Jingda Wu

3.9k total citations · 1 hit paper
70 papers, 2.7k citations indexed

About

Jingda Wu is a scholar working on Automotive Engineering, Electrical and Electronic Engineering and Control and Systems Engineering. According to data from OpenAlex, Jingda Wu has authored 70 papers receiving a total of 2.7k indexed citations (citations by other indexed papers that have themselves been cited), including 57 papers in Automotive Engineering, 36 papers in Electrical and Electronic Engineering and 19 papers in Control and Systems Engineering. Recurrent topics in Jingda Wu's work include Electric and Hybrid Vehicle Technologies (30 papers), Electric Vehicles and Infrastructure (30 papers) and Autonomous Vehicle Technology and Safety (21 papers). Jingda Wu is often cited by papers focused on Electric and Hybrid Vehicle Technologies (30 papers), Electric Vehicles and Infrastructure (30 papers) and Autonomous Vehicle Technology and Safety (21 papers). Jingda Wu collaborates with scholars based in China, Singapore and Hong Kong. Jingda Wu's co-authors include Chen Lv, Zhiyu Huang, Hongwen He, Jiankun Peng, Yuecheng Li, Zhongbao Wei, Yunwei Li, Zhanjiang Li, Zhongyi Quan and Zhongxu Hu and has published in prestigious journals such as Nature Communications, Renewable and Sustainable Energy Reviews and IEEE Transactions on Pattern Analysis and Machine Intelligence.

In The Last Decade

Jingda Wu

64 papers receiving 2.6k citations

Hit Papers

Human-Guided Continual Learning for Personalized Decision... 2025 2026 2025 5 10 15
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.

  1. Human-Guided Continual Learning for Personalized Decision-Making of Autonomous Driving
    2025 16 citations Jingda Wu, Chen Lv 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 Career Trend Papers Cites
Jingda Wu China 23 1.9k 1.3k 705 401 312 70 2.7k
Junqiang Xi China 26 1.9k 1.0× 710 0.5× 643 0.9× 211 0.5× 344 1.1× 93 2.4k
Jian Wu China 23 993 0.5× 695 0.5× 552 0.8× 239 0.6× 281 0.9× 135 2.3k
José Eugenio Naranjo Spain 24 1.4k 0.8× 456 0.3× 980 1.4× 247 0.6× 505 1.6× 89 2.2k
Dongsuk Kum South Korea 32 2.2k 1.2× 1.4k 1.1× 711 1.0× 154 0.4× 470 1.5× 93 3.1k
Tatsuya Suzuki Japan 23 829 0.4× 589 0.5× 1.1k 1.6× 239 0.6× 357 1.1× 295 2.4k
Weida Wang China 30 2.5k 1.3× 1.5k 1.1× 855 1.2× 125 0.3× 299 1.0× 192 3.1k
Shaobing Xu China 25 1.4k 0.7× 352 0.3× 924 1.3× 137 0.3× 239 0.8× 57 1.7k
Lutz Eckstein Germany 21 1.6k 0.9× 478 0.4× 725 1.0× 247 0.6× 369 1.2× 216 2.4k
Clara Marina Martínez United Kingdom 10 1.5k 0.8× 1.0k 0.8× 406 0.6× 100 0.2× 137 0.4× 14 1.9k

