Non-Intrusive Driver Behavior Characterization From Road-Side Cameras

TL;DR

This paper presents a proof of concept for non-intrusive vehicle behavior analysis using roadside cameras, achieving near-equivalent accuracy to vehicle-based methods and promising improvements in traffic safety and efficiency.

Contribution

It introduces a roadside camera-based approach for vehicle behavior characterization that is transparent, inexpensive, and effective, with potential for large-scale traffic monitoring.

Findings

Driver classification accuracy within 1-2% of vehicle-based methods

Residual errors mainly due to object identification and tracking issues

Method can enhance traffic safety and performance in mixed vehicle scenarios

Abstract

In this paper, we demonstrate a proof of concept for characterizing vehicular behavior using only the roadside cameras of the ITS system. The essential advantage of this method is that it can be implemented in the roadside infrastructure transparently and inexpensively and can have a global view of each vehicle's behavior without any involvement of or awareness by the individual vehicles or drivers. By using a setup that includes programmatically controlled robot cars (to simulate different types of vehicular behaviors) and an external video camera set up to capture and analyze the vehicular behavior, we show that the driver classification based on the external video analytics yields accuracies that are within 1-2\% of the accuracies of direct vehicle-based characterization. We also show that the residual errors primarily relate to gaps in correct object identification and tracking and thus can be further reduced with a more sophisticated setup. The characterization can be used to enhance both the safety and performance of the traffic flow, particularly in the mixed manual and automated vehicle scenarios that are expected to be common soon.