Di Feng

2.3k total citations · 1 hit paper
10 papers, 1.3k citations indexed

About

Di Feng is a scholar working on Computer Vision and Pattern Recognition, Automotive Engineering and Cognitive Neuroscience. According to data from OpenAlex, Di Feng has authored 10 papers receiving a total of 1.3k indexed citations (citations by other indexed papers that have themselves been cited), including 6 papers in Computer Vision and Pattern Recognition, 4 papers in Automotive Engineering and 4 papers in Cognitive Neuroscience. Recurrent topics in Di Feng's work include Advanced Neural Network Applications (6 papers), Tactile and Sensory Interactions (4 papers) and Autonomous Vehicle Technology and Safety (4 papers). Di Feng is often cited by papers focused on Advanced Neural Network Applications (6 papers), Tactile and Sensory Interactions (4 papers) and Autonomous Vehicle Technology and Safety (4 papers). Di Feng collaborates with scholars based in Germany, United States and Sweden. Di Feng's co-authors include Klaus Dietmayer, Lars Rosenbaum, Fabian Timm, Christian Schütz, Heinz Hertlein, Claudius Gläser, W. Wiesbeck, Gordon Cheng, Mohsen Kaboli and Ali Harakeh and has published in prestigious journals such as Sensors, IEEE Transactions on Intelligent Transportation Systems and Autonomous Robots.

In The Last Decade

Di Feng

10 papers receiving 1.3k citations

Hit Papers

align trajectories

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.

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						Papers	Cites
Di Feng Germany	10	738	325	275	267	131	10	1.3k
Eduardo Romera Spain	18	1.5k 2.1×	394 1.2×	376 1.4×	407 1.5×	58 0.4×	27	2.0k
Hongkai Yu United States	22	1.3k 1.7×	438 1.3×	383 1.4×	196 0.7×	131 1.0×	85	1.9k
Christoph Mertz United States	17	826 1.1×	222 0.7×	379 1.4×	306 1.1×	150 1.1×	45	1.8k
Min Sun United States	21	1.6k 2.1×	565 1.7×	117 0.4×	326 1.2×	61 0.5×	86	2.0k
Fabian Timm Germany	8	690 0.9×	238 0.7×	204 0.7×	194 0.7×	47 0.4×	12	1.2k
Michael Wolf United States	15	425 0.6×	117 0.4×	158 0.6×	232 0.9×	203 1.5×	43	1.2k
Zsolt Kira United States	22	1.2k 1.6×	765 2.4×	128 0.5×	245 0.9×	158 1.2×	68	1.9k
J. Nuevo Spain	12	910 1.2×	88 0.3×	234 0.9×	430 1.6×	53 0.4×	22	1.8k

Countries citing papers authored by Di Feng

Since Specialization

Citations

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

Fields of papers citing papers by Di Feng

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Di Feng

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

All Works

Sort: Min cites: Since: Top N: Style:

10 of 10 papers shown

1.

Feng, Di, Ali Harakeh, Steven L. Waslander, & Klaus Dietmayer. (2021). A Review and Comparative Study on Probabilistic Object Detection in Autonomous Driving. IEEE Transactions on Intelligent Transportation Systems. 23(8). 9961–9980. 179 indexed citations

2.

Feng, Di, Zining Wang, Yiyang Zhou, et al.. (2021). Labels are Not Perfect: Inferring Spatial Uncertainty in Object Detection. IEEE Transactions on Intelligent Transportation Systems. 23(8). 9981–9994. 17 indexed citations

3.

Feng, Di, Yiyang Zhou, Chenfeng Xu, Masayoshi Tomizuka, & Wei Zhan. (2021). A Simple and Efficient Multi-task Network for 3D Object Detection and Road Understanding. 2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). 7067–7074. 19 indexed citations

4.

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 →

5.

Feng, Di, Yiyang Zhou, Lars Rosenbaum, et al.. (2020). Inferring Spatial Uncertainty in Object Detection. 5792–5799. 16 indexed citations

6.

Feng, Di, Wei Xiao, Lars Rosenbaum, Atsuto Maki, & Klaus Dietmayer. (2019). Deep Active Learning for Efficient Training of a LiDAR 3D Object Detector. 667–674. 50 indexed citations

7.

Kaboli, Mohsen, Kunpeng Yao, Di Feng, & Gordon Cheng. (2018). Tactile-based active object discrimination and target object search in an unknown workspace. Autonomous Robots. 43(1). 123–152. 64 indexed citations

8.

Feng, Di, Mohsen Kaboli, & Gordon Cheng. (2018). Active Prior Tactile Knowledge Transfer for Learning Tactual Properties of New Objects. Sensors. 18(2). 634–634. 22 indexed citations

9.

Kaboli, Mohsen, Di Feng, Kunpeng Yao, Pablo Lanillos, & Gordon Cheng. (2017). A Tactile-Based Framework for Active Object Learning and Discrimination using Multimodal Robotic Skin. IEEE Robotics and Automation Letters. 2(4). 2143–2150. 49 indexed citations

10.

Kaboli, Mohsen, Di Feng, & Gordon Cheng. (2017). Active Tactile Transfer Learning for Object Discrimination in an Unstructured Environment Using Multimodal Robotic Skin. International Journal of Humanoid Robotics. 15(1). 1850001–1850001. 37 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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