This research work uses Learning MPC for a predictive cruise controller which iteratively improves the fuel economy of a vehicle traveling along the same route every day. Our approach uses historical data from previous trip iterations to improve vehicle performance while guaranteeing a desired arrival time.

This research work develops a hardware-in-the-loop (HIL) simulation setup for repeatable testing of Connected Automated Vehicles (CAVs) in dynamic, real-world scenarios using a combination of actual vehicle hardware and simulated environments.

This research proposes a framework for a shared perception system suitable for CAVs and explains the algorithms used in the system. Experiments demonstrate the benefit of the shared perception system for automated vehicles in uncertain environments.