Researchers have proposed a personalized longitudinal motion planning policy for intelligent vehicles that combines reinforcement learning with imitation learning. The approach is designed to reduce ...

Traditional approaches to autonomous vehicles (AVs) rely on using millions of miles of driving data in conjunction with even more miles of simulated data as inputs to supervised machine learning ...

In recent years, the integration of AI systems in vehicles has transformed the driving experience, enabling cars to become more adaptive and intelligent. These AI systems are now capable of learning ...

This project focuses on developing a state-of-the-art safety verification framework for connected autonomous vehicles (CAV). Leveraging the connected automated vehicle platforms and the high-fidelity ...

Researchers have proposed an integrated eco-driving framework for fuel cell hybrid electric vehicles in multi-lane highway ...