Jiechao Liu: h-index, Total Citations, and Citation Map
Jiechao Liu's h-index is 9 (9 i10-index, 735+ total citations across 5+ publications) according to Google Scholar as of May 2026. Jiechao Liu is affiliated with Unknown affiliation.
Jiechao Liu is a researcher affiliated with Unknown affiliation, specializing in Autonomous Driving. Their work has been cited 735 times. This profile visualizes their global influence, highlighting strong citation networks in United States.
Jiechao Liu's Citation Metrics
Bibliometric impact based on 5 indexed publications.
- H-Index
- 9
- i10-Index
- 9
- Total Citations
- 735
- Citing Countries
- 5
As of May 2026.
Jiechao Liu has an h-index of 9 and 735 total citations across 5 publications, with research cited by institutions in 5 countries.
Download Exports (PNG, CSV, Poster)
Free Viewing Jiechao Liu's citation map is always free. Pay once to download poster, PNG, and CSV files for offline use or your visa packet.
Global Impact Map
Visualizing the geographic distribution of institutions that have cited your work.
Starting…
Pins will appear here as institutions are resolved — no need to refresh.
Top Cited Works
Tip: clickto hide a row from the map
Combined Speed and Steering Control in High Speed Autonomous Ground Vehicles for Obstacle Avoidance Using Model Predictive Control
2017188
Top Citing Countries
Top Citing Institutions
Visa Evidence Package
Views and exports tuned for EB-1A, O-1A, and EB-2 NIW petitions. Sustained acclaim, geographic reach, and independent-citation filtering are the strongest evidence categories immigration adjudicators look for.
Significant Contributions
Auto-detected research lines — a seminal paper and the follow-up work building on it. Review and edit before using in a petition. Each Free PDF opens in a new tab — EB-1A organises this into the structure USCIS applies to Criterion 5 of 8 CFR § 204.5(h)(3)(v); EB-1B re-frames it under § 204.5(i)(3) (outstanding researcher); NIW presents it under prong 2 of Matter of Dhanasar.
The researcher developed a multi-stage optimization framework for MPC-based obstacle avoidance in autonomous vehicles, advancing model fidelity and nonlinear control for high-speed, unstructured environments.
The researcher developed a model predictive control framework for simultaneous speed and steering in high-speed autonomous vehicles to enable robust obstacle avoidance.
The researcher developed foundational methods for moving obstacle avoidance in large, high-speed autonomous ground vehicles, establishing a critical safety framework for dynamic environments.
Citation trend (last 10 years)Click to expand
Citation Trend (Last 10 Years)
Related Guides
Learn how to use citation maps for your research and visa applications.
