Deep Multi-Agent Reinforcement Learning for Decision Making in Autonomous Driving Systems
A high intelligence decision-making system is crucial for urban autonomous driving with dense surrounding dynamic objects. It must be able to handle the complex road geometry and topology, complex multi-agent interactions, and accurately follow the high-level commands such as routing information. The vehicle must apply sophisticated negotiation skills with other road users when overtaking, giving way, merging, passing an intersection, taking left and right turns and while pushing ahead in unstructured urban roadways. Also, the vehicle must balance between the unpredictable behavior of other agents in this multi-agent scenario and at the same time not to be too defensive so that normal traffic flow is maintained.
The purpose of this project is to present and develop efficient machine learning-based approaches to solve the decision-making problem in autonomous driving systems in multi-agent environments.
People involved
- Daulet Baimukashev (daulet.baimukashev@aalto.fi), doctoral candidate.
- Gokhan Alcan (gokhan.alcan@aalto.fi), postdoctoral researcher.
- Eshagh Kargar (eshagh.kargar@aalto.fi), doctoral candidate.
- Ville Kyrki (ville.kyrki@aalto.fi), professor.
Project updates
Deep Multi-Agent Reinforcement Learning for Decision Making in Autonomous Driving Systems
A high intelligence decision-making system is crucial for urban autonomous driving with dense surrounding dynamic objects. It must be able to handle the complex road geometry and topology, complex multi-agent interactions, and accurately follow the high-level commands such as routing information. The vehicle must apply sophisticated negotiation skills with other road users when overtaking, giving […]