This research aims to explore whether learning and leveraging world models can also be beneficial in visual representation learning for autonomous driving in a closed-loop settings.

This thesis aims to develop a data-driven diffusion model that elevates realism and controllability in simulations and intricately models the complex interactions between multiple agents for safe planning

The goal of this thesis is to devise an algorithm that combines the advantage of both IL and MBRL for robust and safe planning for autonomous driving. In this context, the thesis is expected to include implementations of IL and MBRL algorithms and fuse them for planning tasks.