TC Emerging Transportation Technology Testing Logo

Chair

  • Xiaopeng (Shaw) Li

Co-chair(s)

  • Xiaobo Qu
  • Xin Wang

Short Description

The Emerging Transportation Technology Testing (ET3) Technical Committee (TC) is a sub-committee of the IEEE Intelligent Transportation Systems Society. The aim of ET3 is to establish a community of leaders from academia , industry, and government focused on sustaining and synergizing the concerted efforts on testing environments, methods, and protocols. The TC aims to facilitate the utilization of existing test facilities and promoting their collaborations with other people across the world. The ET3-TC also creates platforms and publication venues for testing methods and supports the standardization of implementation protocols.

Emerging transportation technologies, such as communications (e.g., connected vehicles), automation, electrification, and mode diversification (e.g., modular vehicles, shared mobility, urban air mobility, hyperloop, boring), have constantly been transforming transportation systems. Various analytical and simulation models have been proposed over the past decades to simulate, model, and analyze these emerging technologies for planning, operating, managing, and evaluating near-future transportation systems. In recent years, studies on testing these technologies in a valid environmenthave rapidly increased, particularly for problems in a system context with multiple components interacting with each other. Proper tests are critical for validating the assumptions and outcomes of the theoretical models, demonstrating the benefits of system-level models, and transforming modeling outcomes into implementable technologies and tangible societal impacts.

Realizing the importance of testing, intensive efforts have been recently put into developing and sharing testbeds for emerging technologies, including utilizing naturalistic data, conducting closed-track or real-world field experiments, and incorporating hardware components in the loop of systematic simulation. Various testing methods (e.g., design of experiments, importance sampling, accelerated testing) are being developed to accomplish the best outcomes within resource constraints. Further, it is imperative for governments and industries to adopt standard protocols to verify and implement the testing results. The main topics of interest to ET3 include (but are not limited to) the following:

  • Emerging transportation technologies, including but not limited to communications, automated vehicles, electric vehicles, modular vehicles, urban air mobility, smart rail, hyperloop, boring, etc.
  • Testbed development and sharing, including hardware-in-the-loop, closed-track testbeds, and naturalistic tests.
  • Testing methods, e.g., scenario design, accelerated tests, human-machine interface, etc.
  • Testing standards and industry implementation.

ET3 organized a workshop on connected automated vehicle (CAV) testbeds at the 2021 IEEE ITSC conference. ET3 plans to host committee meetings with all members to establish the community and set up the work plan in the coming years. In addition, ET3 will also seek publication opportunities (e.g., special issues in appropriate journals, e.g., Communications in Transportation Research) to promote the development of Emerging Transportation Technology Testing.

Testbed

The following testbeds are selected representatives over the world.

  • Mcity (https://mcity.umich.edu/)

Mcity operates the world’s first purpose-built proving ground for testing the performance and safety of connected and automated vehicles and technologies under controlled and realistic conditions.

  • Suntrax (https://www.suntraxfl.com/)

SunTrax is a large-scale, cutting-edge facility being developed by the Florida Department of Transportation and Florida’s Turnpike Enterprise, dedicated to the research, development, and testing of emerging transportation technologies in safe and controlled environments.

  • American Center for Mobility (https://www.acmwillowrun.org/)

American Center for Mobility is a collaborative effort comprised of government, industry, and academic organizations focused on accelerating the mobility transportation industry.

  • GoMentum Station (https://gomentumstation.net/)

GoMentum Station is the cornerstone of AAA Northern California Nevada & Utah’s efforts to create a collaborative space dedicated to the safe development of automated vehicles.

  • Larson Institute test track (https://www.larson.psu.edu/about/test-track.aspx)

The Larson Institute’s full-scale Transportation Research Facilities include a test track facility designed to accommodate a broad range of research activities.

  • Connected and Automated Vehicles Test Field (https://cavtest.chd.edu.cn/8939/main.htm)

Connected and Automated Vehicles Test Field (CAVTest) is a test track of Chang’an University in China. It is the only university-based testing field in China.

  • ZalaZONE Automotive Proving Ground (https://zalazone.hu/)

ZalaZONE Automotive Proving Ground (Hungary) is a 250-hectare high-tech test facility for conventional, electric, connected, and automated vehicles, an optimal test environment for future cars and their communication technologies, which also serves as a hub for innovation activities and for joint research programs with strategic university partners.

Data platform and dataset

1 data platform and 10 representative datasets are summarized for connected and automated vehicle tests.

