajectory prediction},
	author = {Iris Jones and Megan Walter},
	editor = {Rahul Bhadani}
}
