Lars Ohnemus

Orcid: 0009-0009-0612-506X

According to our database1, Lars Ohnemus authored at least 12 papers between 2020 and 2026.

Collaborative distances:
  • Dijkstra number2 of five.
  • Erdős number3 of four.

Timeline

Legend:

Book  In proceedings  Article  PhD thesis  Dataset  Other 

Links

On csauthors.net:

Bibliography

2026
FOGCAST-Towards Uncertainty-Aware Forecasting of Interconnected Environments Under Partial Observations from Mobile Agents Using Semi-Static Scene Graphs.
Proceedings of the 12th International Conference on Automation, Robotics and Applications, 2026

2025
FOGMACHINE - Leveraging Discrete-Event Simulation and Scene Graphs for Modeling Hierarchical, Interconnected Environments under Partial Observations from Mobile Agents.
CoRR, October, 2025

ARK-V1: An LLM-Agent for Knowledge Graph Question Answering Requiring Commonsense Reasoning.
CoRR, September, 2025

A Comparative Analysis of Multi-Modal Semantic Perception Tasks and Datasets for Mobile Robotics.
Proceedings of the 21st IEEE International Conference on Automation Science and Engineering, 2025

2024
Combining Visual Saliency Methods and Sparse Keypoint Annotations to Create Object Representations for Providently Detecting Vehicles at Night.
Proceedings of the IEEE Intelligent Vehicles Symposium, 2024

Design and Real-World Application of a Flexible Mobile Robot System for Urban Logistics.
Proceedings of the 29th IEEE International Conference on Emerging Technologies and Factory Automation, 2024

2023
Provident vehicle detection at night for advanced driver assistance systems.
Auton. Robots, March, 2023

2022
Combining Visual Saliency Methods and Sparse Keypoint Annotations to Providently Detect Vehicles at Night.
CoRR, 2022

2021
A Dataset for Provident Vehicle Detection at Night.
Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems, 2021

2020
Provident Vehicle Detection at Night: The PVDN Dataset.
CoRR, 2020

The Autonomous Racing Software Stack of the KIT19d.
CoRR, 2020

Provident Detection of Vehicles at Night.
Proceedings of the IEEE Intelligent Vehicles Symposium, 2020


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