ACT Driving Simulator

2020 ~ Present


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:

  1. Mobile App Development
  2. Multi-Objective Optimization of Sustainability Objectives in Transportation Networks
  3. Machine Learning-Based Data Fusion for Congestion-Related Factors
  4. Driver Distraction and Traffic Accident Detection
  5. Lane-Change Control of Connected Autonomous Vehicles
  6. Retrofitting Longitudinal Behavior of Autonomous Vehicles
  7. Driving Simulator Experiments
  8. Reinforcement-Learning Based Roundabout Control
  9. Cybersecurity for Connected Autonomous Vehicles

Methods and Technologies

  • High-fidelity driving simulators
  • Biosensors (i.e., EEG, ECG, and eye tracking system)
  • Video image processing
  • Microscopic traffic simulation
  • Virtual reality/augmented reality
  • Experiment design
  • Behavioral and experimental psychology
  • Control theory
  • Optimization
  • Machine learning

Academic Majors of Interest

  • Computer Science
  • Human-Computer Interaction
  • OMSCS synchronous, case-by-case
  • Aerospace Engineering
  • Civil Engineering
  • Electrical Engineering
  • Environmental Engineering
  • Industrial Engineering
  • Mechanical Engineering
  • Robotics
  • Public Policy
  • Psychology

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.


Meeting Schedule & Location

Meeting Location 
Meeting Day 

Team Advisors

Dr. Srinivas Peeta
  • Civil and Environmental Engineering
Dr. Einat Tenenboim
  • Civil and Environmental Engineering