Master Thesis on “Data-Driven Diffusion Models for Enhancing Safety in Autonomous Vehicle Traffic Simulations”

Supervisor: Prof. Ville Kyrki (
Advisor: Dr. Shoaib Azam (

Keywords: Diffusion Models, Traffic simulation, Imitation Learning, Safe planning

Project Description

Simulation is a critical pillar in the testing and validation of autonomous vehicles (AVs), yet traditional methodologies often present a trade-off between realism and controllability, leaving much to be desired in replicating the complex dynamics of real-world driving. This limitation not only hampers AV development but also doubts the reliability of simulation-based validations. Recent advancements in data-driven diffusion models offer a promising avenue for bridging this gap by generating traffic scenarios that closely mirror real-world conditions. However, these models often overlook the intricate web of multi-agent interactions that AVs must navigate, a critical aspect that profoundly influences safety and operational efficiency.

This research aims to transcend these limitations by developing a data-driven diffusion model that elevates realism and controllability in simulations and intricately models the complex interactions between multiple agents for safe planning.


  • Review of relevant literature.
  • Develop a data-driven diffusion model for generating realistic, controllable, and safe traffic scenarios.
  • Extending the model to account for multi-agent interactions, considering both autonomous and human-driven agents.
  • Comparison of the implementation with the state-of-the-art methods.

Practical Information

Pre-requisites: Python(high), Deep Learning (high), and previous experience on RL is a big plus


Simulators: NVLabs-Traffic Simulation, Nuplan

Start:Available immediately