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Engineering Professor Gets NSF CAREER Grant

Funding Will Support Research into Connected and Self-driving Cars

Asst. Prof. Danjue Chen
Asst. Prof. Danjue Chen is an expert on modeling and control of connected and automated vehicles, traffic flow theory and simulation, cyber-physical systems of smart vehicles and human-machine interaction.

05/05/2020
By Edwin L. Aguirre

Civil and Environmental Engineering Asst. Prof. Danjue Chen’s research into the complex traffic interactions between self-driving and human-driven cars has won a five-year, $500,000 faculty early-career development award from the National Science Foundation (NSF).

Called the CAREER award, the highly competitive annual program selects the nation’s best young university scholars who, according to the NSF, “most effectively integrate research and education within the context of the mission of their organization.”

Chen, who joined UMass Lowell in 2016, will use the CAREER award to pursue her research project, “Conflicting Traffic Streams with Mixed Traffic: Modeling and Control.” Conflicting traffic streams refer to traffic that are moving in different directions such as on highways and at intersections.

Connected and automated vehicle, or CAV, technologies hold enormous potential in solving some of the most challenging problems in the country’s transportation system, including how to mitigate traffic congestion, improve road safety, increase fuel economy and reduce emissions,” says Chen. 
 
Connected vehicles are those that can communicate with other vehicles and roadway infrastructure using wireless networks, while automated vehicles, also known as autonomous or driverless cars, use cameras, radar, lidar, GPS and computer vision to operate the vehicle with little or no human assistance.
Traffic congestion on the road
Chen’s National Science Foundation CAREER grant will enable her to study the complex traffic interactions between self-driving and human-driven cars.

“My research will study how CAVs interact with conventional, human-driven vehicles on highways in real time and in real-life scenarios, such as on-ramp merging, lane drops (narrowing of roads) and lane changing. I hope to develop robust strategies to improve traffic flow while reducing safety risk,” says Chen.

According to a report published by the National Highway Traffic Safety Administration, nearly 27,000 people died in motor vehicle crashes during the first nine months of 2019. Many crashes on U.S. roads involve speeding and driving while impaired, distracted or drowsy. Depending on the level of automation, CAVs could potentially increase road safety by reducing human error. 

“This will be the first research that investigates mixed traffic in conflicting streams using experimental and analytical approaches,” notes Chen. “As CAV technologies are new, there is a lack of empirical data to reveal the cars’ behaviors in such complex settings. Also, previous research focuses mostly on traffic streams involving one vehicle type, which is overly simplified.” 

Assisting Chen in the project are transportation engineering Ph.D. students Xi Zhang and Tienan Li. The team will collect experimental data and use analytical and artificial intelligence-based methods to develop traffic control strategies.  

“This research is an important step in trying to understand the impact of CAV technologies. Our results will also guide the development of roadway design and policies and the long-term planning for future transportation systems,” says Chen. 

The Road to Research

Chen earned a bachelor’s degree in environmental science from Peking University in China in 2007 and a doctorate in civil engineering from Georgia Institute of Technology in 2012.

She is a member of the U.S. Transportation Research Board’s Traffic Flow Theory Committee and a founding member of the board’s subcommittee on Traffic Flow Modeling for Connected and Automated Vehicles. 

Last year, she received two NSF grants totaling $282,000 to support her traffic research. The first grant, worth nearly $182,000 over three years, aims to understand the impact of automated vehicles on traffic flow using empirical data. The second grant, worth nearly $100,000 over two years, focuses on the interaction between human-driven vehicles and different classes of CAVs during traffic disturbances that cause momentary reduction in traffic flow and speed.

In addition to the NSF, she has also received research funding from the U.S. Department of Transportation and several state departments of transportation.