Safe Model Predictive Control

Safe Model Predictive Control (Safe MPC) aims to ensure that a physical system’s safety constraints are satisfied with high probability. Our research is on extending constrained MPC methods to cope with probabilistic safety constraints. We further research modeling uncertainty of dynamics to ensure safe exploration when combined with safety constraints learned in simulation, and learning powerful data-efficient surrogate models for complex dynamics.

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, […]

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 […]

Autonomous Driving

Driverless cars and autonomous driving have shown major progress recently with the use of machine learning to learn driving behaviors from human demonstrations. However, the uptake of these is still limited, especially since the safety of such data-driven solutions is difficult to guarantee or even assess. Our work in autonomous driving targets the question how […]

Robot Control

Robotic tasks in real-world applications generally involve uncertain, stochastic and dynamic environments. Pre-programming based solutions either do not work or give unsatisfactory results in such environments. This requires to generate cautious control strategies that provide optimum actions to perform the desired task while considering the effects of the uncertainties in the environment. Robot control aims […]