Cooperative Automated Transportation (CAT)

Cooperative Automated Transportation

Roadway safety in a cooperative automated world

Highway automation is not years away, or even days away. It’s here now, causing a number of state transportation agencies to react with initiatives related to preparing and supporting Connected Automated Vehicles (CAVs) on U.S. roadways.


Connected and Automated Vehicles (CAVs)

Cooperative Automated Transportation (CAT) deals with CAVs, which are vehicles capable of driving on their own with limited or no human involvement in navigation and control. Per the definition adopted by the National Highway Traffic Safety Administration (NHTSA), there are six levels of automation (Levels 0-2: driver assistance and Levels 3-5: HAV), each of which requires its own specification and marketplace considerations.


Vehicle-to-Everything (V2X) and Connected and Automated Vehicles (CAVs)

For traffic safety, vehicle-to-everything communications is the wireless exchange of critical safety and operational data between vehicles and anything else. The "X" could be roadway infrastructure, other vehicles, roadway workers or other safety and communication devices. ATSSA members are at the forefront of these technologies, and are working with stakeholders across new industries to see these innovations come to life.


Sensor Technology

CAVs rely on three main groups of sensors: camera, radar, and Light Detection and Ranging (LIDAR). The camera sensors capture moving objects and the outlines of roadway devices to get speed and distance data. Short- and long-range radar sensors work to detect traffic from the front and the back of CAVs. LIDAR systems produce three-dimensional images of both moving and stationary objects.


For more information about ATSSA’s efforts on CAT and CAV’s and their interaction with our member products check out the resources below.




Resources

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ATSSA & TRB announce winners of 2023 TCD Student Challenge

Arlington High School team is the first high school to win the contest

FREDERICKSBURG, Va. (Jan. 11, 2023) – The American Traffic Safety Services Association (ATSSA), in partnership with the National Academy of Sciences Transportation Research Board (TRB), announces the winners of the 2023 Traffic Control Device (TCD) Student Challenge.

Ten teams, made up primarily of engineering students from universities across the U.S., competed in the TCD Student Challenge, which was titled, Innovative Traffic Control Devices to Improve Vulnerable Road User Safety.”

The team from Arlington High School in Arlington, Mass., won the contest and was the first high school team in the history of the TCD Student Challenge to achieve that honor. Petru Sofio, below left, and Talia Askenazi, who are computer-aided drafting and design students, received first place for their project entitled, “Lenticular Traffic Signal.” 

A team from Auburn University placed second with its project entitled, “Rectangular Rapid-Flashing Beacon Supplement Strategy.” The team consisted of Department of Civil Engineering students pictured below, from left, team leader Zijie Zhao, the team leader, and teammates Stanton Freeman, Jessie Chea, Fanguian Yang, and Tonghui Li.

A team from Michigan State University placed third with its project entitled, “Smart Safety Using Connected Dynamic Messaging Panel and Flashing Crosswalk for People Walking and Biking.” The team consisted of, below from left, Sakar Pahari, Nischal Gupta, Gagan Gupta, and team leader Sagar Keshari.

“We always enjoy seeing students putting their skills to work solving real-world problems,” said ATSSA Innovation & Technical Services Manager Nagham "Melodie" Matout. “Finding ways to help vulnerable road users travel safely whether on foot, bike, wheelchair or with another aid is of critical importance. We appreciate the thought that went into the entries submitted this year.”

The TCD Student Challenge is open to high school, junior college, college and university students or teams of students who have an interest in transportation and an understanding of traffic control devices. Students in relevant fields such as transportation, human factors and technology-related curricula are particularly encouraged to participate.

Entries are judged on the ability of the idea to address the problem, applicability of the idea and its transferability to various environments and roadways, and feasibility of implementation.

“We commend the students from Arlington High School for being the first high school team to win the contest and congratulate all of the winners and each team that participated,” Matout said.

The three winning teams were chosen during the TRB Annual Meeting that started Sunday and runs through Thursday in Washington, D.C. Each winning team receives a cash prize ($1,500 for first place, $1,000 for second place and $500 for third place) and the opportunity to present their submissions to members of the roadway safety infrastructure industry at ATSSA’s 53rd Annual Convention & Traffic Expo in Phoenix, Feb. 17-21.

The following seven teams also competed in the 2023 challenge.

Davidson Academy of Reno (Nevada), Adrian Lin, “Conflict Early Warning Beacon.”

Northeastern University, Milad Tahmasebi, “Enhanced Flashing Yellow Arrow: A Dynamic Warning Mitigating Permitted Turn Conflicts with Cyclists and Pedestrians.”

Oregon State, Benjamin Fryback, team leader, and teammates Kezia Suwandhaputra, Logan Scott-Deeter and Amy Wyman, “Right Hook Conflict Mitigation Using Modified Signage.”

University of California, Irvine, Montana Rodriguez Reinoehl, team leader, and teammates Siwei Hu and Koti Reddy Allu, “A Non-Intrusive and Influence-Agnostic Impaired Driving Detection and Control Framework using LiDAR.”

University of Florida, Agustin Guerra, team leader, and teammate Liliana P. Salas of the University of Arizona, “Actuated Micromobility Users Presence Awareness System in Urban Arterials.”

University of Washington, Hao Frank Yang, team leader, and teammates Chenxi Liu, Yifan Ling, Cole Kopca and Sam Ricord, “Cooperative Traffic Signal Assistance System for Non-motorized Users and Disabilities Empowered by Computer Vision and Edge Artificial Intelligence.”

University of Wisconsin-Madison, Rei Tamaru, “Prepared for Abrupt Emergency of Vulnerable Road users at Geofencing Crosswalk.”

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