Decision-Making in Autonomous Driving with Data- and Model-Based Methods Combination Ensuring Road Safety Aspects
In addition to transportation comfort and efficiency, autonomous vehicles provide a vital improvement in traffic safety by minimizing impact of human factor. In this project, the data- and model-based approaches will be combined to develop a safety-oriented decision-making algorithm for autonomous driving systems. The main assessment criteria for the vehicle performance actuation is traffic safety, which includes other road users and ego vehicle itself. The algorithm will be first evaluation in simulation with real driving data. Therefore, the method will be tested on an experimental vehicle.
People involved
- Gokhan Alcan (gokhan.alcan@aalto.fi), postdoctoral researcher.
- Shoaib Azam (shoaib.azam@aalto.fi), postdoctoral researcher.
- Daulet Baimukashev (daulet.baimukashev@aalto.fi), doctoral candidate.
- Kargar Eshagh (eshagh.kargar@aalto.fi), doctoral candidate.
- Ville Kyrki (ville.kyrki@aalto.fi), professor.
Project updates
Master Thesis on “Predictive World Models for End-to-End Autonomous Driving”
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.
Master Thesis on “Data-Driven Diffusion Models for Enhancing Safety in Autonomous Vehicle Traffic Simulations”
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
Master Thesis on “Deep Imitative Models for Safe Planning of Autonomous Driving”
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.