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.
- Andrei Aksjonov (email@example.com), postdoctoral researcher.
- Kargar Eshagh (firstname.lastname@example.org), doctoral candidate.
- Ville Kyrki (email@example.com), professor.
Master Thesis on “Trajectory Planning and Tracking for Autonomous Vehicles Using Model Predictive Control Incorporating Vehicle Dynamics”
In this thesis, an MPC based trajectory planning and tracking method will be developed for an autonomous vehicle in simulation. A high fidelity driving simulator will be employed to incorporate vehicle dynamics in MPC constraints. The developed control must guarantee collision-free, comfortable and efficient driving performance in complex urban driving environment.
Master Thesis on “Research and Development of a Decision Making and Control Method for Autonomous Vehicles Combining Advanced Driver Assistance System with Reinforcement Learning”
In this thesis a method of training the agent with RL, where traffic participants’ safety is addressed with ADAS functions, will be investigated.