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.
- Gokhan Alcan (firstname.lastname@example.org), postdoctoral researcher.
- Shoaib Azam (email@example.com), postdoctoral researcher.
- Daulet Baimukashev (firstname.lastname@example.org), doctoral candidate.
- Kargar Eshagh (email@example.com), doctoral candidate.
- Ville Kyrki (firstname.lastname@example.org), professor.
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.
The goal of this thesis is to formulate driving behavior modeling as an inverse reinforcement learning (IRL) problem. In this context, the thesis is expected to include implementations of different IRL methods and compare them concerning prediction capabilities.
The goal of this thesis is to model the interactions among autonomous agents in dense traffic using GNN. In this context, the thesis is expected to include an ablation study to identify which input features (such as velocity, distance to other vehicles, etc.) has the maximum effect on prediction and the effects of changing the graph structure on the interactions among the vehicles.