Author: Gökhan Alcan
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.
Master Thesis on “Inverse Reinforcement Learning for Driving Behavior Modeling”
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.
Master Thesis on “Interaction Modeling for Autonomous Driving Using Graph Neural Networks”
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.
Master Thesis on “Model-based Dynamic Manipulation of Deformable Objects”
The goal of this thesis is to develop model-based approaches for the dynamic manipulation of a deformable object. Particularly, the focus will be on the task of throwing a lasso around a target (a bollard) with a robotic arm.
Master Thesis on “Safe Constrained Differential Dynamic Programming”
In this thesis, an extensive investigation of constrained DDP methods will be performed and the major selected ones will be implemented in simulation environment for trajectory optimizations of different robots such as a simple point robot, 2D car-like robot, 3D quadrotor robot and cart-pole system. In this context, the methods will be compared in terms of convergence speed, computational complexity, sensitivity to initializations and parameter selections.
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.
Data Driven Nonlinear Dynamic Models for Predicting Heavy-Duty Diesel Engine Torque and Combustion Emissions
Robotic Seminar Series – 21st February 2020. Speaker: Gökhan Alcan.
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 […]