Towards 3D LiDAR-based semantic scene understanding of 3D point cloud sequences: The SemanticKITTI Dataset

A holistic semantic scene understanding exploiting all available sensor modalities is a core capability to master self-driving in complex everyday traffic. To this end, we present the SemanticKITTI dataset that provides point-wise semantic annotations of Velodyne HDL-64E point clouds of the KITTI Odometry Benchmark. Together with the data, we also published three benchmark tasks for semantic scene understanding covering different aspects of semantic scene understanding: (1) semantic segmentation for point-wise classification using single or multiple point clouds as input; (2) semantic scene completion for predictive reasoning on the semantics and occluded regions; and (3) panoptic segmentation combining point-wise classification and assigning individual instance identities to separate objects of the same class. In this article, we provide details on our dataset showing an unprecedented number of fully annotated point cloud sequences, more information on our labeling process to efficiently annotate such a vast amount of point clouds, and lessons learned in this process. The dataset and resources are available at http://www.semantic-kitti.org .

Adaptive Modeling for Risk-Aware Decision Making

This thesis aims to provide a foundation for risk-aware decision making. Decision making under uncertainty is a core capability of an autonomous agent. A cornerstone for with long-term autonomy and safety is risk-aware decision making. A risk-aware model fully accounts for a known set of risks in the environment, with respect to the problem under consideration, and the process of decision making using such a model is risk-aware decision making. Formulating risk-aware models is critical for robust reasoning under uncertainty, since the impact of using less accurate models may be catastrophic in extreme cases due to overly optimistic view of problems. I propose adaptive modeling, a framework that helps balance the trade-off between model simplicity and risk awareness, for different notions of risks, while remaining computationally tractable.

Recognizing our hybridity and interconnectedness: Implications for social and environmental justice

A core capability for sociologists who wish to respond to the complex interconnected social, cultural, political and economic challenges will be the ability to transcend disciplinary boundaries and to work with diverse perspectives. Thus those who inform the argument for this article include De Waal and Dawkins (primatology and philosophy), Kymlicka and Donaldson (animal rights and shared habitat), Hirschman and Hannah Arendt (on economics and politics), Amartya Sen (on economics and morality), Stuart Hall (on identity) and Martha Nussbaum (on social justice). The work of Stiglitz on wellbeing stocks is extended through drawing on Vandana Shiva (on the intersections spanning economics, politics and the environment) and a recognition of our interconnectedness as part of a living system. This provides the basis for intersectional policy approaches to address violence against the planet and violence against those without a voice. This capability is important if we are to inform praxis on governing the Anthropocene, in order to protect both human and animal rights along with their shared and separate habitats.