Jonas Bärgman

1.4k total citations
51 papers, 960 citations indexed

About

Jonas Bärgman is a scholar working on Safety, Risk, Reliability and Quality, Social Psychology and Automotive Engineering. According to data from OpenAlex, Jonas Bärgman has authored 51 papers receiving a total of 960 indexed citations (citations by other indexed papers that have themselves been cited), including 36 papers in Safety, Risk, Reliability and Quality, 29 papers in Social Psychology and 22 papers in Automotive Engineering. Recurrent topics in Jonas Bärgman's work include Traffic and Road Safety (35 papers), Human-Automation Interaction and Safety (29 papers) and Autonomous Vehicle Technology and Safety (18 papers). Jonas Bärgman is often cited by papers focused on Traffic and Road Safety (35 papers), Human-Automation Interaction and Safety (29 papers) and Autonomous Vehicle Technology and Safety (18 papers). Jonas Bärgman collaborates with scholars based in Sweden, United States and United Kingdom. Jonas Bärgman's co-authors include Marco Dozza, Trent Victor, Gustav Markkula, Nils Lübbe, John D. Lee, Bo Sui, Johan Engström, Azra Habibovic, Julia Werneke and Kip Smith and has published in prestigious journals such as SHILAP Revista de lepidopterología, Technometrics and IEEE Transactions on Intelligent Transportation Systems.

In The Last Decade

Jonas Bärgman

49 papers receiving 923 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Jonas Bärgman Sweden 17 639 539 409 170 127 51 960
Yee Mun Lee United Kingdom 20 640 1.0× 749 1.4× 394 1.0× 137 0.8× 113 0.9× 64 1.2k
Zachary R. Doerzaph United States 11 493 0.8× 429 0.8× 296 0.7× 90 0.5× 120 0.9× 54 896
R R Knipling United States 15 684 1.1× 558 1.0× 328 0.8× 127 0.7× 146 1.1× 62 1.1k
Jeremy Sudweeks United States 8 707 1.1× 588 1.1× 346 0.8× 100 0.6× 183 1.4× 13 1.1k
Vicki L. Neale United States 11 644 1.0× 536 1.0× 351 0.9× 114 0.7× 167 1.3× 30 1.1k
James R. Sayer United States 19 694 1.1× 551 1.0× 391 1.0× 224 1.3× 235 1.9× 79 1.1k
Raymond J. Kiefer United States 15 497 0.8× 571 1.1× 331 0.8× 134 0.8× 64 0.5× 42 922
Giulio Bianchi Piccinini Sweden 12 581 0.9× 358 0.7× 338 0.8× 135 0.8× 280 2.2× 25 838
Dot Hs 15 375 0.6× 226 0.4× 250 0.6× 125 0.7× 105 0.8× 52 710
David Ramsey France 8 441 0.7× 370 0.7× 227 0.6× 60 0.4× 111 0.9× 13 744

Countries citing papers authored by Jonas Bärgman

Since Specialization
Citations

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

Fields of papers citing papers by Jonas Bärgman

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Jonas Bärgman

This figure shows the co-authorship network connecting the top 25 collaborators of Jonas Bärgman. A scholar is included among the top collaborators of Jonas Bärgman 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 Jonas Bärgman. Jonas Bärgman 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.
Bärgman, Jonas, et al.. (2024). Methodological challenges of scenario generation validation: A rear-end crash-causation model for virtual safety assessment. Transportation Research Part F Traffic Psychology and Behaviour. 104. 374–410. 6 indexed citations
2.
3.
Knauss, Eric, et al.. (2023). Human factors in developing automated vehicles: A requirements engineering perspective. Journal of Systems and Software. 205. 111810–111810. 2 indexed citations
4.
Knauss, Eric, et al.. (2023). Managing Human Factors in Automated Vehicle Development: Towards Challenges and Practices. Chalmers Research (Chalmers University of Technology). 347–352. 1 indexed citations
5.
Bärgman, Jonas, et al.. (2023). Analysis of Time-to-Lane-Change-Initiation Using Realistic Driving Data. IEEE Transactions on Intelligent Transportation Systems. 25(5). 4620–4633. 11 indexed citations
6.
7.
Markkula, Gustav, et al.. (2021). Computational modeling of driver pre-crash brake response, with and without off-road glances: Parameterization using real-world crashes and near-crashes. Accident Analysis & Prevention. 163. 106433–106433. 20 indexed citations
8.
Sui, Bo, Nils Lübbe, & Jonas Bärgman. (2019). A clustering approach to developing car-to-two-wheeler test scenarios for the assessment of Automated Emergency Braking in China using in-depth Chinese crash data. Accident Analysis & Prevention. 132. 105242–105242. 46 indexed citations
9.
Lübbe, Nils, et al.. (2018). Predicted road traffic fatalities in Germany: The potential and limitations of vehicle safety technologies from passive safety to highly automated driving. Chalmers Research (Chalmers University of Technology). 14 indexed citations
10.
Carsten, Oliver, et al.. (2017). Driver Distraction and Inattention. elib (German Aerospace Center). 12 indexed citations
11.
Piccinini, Giulio Bianchi, Johan Engström, Jonas Bärgman, & Xuesong Wang. (2017). Factors contributing to commercial vehicle rear-end conflicts in China: A study using on-board event data recorders. Journal of Safety Research. 62. 143–153. 22 indexed citations
12.
Bärgman, Jonas, et al.. (2017). Counterfactual simulations applied to SHRP2 crashes: The effect of driver behavior models on safety benefit estimations of intelligent safety systems. Accident Analysis & Prevention. 102. 165–180. 58 indexed citations
13.
Lee, Ja Young, John D. Lee, Jonas Bärgman, Joonbum Lee, & Bryan Reimer. (2017). How safe is tuning a radio?: using the radio tuning task as a benchmark for distracted driving. Accident Analysis & Prevention. 110. 29–37. 25 indexed citations
14.
Markkula, Gustav, et al.. (2016). A farewell to brake reaction times? Kinematics-dependent brake response in naturalistic rear-end emergencies. Accident Analysis & Prevention. 95(Pt A). 209–226. 125 indexed citations
15.
Victor, Trent, et al.. (2013). Safer Glances, Driver Inattention, and Crash Risk: An Investigation Using the SHRP 2 Naturalistic Driving Study. Chalmers Publication Library (Chalmers University of Technology). 6 indexed citations
16.
Engström, Johan, Julia Werneke, Jonas Bärgman, Noël Nguyen, & Bryan G. Cook. (2013). Analysis of the role of inattention in road crashes based on naturalistic on-board safety monitoring data. Chalmers Publication Library (Chalmers University of Technology). 11 indexed citations
17.
Dozza, Marco, Jonas Bärgman, & John D. Lee. (2012). Chunking: A procedure to improve naturalistic data analysis. Accident Analysis & Prevention. 58. 309–317. 32 indexed citations
18.
Habibovic, Azra, Emma Tivesten, Nobuyuki Uchida, Jonas Bärgman, & Mikael Ljung Aust. (2012). Driver behavior in car-to-pedestrian incidents: An application of the Driving Reliability and Error Analysis Method (DREAM). Accident Analysis & Prevention. 50. 554–565. 57 indexed citations
19.
Bärgman, Jonas, et al.. (2011). On data security and analysis platforms for analysis of naturalistic driving data. Chalmers Publication Library (Chalmers University of Technology). 1 indexed citations
20.
Sullivan, John M., et al.. (2007). Driver Performance and Workload Using a Night Vision System. 11 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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