Modeling Human Car-Following Behavior from Demonstration with Recurrent Neural Networks
|Title||Modeling Human Car-Following Behavior from Demonstration with Recurrent Neural Networks|
|Year of Publication||2020|
|Authors||Jones, I, Walter, M|
|Series Editor||Bhadani, R|
|Tertiary Authors||Sprinkle, J|
|Keywords||car-following models, driving behavior, recurrent neural network, simulation, trajectory prediction|
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.