Countries citing papers authored by Jingda Wu

Since Specialization
Citations

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

Fields of papers citing papers by Jingda Wu

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Jingda Wu

This figure shows the co-authorship network connecting the top 25 collaborators of Jingda Wu. A scholar is included among the top collaborators of Jingda Wu 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 Jingda Wu. Jingda Wu 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
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Fan, Yi, et al.. (2025). Eco-Driving Strategy for Series Hybrid Electric Vehicle Based on Multi-Objective Deep Reinforcement Learning. IEEE Transactions on Transportation Electrification. 11(5). 12381–12392. 1 indexed citations
3.
Huang, Chao, et al.. (2025). Toward Multi-Task Generalization in Autonomous Navigation: A Human-in-the-Loop Adversarial Reinforcement Learning With Diffusion Policy. IEEE Transactions on Intelligent Transportation Systems. 26(11). 19493–19507. 1 indexed citations
4.
Wu, Jingda, et al.. (2025). Trust-Calibrated Human-in-the-Loop Reinforcement Learning for Safe and Efficient Autonomous Navigation. IEEE Internet of Things Journal. 12(18). 39029–39041. 1 indexed citations
5.
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He, Hongwen, et al.. (2025). An adaptive Kalman filter-based estimation method for online oxygen flow measurement in PEMFCs with mismatch detection. Measurement. 254. 118011–118011. 1 indexed citations
8.
Huang, Chao, et al.. (2024). Enhancing data-driven energy management strategy via digital expert guidance for electrified vehicles. Applied Energy. 381. 125138–125138. 2 indexed citations
9.
Mei, Yan, et al.. (2024). Attentive hybrid reinforcement learning-based eco-driving strategy for connected vehicles with hybrid action spaces and surrounding vehicles attention. Energy Conversion and Management. 321. 119059–119059. 5 indexed citations
10.
Tang, Yingjuan, Hongwen He, Yong Wang, & Jingda Wu. (2024). Towards efficient multi-modal 3D object detection: Homogeneous sparse fuse network. Expert Systems with Applications. 256. 124945–124945. 3 indexed citations
11.
Wu, Jingda, et al.. (2024). Recent advances in reinforcement learning-based autonomous driving behavior planning: A survey. Transportation Research Part C Emerging Technologies. 164. 104654–104654. 28 indexed citations
12.
Peng, Jiankun, et al.. (2024). Multi-performance enhanced eco-driving strategy for connected fuel cell hybrid electric bus based on stein soft actor-3-critic. Energy. 307. 132697–132697. 12 indexed citations
13.
Wei, Henglai, et al.. (2024). Intelligent EV Charging Control and Management: From Microscale Battery Cell to Macroscale Grid Synergy. IEEE Transactions on Intelligent Vehicles. 10(3). 1693–1713. 2 indexed citations
14.
Huang, Zhiyu, Haochen Liu, Jingda Wu, Wenhui Huang, & Chen Lv. (2023). Learning Interaction-Aware Motion Prediction Model for Decision-Making in Autonomous Driving. 4820–4826. 18 indexed citations
15.
Peng, Jiankun, et al.. (2023). Multiple Electric Components Health-Aware Eco-Driving Strategy for Fuel Cell Hybrid Electric Vehicle Based on Soft Actor-Critic Algorithm. IEEE Transactions on Transportation Electrification. 10(3). 6242–6257. 18 indexed citations
16.
Huang, Wenhui, Cong Zhang, Jingda Wu, et al.. (2023). Sampling Efficient Deep Reinforcement Learning Through Preference-Guided Stochastic Exploration. IEEE Transactions on Neural Networks and Learning Systems. 35(12). 18553–18564. 10 indexed citations
17.
Liu, Haochen, Zhiyu Huang, Jingda Wu, & Chen Lv. (2022). Improved Deep Reinforcement Learning with Expert Demonstrations for Urban Autonomous Driving. 2022 IEEE Intelligent Vehicles Symposium (IV). 921–928. 56 indexed citations
18.
Wu, Jingda, Zhiyu Huang, Wenhui Huang, & Chen Lv. (2022). Prioritized Experience-Based Reinforcement Learning With Human Guidance for Autonomous Driving. IEEE Transactions on Neural Networks and Learning Systems. 35(1). 855–869. 77 indexed citations
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20.
Huang, Zhiyu, Chen Lv, & Jingda Wu. (2020). Modeling Human Driving Behavior in Highway Scenario using Inverse Reinforcement Learning.. arXiv (Cornell University). 2 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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