Recent advancements in perception for autonomous driving are driven by deep learning. In
order to achieve robust and accurate scene understanding, autonomous vehicles are …

Reinforcement learning (RL) can learn from past failures and has the potential to provide
self-improvement ability and higher-level intelligence. However, the current RL algorithms …

Reliable uncertainty estimation is crucial for perception systems in safe autonomous driving.
Recently, many methods have been proposed to model uncertainties in deep learning …

Obstacle avoidance is a fundamental operation for automated driving and its formulation
traditionally originates from robotics and decision making control fields. Given the high …

We propose a deep convolutional object detector for automated driving applications that
also estimates classification, pose and shape uncertainty of each detected object. The input …

This work presents a probabilistic deep neural network that combines LiDAR point clouds
and RGB camera images for robust, accurate 3D object detection. We explicitly model …

Behavior planning and decision-making are some of the biggest challenges for highly
automated systems. A fully automated vehicle (AV) is faced with numerous tactical and …

Two of the most known sources of danger for automated mobile platforms such as robots or
automated vehicles arise from objects in the environment that they cannot perceive …

We propose a method to compute an approximation of the moments of a discrete-time
stochastic polynomial system. We use the Carleman linearization technique to transform this …

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 …