Hugo Grimmett

422 total citations
9 papers, 128 citations indexed

About

Hugo Grimmett is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence and Automotive Engineering. According to data from OpenAlex, Hugo Grimmett has authored 9 papers receiving a total of 128 indexed citations (citations by other indexed papers that have themselves been cited), including 6 papers in Computer Vision and Pattern Recognition, 5 papers in Artificial Intelligence and 4 papers in Automotive Engineering. Recurrent topics in Hugo Grimmett's work include Autonomous Vehicle Technology and Safety (4 papers), Anomaly Detection Techniques and Applications (3 papers) and Robotic Path Planning Algorithms (3 papers). Hugo Grimmett is often cited by papers focused on Autonomous Vehicle Technology and Safety (4 papers), Anomaly Detection Techniques and Applications (3 papers) and Robotic Path Planning Algorithms (3 papers). Hugo Grimmett collaborates with scholars based in United Kingdom, United States and Switzerland. Hugo Grimmett's co-authors include Ingmar Posner, Rudolph Triebel, Rohan Paul, Peter Ondrúška, Błażej Osiński, Yawei Ye, Qiangui Huang, Ana Ferreira, Ashesh Jain and Paul Newman and has published in prestigious journals such as The International Journal of Robotics Research, 2022 International Conference on Robotics and Automation (ICRA) and 2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).

In The Last Decade

Hugo Grimmett

9 papers receiving 118 citations

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Hugo Grimmett United Kingdom	6	69	51	48	31	28	9	128
Bencheng Liao China	4	88 1.3×	52 1.0×	29 0.6×	27 0.9×	13 0.5×	8	146
Ibrahim Sobh France	6	77 1.1×	59 1.2×	54 1.1×	15 0.5×	32 1.1×	15	159
Yibing Zhao China	5	105 1.5×	39 0.8×	27 0.6×	16 0.5×	10 0.4×	17	163
Seunghak Shin South Korea	6	59 0.9×	48 0.9×	21 0.4×	37 1.2×	29 1.0×	12	132
Éloi Zablocki France	7	94 1.4×	59 1.2×	107 2.2×	9 0.3×	10 0.4×	11	214
Nicholas Rhinehart United States	7	104 1.5×	31 0.6×	84 1.8×	10 0.3×	35 1.3×	17	166
Víctor Jiménez Spain	7	41 0.6×	33 0.6×	78 1.6×	25 0.8×	13 0.5×	15	147
Blake Wulfe United States	5	40 0.6×	74 1.5×	39 0.8×	17 0.5×	46 1.6×	6	131
Georg Volk Germany	8	126 1.8×	74 1.5×	48 1.0×	23 0.7×	15 0.5×	17	225
Jan Effertz Germany	7	73 1.1×	65 1.3×	34 0.7×	42 1.4×	10 0.4×	14	113

Countries citing papers authored by Hugo Grimmett

Since Specialization

Citations

This map shows the geographic impact of Hugo Grimmett'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 Hugo Grimmett with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Hugo Grimmett more than expected).

Fields of papers citing papers by Hugo Grimmett

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Hugo Grimmett. 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 Hugo Grimmett. The network helps show where Hugo Grimmett may publish in the future.

Co-authorship network of co-authors of Hugo Grimmett

This figure shows the co-authorship network connecting the top 25 collaborators of Hugo Grimmett. A scholar is included among the top collaborators of Hugo Grimmett 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 Hugo Grimmett. Hugo Grimmett is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

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9 of 9 papers shown

1.

Naseer, Tayyab, et al.. (2022). Quantity over Quality: Training an AV Motion Planner with Large Scale Commodity Vision Data. 2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). 5752–5759. 1 indexed citations

2.

Ye, Yawei, Ana Ferreira, Błażej Osiński, et al.. (2022). SafetyNet: Safe Planning for Real-World Self-Driving Vehicles Using Machine-Learned Policies. 2022 International Conference on Robotics and Automation (ICRA). 897–904. 42 indexed citations

3.

Chen, Long, Błażej Osiński, Yawei Ye, et al.. (2021). What data do we need for training an AV motion planner?. 1066–1072. 7 indexed citations

4.

Grimmett, Hugo, et al.. (2018). Visual Vehicle Tracking Through Noise and Occlusions Using Crowd-Sourced Maps. 4531–4538. 2 indexed citations

5.

Dabisias, Giacomo, Emanuele Ruffaldi, Hugo Grimmett, & Peter Ondrúška. (2018). VALUE: Large Scale Voting-Based Automatic Labelling for Urban Environments. abs 1608 6993. 1–6. 1 indexed citations

6.

Grimmett, Hugo, et al.. (2018). Predicting trajectories of vehicles using large-scale motion priors. 1639–1644. 9 indexed citations

7.

Grimmett, Hugo, Lina María Paz, Pedro Piniés, et al.. (2015). Integrating metric and semantic maps for vision-only automated parking. 2159–2166. 22 indexed citations

8.

Grimmett, Hugo, Rudolph Triebel, Rohan Paul, & Ingmar Posner. (2015). Introspective classification for robot perception. The International Journal of Robotics Research. 35(7). 743–762. 25 indexed citations

9.

Grimmett, Hugo, Rohan Paul, Rudolph Triebel, & Ingmar Posner. (2013). Knowing when we don't know: Introspective classification for mission-critical decision making. 2. 4531–4538. 19 indexed citations

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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