DOI: 10.15787/vtt1/zymsem
title: Trucking Fleet Concept of Operations (CONOPS) for Managing Mixed Fleets

description: Project Description: The CONOPS is a living, comprehensive document that describes the ADS characteristics from the viewpoint of the truck fleets that will use ADS technology. the CONOPS includes eight key sections: 1) Installation and Maintenance Guide for Fleets, 2) Inspection Procedures, 3. Driver State Monitoring Technology and Protocol, 4) Motor Carrier Guide to Insuring Advanced Driver Assistance Systems (ADAS)/Automated Driving Systems (ADS)-Equipped Trucks, 5) Identification of Safety Metrics/Variables, 6) Road Readiness Assessment, 7) Data Security/Transfer Protocol, and 8) Cybersecurity Best Practices. The overarching goals of the project are to: (1) demonstrate the safe integration of ADAS/ADS-equipped trucks into the U.S. on-road transportation system; (2) provide the USDOT data for safety analysis and rulemakings to help modernize regulations; and (3) demonstrate how to integrate ADAS/ADS-equipped trucks in a productive, cooperative way into the existing road freight ecosystem. A more detailed description can be found at: https://www.transportation.gov/sites/dot.gov/files/docs/policy-initiatives/automated-vehicles/351301/23-virginia-tech-transportation-institute.pdf Data Scope: The VTTI CONOPS Dataverse was developed to house the data from the CONOPS roadshows and demonstrations. It provides an overview of the dataset, including a description of the project, subject, and keywords. The interface also provides information on the data directories, metadata, terms, and versions. All the non-proprietary data collected over the course of the project, including the data generated during the three operational use case demonstrations (port queueing, cross-country trips, and fleet integration) of the ADAS/ADS-equipped trucks including video, kinematic, GPS, other sensors, and the survey responses obtained from the public during the roadshow outreach events are made available here. The Port Queuing deployment data consists of vehicle state information, which is stored in CSV format. Images were also captured (at 25 frames per second) from the front-facing video stream during the port queueing. These image frames are in “.jpeg” format with a uniquely identifiable name. The data is provided in two folders. One folder is dedicated to all the CSV files, organized by trip. Each trip folder contains subfolders for each file name (such as carState, IMU) organized as file name-hr-min. The second folder has all the images, organized by trip. Data from the cross-country trips consist of IMU data, infrastructure metrics data, and front view images. The IMU data provided information on truck acceleration, motion basics (e.g., roll, pitch, and yaw), location, and speed. Infrastructure metrics data include cellular LTE connectivity, lane marking quality, road bumpiness, and GPS satellite coverage. The data is provided by the State, followed by the trip name. Within the state folder, there are two folders. One folder has the CSV files containing the numerical data (IMU and infrastructure metrics) that were collected on a particular day. The second folder has images (captured at 25 frames per second) in “.jpeg” format with uniquely identifiable names. The files in the image folder are organized as day-hr-min. The Fleet Integration use case data is similar to the port queuing deployment dataset, with information on the vehicle state information stored in CSV and the front-facing images captured during the deployment. The questionnaires from the roadshows were used to investigate attitudes toward truck automation, the use cases where automation will provide economic and/or safety benefits, and the ways in which truck drivers and the driving public can expect to interact with truck automation. The surveys also gathered demographic data to understand how different segments of truck fleets view ADS. The survey data are available in the “Hands-On Demonstration Data (Roadshow Survey)” zip file. The folder contains the raw responses in an excel file, a word document with the summary statistics, and a high-level visualization of the results in PowerPoint. All the data are accessible via the VTTI CONOPS Dataverse. The datasets are minted with permanent digital object identifier (DOI) citations and published on the VTTI Dataverse. The datasets do not contain proprietary or confidential information, hence, there are no concerns regarding privacy, ethics, or confidentiality. The data are available for open sharing under the Creative Commons Zero (CC0) universal public domain dedication.