5 accepted papers
In this article, we present a perception-based full-stack system for autonomous vehicle following that does not rely on accurate global localization or map data. Our architecture consists of modules for vehicle communication, localization, object tracking, waypoint management, static environment mod…
The successful deployment of deep learning-based techniques for autonomous systems is highly dependent on the data availability for the respective system in its deployment environment. Especially for unstructured outdoor environments, very few datasets exist for even fewer robotic platforms and scen
An increasing number of datasets sharing similar domains for semantic segmentation have been published over the past few years. But despite the growing amount of overall data, it is still difficult to train bigger and better models due to inconsistency in taxonomy and/or labeling policies of differe
The potential for deploying autonomous systems can be significantly increased by improving the perception and interpretation of the environment. However, the development of deep learning-based techniques for autonomous systems in unstructured outdoor environments poses challenges due to limited data…
We present a novel approach to trajectory generation that enables an autonomous vehicle to accurately follow a lead vehicle tracked by on-board sensors. In contrast to other approaches, we ignore the structure of the environment (e.g., lane markings), focusing purely on following the path driven by…