Policy networks have great potential to learn sophisticated driving policy under complicated
interaction between human drivers. However, it is hard for policy networks to satisfy safety …

To enable effective model-based control for autonomous vehicles, accurate vehicle states
and model parameters are required. Most of the state estimation and model-based control …

We propose a tactical homotopy-aware decision-making framework for game-theoretic
motion planning in urban environments. We model urban driving as a generalized Nash …

Autonomous driving planning is a challenging problem when the environment is
complicated. It is difficult for the planner to find a good trajectory that navigates autonomous …

This thesis introduces fail-safe motion planning as the first approach to guarantee legal
safety of autonomous vehicles in arbitrary traffic situations. The proposed safety layer …

The Institute of Measurement, Control, and Microtechnology at Ulm University has
investigated advanced driver assistance systems for decades and concentrated, in large …

In this study, a 3D sample trajectory planning approach for on-road scenarios is provided,
which can directly supply the coordinate and velocity of each planning point …

高速公路无人驾驶轨迹规划面临着实时性强, 安全性高的挑战. 为此, 提出一种分层抽样多动态
窗口的轨迹规划算法(Stratied sampling based multi-dynamic window trajectory planner …

In motion planning for automated vehicles, a thorough uncertainty consideration is crucial to
facilitate safe and convenient driving behavior. This work presents three motion planning …

Automated vehicles have immense potential to improve the safety of our roadways. In order
to handle the complex task of driving, they need the ability to make decisions regarding …