Predicting human-driving behavior to help driverless vehicles drive: random intercept Bayesian Additive Regression Trees

TL;DR

This paper introduces a novel random intercept Bayesian Additive Regression Trees model to improve the prediction of human driving behavior, specifically vehicle stopping before left turns, by accounting for driver clustering effects.

Contribution

The paper extends BART to handle correlated binary data with random intercepts, enhancing prediction accuracy in driver behavior modeling.

Findings

Random intercept BART outperforms standard BART and linear logistic regression.

Simulation shows the model has low bias and good coverage.

Application to real data improves prediction of vehicle stopping behavior.

Abstract

The development of driverless vehicles has spurred the need to predict human driving behavior to facilitate interaction between driverless and human-driven vehicles. Predicting human driving movements can be challenging, and poor prediction models can lead to accidents between the driverless and human-driven vehicles. We used the vehicle speed obtained from a naturalistic driving dataset to predict whether a human-driven vehicle would stop before executing a left turn. In a preliminary analysis, we found that BART produced less variable and higher AUC values compared to a variety of other state-of-the-art binary predictor methods. However, BART assumes independent observations, but our dataset consists of multiple observations clustered by driver. Although methods extending BART to clustered or longitudinal data are available, they lack readily available software and can only be applied to clustered continuous outcomes. We extend BART to handle correlated binary observations by adding a random intercept and used a simulation study to determine bias, root mean squared error, 95% coverage, and average length of 95% credible interval in a correlated data setting. We then successfully implemented our random intercept BART model to our clustered dataset and found substantial improvements in prediction performance compared to BART and random intercept linear logistic regression.