Claudius Gläser

1.7k total citations · 1 hit paper
31 papers, 1.1k citations indexed

About

Claudius Gläser is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence and Automotive Engineering. According to data from OpenAlex, Claudius Gläser has authored 31 papers receiving a total of 1.1k indexed citations (citations by other indexed papers that have themselves been cited), including 12 papers in Computer Vision and Pattern Recognition, 12 papers in Artificial Intelligence and 10 papers in Automotive Engineering. Recurrent topics in Claudius Gläser's work include Autonomous Vehicle Technology and Safety (10 papers), Speech and Audio Processing (6 papers) and Advanced Neural Network Applications (5 papers). Claudius Gläser is often cited by papers focused on Autonomous Vehicle Technology and Safety (10 papers), Speech and Audio Processing (6 papers) and Advanced Neural Network Applications (5 papers). Claudius Gläser collaborates with scholars based in Germany, Japan and Portugal. Claudius Gläser's co-authors include Klaus Dietmayer, Lars Rosenbaum, Christian Schütz, Heinz Hertlein, Fabian Timm, Di Feng, W. Wiesbeck, Frank Joublin, Christian Goerick and Martin Heckmann and has published in prestigious journals such as IEEE Transactions on Intelligent Transportation Systems, IEEE Transactions on Audio Speech and Language Processing and eScholarship (California Digital Library).

In The Last Decade

Claudius Gläser

26 papers receiving 1.0k citations

Hit Papers

Deep Multi-Modal Object Detection and Semantic Segmentati... 2020 2026 2022 2024 2020 250 500 750
What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

  1. Deep Multi-Modal Object Detection and Semantic Segmentation for Autonomous Driving: Datasets, Methods, and Challenges
    2020 864 citations Di Feng, Christian Schütz et al. IEEE Transactions on Intelligent Transportation Systems profile →

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Claudius Gläser Germany 10 619 257 256 249 94 31 1.1k
Heinz Hertlein United Kingdom 3 553 0.9× 221 0.9× 191 0.7× 185 0.7× 79 0.8× 5 875
Senthil Yogamani France 18 693 1.1× 183 0.7× 189 0.7× 232 0.9× 92 1.0× 61 1.0k
Fabian Timm Germany 8 690 1.1× 238 0.9× 204 0.8× 194 0.8× 80 0.9× 12 1.2k
Di Feng Germany 10 738 1.2× 325 1.3× 275 1.1× 267 1.1× 92 1.0× 10 1.3k
Christian Schütz Switzerland 8 644 1.0× 219 0.9× 192 0.8× 286 1.1× 91 1.0× 23 1.1k
Xinjing Cheng China 10 859 1.4× 278 1.1× 222 0.9× 251 1.0× 89 0.9× 12 1.2k
Lewei Lu China 9 881 1.4× 328 1.3× 203 0.8× 195 0.8× 53 0.6× 19 1.3k
Xinyu Huang United States 13 641 1.0× 131 0.5× 283 1.1× 184 0.7× 86 0.9× 36 991
Yuenan Hou China 16 856 1.4× 319 1.2× 410 1.6× 232 0.9× 201 2.1× 27 1.4k
Qichuan Geng China 7 530 0.9× 144 0.6× 255 1.0× 157 0.6× 81 0.9× 15 807

Countries citing papers authored by Claudius Gläser

Since Specialization
Citations

This map shows the geographic impact of Claudius Gläser's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Claudius Gläser with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Claudius Gläser more than expected).

Fields of papers citing papers by Claudius Gläser

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Claudius Gläser. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Claudius Gläser. The network helps show where Claudius Gläser may publish in the future.

Co-authorship network of co-authors of Claudius Gläser

This figure shows the co-authorship network connecting the top 25 collaborators of Claudius Gläser. A scholar is included among the top collaborators of Claudius Gläser based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Claudius Gläser. Claudius Gläser is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
1.
2.
Gläser, Claudius, et al.. (2024). Revisiting Out-of-Distribution Detection in LiDAR-based 3D Object Detection. 2806–2813. 1 indexed citations
3.
Gläser, Claudius, et al.. (2023). Self-Supervised Occupancy Grid Map Completion for Automated Driving. 3 indexed citations
4.
Gläser, Claudius, et al.. (2023). Group Regression for Query Based Object Detection and Tracking. 28. 2677–2684. 1 indexed citations
5.
Belagiannis, Vasileios, et al.. (2022). A Multi-Task Recurrent Neural Network for End-to-End Dynamic Occupancy Grid Mapping. 2022 IEEE Intelligent Vehicles Symposium (IV). 315–322. 16 indexed citations
6.
Gläser, Claudius, et al.. (2022). Transformers for Object Detection in Large Point Clouds. 2022 IEEE 25th International Conference on Intelligent Transportation Systems (ITSC). 832–838. 3 indexed citations
7.
Gläser, Claudius, et al.. (2022). Improved Orientation Estimation and Detection with Hybrid Object Detection Networks for Automotive Radar. 2022 IEEE 25th International Conference on Intelligent Transportation Systems (ITSC). 111–117. 27 indexed citations
8.
Feng, Di, et al.. (2022). DeepFusion: A Robust and Modular 3D Object Detector for Lidars, Cameras and Radars. 2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). 560–567. 22 indexed citations
9.
Condurache, Alexandru Paul, et al.. (2020). Deep Radar Sensor Models for Accurate and Robust Object Tracking. 1–6. 6 indexed citations
10.
Feng, Di, Christian Schütz, Lars Rosenbaum, et al.. (2020). Deep Multi-Modal Object Detection and Semantic Segmentation for Autonomous Driving: Datasets, Methods, and Challenges. IEEE Transactions on Intelligent Transportation Systems. 22(3). 1341–1360. 864 indexed citations breakdown →
11.
Baumann, Ulrich, et al.. (2018). Classifying Road Intersections using Transfer-Learning on a Deep Neural Network. 683–690. 11 indexed citations
12.
Gläser, Claudius, et al.. (2014). Environment perception for inner-city driver assistance and highly-automated driving. 13. 1270–1275. 11 indexed citations
13.
Gläser, Claudius, et al.. (2013). The narrow road assistant - Next generation advanced driver assistance in inner-city. 2173–2180. 9 indexed citations
14.
Gläser, Claudius & Frank Joublin. (2011). Firing Rate Homeostasis for Dynamic Neural Field Formation. 3(4). 285–299. 5 indexed citations
15.
Gläser, Claudius, Martin Heckmann, Frank Joublin, & Christian Goerick. (2010). Robust Formant Tracking in Echoic and Noisy Environments.. 1–4.
16.
Gläser, Claudius & Frank Joublin. (2010). Perceptually Grounded Word Meaning Acquisition: A Computational Model. eScholarship (California Digital Library). 32(32).
17.
Gläser, Claudius & Frank Joublin. (2010). An adaptive Normalized Gaussian Network and its application to online category learning. 9. 1–8. 2 indexed citations
18.
Heckmann, Martin, Claudius Gläser, Frank Joublin, & Kazuhiro Nakadai. (2010). Applying geometric source separation for improved pitch extraction in human-robot interaction. 2602–2605.
19.
Rodemann, Tobias, Martin Heckmann, Claudius Gläser, Frank Joublin, & Christian Goerick. (2010). Towards Speech Acquisition in Natural Interaction on ASIMO. Journal of the Robotics Society of Japan. 28(1). 18–22. 1 indexed citations
20.

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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