Fabio Tango

1.1k total citations
42 papers, 668 citations indexed

About

Fabio Tango is a scholar working on Automotive Engineering, Social Psychology and Artificial Intelligence. According to data from OpenAlex, Fabio Tango has authored 42 papers receiving a total of 668 indexed citations (citations by other indexed papers that have themselves been cited), including 27 papers in Automotive Engineering, 25 papers in Social Psychology and 8 papers in Artificial Intelligence. Recurrent topics in Fabio Tango's work include Autonomous Vehicle Technology and Safety (26 papers), Human-Automation Interaction and Safety (25 papers) and Traffic control and management (6 papers). Fabio Tango is often cited by papers focused on Autonomous Vehicle Technology and Safety (26 papers), Human-Automation Interaction and Safety (25 papers) and Traffic control and management (6 papers). Fabio Tango collaborates with scholars based in Italy, Germany and Greece. Fabio Tango's co-authors include Andrea Saroldi, Mauro Da Lio, Enrico Bertolazzi, Francesco Biral, A. Polychronopoulos, Angelos Amditis, Luisa Andreone, Manolis Tsogas, Roberto Montanari and Natasha Merat and has published in prestigious journals such as IEEE Access, Sensors and IEEE Transactions on Intelligent Transportation Systems.

In The Last Decade

Fabio Tango

39 papers receiving 642 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Fabio Tango Italy 11 387 229 175 174 112 42 668
Lisheng Jin China 16 421 1.1× 185 0.8× 208 1.2× 167 1.0× 217 1.9× 79 898
Qinyu Sun China 15 308 0.8× 133 0.6× 196 1.1× 191 1.1× 87 0.8× 30 561
Khaled Saleh Australia 14 296 0.8× 123 0.5× 81 0.5× 150 0.9× 204 1.8× 50 707
Tomohiro Yamamura Japan 9 322 0.8× 230 1.0× 193 1.1× 156 0.9× 57 0.5× 22 558
Luisa Andreone Italy 13 302 0.8× 154 0.7× 94 0.5× 109 0.6× 114 1.0× 33 529
Bo Cheng China 14 299 0.8× 152 0.7× 199 1.1× 214 1.2× 96 0.9× 31 662
Iván García Daza Spain 12 279 0.7× 115 0.5× 106 0.6× 122 0.7× 259 2.3× 23 662
Toshihiro Wakita Japan 11 443 1.1× 100 0.4× 134 0.8× 119 0.7× 132 1.2× 23 638
Nobuyuki Kuge Japan 9 319 0.8× 224 1.0× 186 1.1× 167 1.0× 56 0.5× 16 537
Jochen Pohl Sweden 10 251 0.6× 121 0.5× 137 0.8× 97 0.6× 88 0.8× 23 468

Countries citing papers authored by Fabio Tango

Since Specialization
Citations

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

Fields of papers citing papers by Fabio Tango

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Fabio Tango

This figure shows the co-authorship network connecting the top 25 collaborators of Fabio Tango. A scholar is included among the top collaborators of Fabio Tango 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 Fabio Tango. Fabio Tango 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.
Berta, Riccardo, et al.. (2025). A Deployment-Oriented Simulation Framework for Deep Learning-Based Lane Change Prediction. IEEE Signal Processing Letters. 33. 136–140.
2.
Bellotti, Francesco, et al.. (2025). Deep Learning-Based Real-Time Driver Cognitive Distraction Detection. IEEE Access. 13. 26589–26607. 3 indexed citations
3.
Camara, Fanta, Nicola Bellotto, Serhan Coşar, et al.. (2020). Pedestrian Models for Autonomous Driving Part II: High-Level Models of Human Behavior. IEEE Transactions on Intelligent Transportation Systems. 22(9). 5453–5472. 94 indexed citations
4.
Wolter, Stefan C., et al.. (2020). Human–Vehicle Integration in the Code of Practice for Automated Driving. Information. 11(6). 284–284. 6 indexed citations
5.
Amparore, Elvio Gilberto, Marco Beccuti, Marco Botta, Susanna Donatelli, & Fabio Tango. (2018). Adaptive artificial co-pilot as enabler for autonomous vehicles and intelligent transportation systems. International Joint Conference on Artificial Intelligence. 2129. 70–77. 2 indexed citations
6.
Wilbrink, Marc, Anna Schieben, Florian Weber, et al.. (2017). D1.1 Definition of interACT use cases and scenarios. elib (German Aerospace Center). 2 indexed citations
7.
Zlocki, Adrian, et al.. (2017). A Comprehensive Evaluation Approach for Highly Automated Driving. RWTH Publications (RWTH Aachen). 1–13. 23 indexed citations
8.
Tango, Fabio, et al.. (2017). Workshop on Human Machine Interaction in Autonomous Vehicles. Publikationsdatenbank der Fraunhofer-Gesellschaft (Fraunhofer-Gesellschaft). 33–38. 2 indexed citations
9.
Vokřínek, Jiří, et al.. (2013). A cooperative driver model for traffic simulations. 756–761. 4 indexed citations
10.
Saroldi, Andrea, et al.. (2012). Implementation of a Co-Driver for Continuous Support. Institutional Research Information System (Università degli Studi di Trento). 3 indexed citations
11.
Bertolazzi, Enrico, Francesco Biral, Mauro Da Lio, Andrea Saroldi, & Fabio Tango. (2010). Supporting Drivers in Keeping Safe Speed and Safe Distance: The SASPENCE Subproject Within the European Framework Programme 6 Integrating Project PReVENT. IEEE Transactions on Intelligent Transportation Systems. 11(3). 525–538. 73 indexed citations
12.
Tango, Fabio, et al.. (2009). Field tests and machine learning approaches for refining algorithms and correlations of driver’s model parameters. Applied Ergonomics. 41(2). 211–224. 17 indexed citations
13.
Tango, Fabio, et al.. (2008). Advanced multiple objects tracking by fusing radar and image sensor data - Application on a case study.. International Conference on Information Fusion. 1–7. 3 indexed citations
14.
Tango, Fabio, et al.. (2006). ProFusion2 - Sensor Data Fusion for Multiple Active Safety Applications. 2 indexed citations
15.
Tango, Fabio & Roberto Montanari. (2006). Shaping the drivers’ interaction: how the new vehicle systems match the technological requirements and the human needs. Cognition Technology & Work. 8(3). 215–226. 10 indexed citations
16.
Polychronopoulos, A., et al.. (2004). Centralized data fusion for obstacle and road borders tracking in a collision warning system. DSpace - NTUA (National Technical University of Athens). 33 indexed citations
17.
Tango, Fabio, et al.. (2003). Functional Specification and Architecture for the Traffic Risk Estimation Module. IFAC Proceedings Volumes. 36(12). 137–142. 1 indexed citations
18.
Tango, Fabio, et al.. (2002). EVALUATION OF THE LATERAL SUPPORT SYSTEM: A PILOT STUDY WITHIN THE SCOPE OF THE ADVISORS PROJECT. 1 indexed citations
19.
Saroldi, Andrea, Fabio Tango, Evangelos Bekiaris, et al.. (2000). IN-ARTE SYSTEM TO SUPPORT THE DRIVER IN EXTRA-URBAN ENVIRONMENT. Publikationsdatenbank der Fraunhofer-Gesellschaft (Fraunhofer-Gesellschaft). 1 indexed citations
20.
Tango, Fabio, et al.. (2000). THE DEVELOPMENT OF A SMART PRE-CRASH SYSTEM: THE CHAMELEON PROJECT. 1 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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