Summarizing Normative Driving Behavior From Large-Scale NDS Datasets for Vehicle System Development

TL;DR

This paper develops a methodology to analyze large-scale naturalistic driving data, describing normative behaviors across various metrics and driver groups to support vehicle safety and infrastructure improvements.

Contribution

It introduces a comprehensive approach to process NDS data for describing normative driving behaviors with interactive visualization tools.

Findings

Females aged 16-19 exceeded speed limits more often than males.

Younger drivers maintained shorter headways than older drivers.

Methodology applied to over 34 million miles of driving data.

Abstract

This paper presents a methodology to process large-scale naturalistic driving studies (NDS) to describe the driving behavior for five vehicle metrics, including speed, speeding, lane keeping, following distance, and headway, contextualized by roadway characteristics, vehicle classes, and driver demographics. Such descriptions of normative driving behaviors can aid in the development of vehicle safety and intelligent transportation systems. The methodology is demonstrated using data from the Second Strategic Highway Research Program (SHRP 2) NDS, which includes over 34 million miles of driving across more than 3,400 drivers. Summaries of each driving metric were generated using vehicle, GPS, and forward radar data. Additionally, interactive online analytics tools were developed to visualize and compare driving behavior across groups through dynamic data selection and grouping. For example, among drivers on 65-mph roads for the SHRP 2 NDS, females aged 16-19 exceeded the speed limit by 7.5 to 15 mph slightly more often than their male counterparts, and younger drivers maintained headways under 1.5 seconds more frequently than older drivers. This work supports better vehicle systems and safer infrastructure by quantifying normative driving behaviors and offers a methodology for analyzing NDS datasets for cross group comparisons.