Processing and Analyzing Real-World Driving Data: Insights on Trips, Scenarios, and Human Driving Behaviors

TL;DR

The paper presents a LaTeX-based template bundle designed to streamline manuscript preparation for Elsevier journals under the updated submission workflow. It introduces two class files, cas-sc.cls for single-column and cas-dc.cls for double-column layouts, along with template files and a long front matter option to accommodate complex front matter. The work details front-matter customization, including author affiliations, footnotes, corresponding author markers, and keyword formatting, as well as support for theorem-like environments. By standardizing front matter and layout, the approach improves formatting fidelity and submission efficiency across Elsevier journals.

Abstract

Analyzing large volumes of real-world driving data is essential for providing meaningful and reliable insights into real-world trips, scenarios, and human driving behaviors. To this end, we developed a multi-level data processing approach that adds new information, segments data, and extracts desired parameters. Leveraging a confidential but extensive dataset (over 1 million km), this approach leads to three levels of in-depth analysis: trip, scenario, and driving. The trip-level analysis explains representative properties observed in real-world trips, while the scenario-level analysis focuses on scenario conditions resulting from road events that reduce vehicle speed. The driving-level analysis identifies the cause of driving regimes for specific situations and characterizes typical human driving behaviors. Such analyses can support the design of both trip- and scenario-based tests, the modeling of human drivers, and the establishment of guidelines for connected and automated vehicles.