Milestones in Autonomous Driving and Intelligent Vehicles: Survey of Surveys
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doi.org/10.1109/tiv.2022.3223131 →Countries where authors are citing Milestones in Autonomous Driving and Intelligent Vehicles: Survey of Surveys
This map shows the geographic impact of Milestones in Autonomous Driving and Intelligent Vehicles: Survey of Surveys. 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 Milestones in Autonomous Driving and Intelligent Vehicles: Survey of Surveys with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Milestones in Autonomous Driving and Intelligent Vehicles: Survey of Surveys more than expected).
Fields of papers citing Milestones in Autonomous Driving and Intelligent Vehicles: Survey of Surveys
This network shows the impact of Milestones in Autonomous Driving and Intelligent Vehicles: Survey of Surveys. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the Milestones in Autonomous Driving and Intelligent Vehicles: Survey of Surveys.
About Milestones in Autonomous Driving and Intelligent Vehicles: Survey of Surveys
This paper, published in 2022, received 350 indexed citations . Written by Long Chen, Yuchen Li, Chao Huang, Bai Li, Yang Xing, Daxin Tian, Li Li, Zhongxu Hu, Xiaoxiang Na and Zixuan Li covering the research area of Automotive Engineering, Safety Research and Social Psychology. It is primarily cited by scholars working on Automotive Engineering (150 citations), Computer Vision and Pattern Recognition (110 citations), Control and Systems Engineering (91 citations), Artificial Intelligence (71 citations) and Electrical and Electronic Engineering (48 citations). Published in IEEE Transactions on Intelligent Vehicles.
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.
This paper is also available at doi.org/10.1109/tiv.2022.3223131.