Eco-driving in urban area :
A eco driving style (motion planning) is studied in this project for the pursuit of fuel economy and safety. Specifically, an efficient searching algorithm in state space is proposed for the optimal motion planning of a single vehicle traversing a signalized intersection. This deterministic algorithm is extended to a stochastic scenario, where the vehicle only has partial information of the traffic light. Here a Markov Decision Process (MDP) based model is used to help reduce the fuel cost for traversing the intersection.
High Efficiency Electric Vehicle Powertrain :
“Range anxiety” continues to be a major hurdle to large scale adoption of electric vehicles, and this project attempts to address that with an unconventional powertrain architecture. In this project we propose an architecture inspired by Electronic Continuously Variable Transmissions (E-CVTs) used successfully in hybrid electric vehicles. The E-CVTs employ Motor-Generator Units (MGUs) and Planetary Gear Trains (PGTs), and pose a controls and optimization problem to balance the ability to deliver the right speed ratio at maximum efficiency, and that is the focus of this project.
Safe Core Architecture : –To be updated–
Virtual Autonomous Driving Simulator : –To be updated–
Health Monitoring for Cyber Physical Systems : –To be updated–
Dynamic Watermarking for Cyber Security in Autonomous Vehicles : –To be updated–
Formalization of Human-AV communication : –To be updated–
Auto-Pedestrian : –To be updated–
Vehicle Driving Simulator : –To be updated–
Reconfigurable Autonomous Driving Test Environment : –To be updated–