01576nas a2200193   4500008004100000245009200041210006900133520087400202653002501076653002101101653002901122653001501151653002601166100001601192700001801208700001901226700002301245856011401268       2020                        eng d00aModeling Human Car-Following Behavior from Demonstration with Recurrent Neural Networks0 aModeling Human CarFollowing Behavior from Demonstration with Rec3 a<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’s driving data, and to test the ability of the model to predict trajectories that replicate the driver’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’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>
10acar-following models10adriving behavior10arecurrent neural network10asimulation10atrajectory prediction1 aJones, Iris1 aWalter, Megan1 aBhadani, Rahul1 aSprinkle, Jonathan  uhttps://csl.arizona.edu/content/modeling-human-car-following-behavior-demonstration-recurrent-neural-networks