Enabling Technology

In early 2016, two separate events sparked excitement in the fields of autonomous vehicles and artificial intelligence. NVidia announced that their camera-based vehicle, driven by their Drive-PX computer, was trained to drive, using no prior knowledge and with just 72 hours of training video data; and AlphaGo, a computer program designed to play the game Go, defeated Lee Sedol, one of the world’s top-ranked players. These two events highlight the potential of artificial intelligence, as an alternative to the current focus on expensive sensors like Lidars. In both cases, the key enablers included rich training data sets, a learning algorithm, and a test platform.

Research Focusing on Enabling Technology

CAV Systems Incorporating Air Pollution Information from Traffic Congestion
Principal Investigator:
Krishnakumar Nedunuri, Ramanitharan Kandiah, & Deng Cao
Research Thrusts: Enabling TechnologyModeling & Implementation
Impact of Autonomous Freight Delivery on Trucking Operations
Principal Investigator:
Imad Al-Qadi, Jeffery Roesler, Yanfeng Ouyang, Hadi Meidani, & Hasan Ozer
Research Thrusts: Control & OperationsEnabling Technology, Infrastructure Design & ManagementModeling & Implementation
Real‐Time Distributed Optimization of Traffic Signal Timing
Principal Investigator:
Yafeng Yin, Siqian Shen, & Henry Liu
Research Thrusts: Control & OperationsEnabling TechnologyModeling & Implementation
Reliable V2V Communication Networks: Applications in Fuel-Efficient Platooning
Principal Investigator:
Sridhar Lakshmanan & Paul Richardson
Research Thrusts: Enabling Technology
xBOT – A Versatile Robot to Assist Testing of Autonomous-Connected Vehicles
Principal Investigator:
Sridhar Lakshmanan, Paul Richardson, & Weidong Xiang
Research Thrusts: Control & Operations, Enabling Technology, Infrastructure Design & Management