  • ETS-Data platform (https://ets-data.sciopen.com/home)

ETS-Data is a publicly accessible database in the transportation community, that provides indispensable materials for result replications, including data, codes, scripts, simulation, experiment design and other essential files.

  • Waymo Open Dataset (https://waymo.com/intl/en_us/dataset-download-terms/)

The Waymo Open Dataset is high-resolution sensor data collected using ADAS-equipped vehicles operated by Waymo drivers in various environments, objects, and weather conditions.

  • Connected Vehicle Pilot (CVP) Open Dataset

(https://datahub.transportation.gov/stories/s/Connected-Vehicle-Pilot-CVP-Open-Data/hr8h-ufhq/)

The CVP team integrates CV research concepts into practical and effective equipment to enhance vehicle operation. The CVP team collected vehicle trajectory data and lane information to facilitate independent evaluations of the use of CV technology with connected roadside infrastructure on real roadways.

  • University of South Florida (USF) Open Datasets Collected in the United States (https://github.com/CATS-Lab)

The Connected and Autonomous Transportation Systems laboratory at USF has collected different ADAS data sets, including the mixed traffic platoon dataset and the ADAS-equipped vehicle platooning dataset.

  • Vanderbilt ACC Dataset (https://vanderbilt.app.box.com/v/accData)

Vanderbilt University collected the Vanderbilt ACC dataset. It included car-following data of commercial ADAS-equipped vehicles with ACC enabled.

  • Ford Campus Dataset (http://robots.engin.umich.edu/SoftwareData/Ford)

The University of Michigan and the Ford research team established a dataset for an ACC vehicle (a modified Ford® F-250 pickup truck). These data were collected around the Ford research campus and downtown Dearborn, Michigan, from November 2009 to December 2009.

  • Ford Multi-AV Seasonal Dataset (https://avdata.ford.com/home/default.aspx)

The Ford research team presents a challenging multi-agent seasonal dataset. Ford autonomous vehicles (enabled by ACC) are applied in collecting data on different days and times during 2017–2018.

  • Lyft Dataset (https://level-5.global/data/)

Lyft dataset has consisted of sensor input and maps, detected traffic agents, predicted agent motion, and path is taken by AVs.

  • OpenACC Dataset (https://data.europa.eu/data/datasets/9702c950-c80f-4d2f-982f-44d06ea0009f?locale=en)

The European Commission collected the OpenACC Dataset. It provides an overview of the behavior of commercial ACC systems under different driving conditions.

  • Oxford RobotCar Dataset (http://robotcar-dataset.robots.ox.ac.uk)

The Oxford RobotCar Dataset contains over 100 repetitions of one subject ACC vehicle operating on a consistent route through Oxford, United Kingdom, captured over a period of more than 1 year.

  • pNEUMA Dataset (https://open-traffic.epfl.ch/)

pNEUMA is an open large-scale dataset of naturalistic trajectories of half a million vehicles that have been collected by a one-of-a-kind experiment by a swarm of drones in the congested downtown area of Athens, Greece.

Free Mailing List

Please click https://www.freelists.org/list/et3 to subscribe. Welcome to join us and discuss emerging transportation technology testing.

Committee Members List

ET3 committee members, who have participated and cooperated in our activities are listed below:

  • Soyoung Ahn, Professor, University of Wisconsin – Madison (CAV)
  • Yaobin Chen, Director of Transportation and Autonomous Systems Institute, Professor, Indiana University–Purdue University Indianapolis (complex systems)
  • Leila Hajibabai, Assistant Professor, North Carolina State University (electric vehicles)
  • Jorge Laval, Associate Professor, Georgia Institute of Technology (traffic flow modeling)
  • Lingxi Li, Associate Professor, Indiana University–Purdue University Indianapolis (intelligent transportation systems, human-machine interface)
  • Henry Liu, Director of USDOT Midwest Center for Connected and Automated Transportation, Professor, University of Michigan (CAV)
  • Xiang Liu, Associate Professor, Rutgers University (smart rail)
  • Srinivas Peeta, Frederick R. Dickerson Chair & Professor, Georgia Institute of Technology (connected vehicles)
  • Huizhao Tu, Associate School Head, Professor, Tongji University, China (CAV)
  • Dan Work, Associate Professor, Vanderbilt University (CAV)
  • Zhigang Xu, Associate School Head, Professor, Chang’an University, China (CAV)
  • Zsolt Szalay, Head of Research and Innovation, ZalaZONE, Head of Department of Automotive Technologies, Budapest University of Technology and Economics (BME), Hungaria (automotive technologies)