The ACT (Autonomous and Connected Transportation) Lab is focused on understanding the interactions between drivers/travelers, emerging vehicular technologies, and novel infrastructure designs. To this aim, a high-fidelity full-cab driving simulator is employed for data collection. Based on the data collected in the simulated environment, as well as data from other sources, the research team develops analytical models and performs data analytics to predict and support the future of autonomous and connected transportation.
Issues Involved or Addressed
Towards gaining a better understanding of the interactions between users, autonomous vehicles, and the infrastructure, the ACT Lab research team focuses on the following projects:
- Mobile app development
- Longitudinal control of CAVs
- Lane-Change Control of CAVs
- Reinforcement learning-based speed control for CAVs with heterogeneous inputs
- Driving simulator experiments
- Driver Status monitoring using computer vision technology
- Learning-based approaches in Transportation
- Cybersecurity for connected vehicles
- Multi-objective optimization of ridesourcing systems with AVs
Methods and Technologies
Academic Majors of Interest
Preferred Interests and Preparation
CEE, ISyE: Background/interest in connected and autonomous vehicle, smart and sustainable city, transportation engineering, human factors, analytical modeling, optimization, data analytics, machine learning, etc.
ECE, CS, ME: Background/interest in human-machine interactions, machine learning, control theory, image processing, augmented reality, virtual reality, robotics, etc.
PSYCH: Background/interest in human factors, cognitive/applied psychology, experimental psychology, human in the loop experiments, etc.