Bhaskar Anand | Autonomous Vechicles | Innovative Research Award

Posted on by Automobile Award

Innovative Research Award

Bhaskar Anand
Affiliation Kody Technolab Limited
Country India
Scopus ID 57209881634
Documents 20
Citations 202
h-index 9
Subject Area Autonomous Vehicles
Event Global Automobile Award
ORCID 0000-0002-2035-9446

Bhaskar Anand

Kody Technolab Limited, India

Bhaskar Anand is a researcher associated with Kody Technolab Limited whose scholarly profile includes contributions relevant to autonomous vehicles and intelligent transportation technologies. His recorded research portfolio comprises 20 documents, 202 citations, and an h-index of 9, providing measurable indicators of research activity and scholarly visibility in the field of autonomous vehicle research.
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Abstract

Bhaskar Anand is associated with research activities in autonomous vehicles, an interdisciplinary field combining artificial intelligence, sensing, robotics, communications, control systems, and transportation engineering. His scholarly record includes 20 documents, 202 citations, and an h-index of 9, indicating sustained publication activity and measurable academic influence. Autonomous vehicle research addresses perception, localization, planning, decision-making, vehicle control, connectivity, and safety, with the broader objective of improving transportation efficiency and reducing human-error-related risks[2].

Keywords

Autonomous Vehicles, Automated Driving, Intelligent Transportation Systems, Vehicle Automation, Artificial Intelligence, Automotive Research, Connected Vehicles, Vehicle Safety, Smart Mobility, Sensor Fusion, Autonomous Navigation, Machine Learning, Transportation Engineering, Intelligent Mobility, Automotive Innovation.

Introduction

Autonomous vehicles represent a major research direction within modern transportation engineering, integrating perception, computation, communication, and control into increasingly automated driving systems. Research in this area seeks to address complex road environments, uncertain traffic conditions, human interaction, and operational safety while supporting more efficient and connected mobility systems[2].

Research Profile

Bhaskar Anand’s documented scholarly profile contains 20 research documents and 202 citations, with an h-index of 9. These bibliometric indicators provide a quantitative representation of publication activity and citation-based visibility. The profile is associated with autonomous vehicle research, a rapidly developing area within intelligent transportation and automotive engineering[1].

Research Contributions

Research associated with autonomous vehicles commonly addresses the development and assessment of computational methods for perception, localization, trajectory planning, decision-making, and vehicle control. These components must operate together to support reliable automated driving. The field also requires evaluation of system robustness under changing environmental, traffic, and operational conditions[3].

Publications

The available profile records 20 scholarly documents attributed to Bhaskar Anand and reports 202 citations. Publication activity provides evidence of continued engagement with academic research and contributes to the quantitative assessment of scholarly productivity. Two publications particularly relevant to the stated research area are the book LiDAR Technology for Intelligent Transportation and Autonomous Systems and the IEEE Journal of Emerging and Selected Topics in Industrial Electronics article on object georeferencing, speed, and heading detection using LiDAR-INS/GNSS[2][3].

Research Impact

The reported citation count of 202 and h-index of 9 indicate measurable scholarly visibility within the available indexed record. Citation indicators should be interpreted alongside publication quality, authorship contribution, research originality, collaboration, and field-specific citation practices rather than as standalone measures of research excellence[1].

Award Suitability

Bhaskar Anand’s documented research activity in autonomous vehicles provides a relevant basis for consideration for the Innovative Research Award. The reported publication and citation indicators demonstrate an established scholarly record, while the subject area aligns with emerging automotive technologies and intelligent transportation research[1].

Conclusion

Bhaskar Anand is associated with an academic profile focused on autonomous vehicle research, supported by 20 documents, 202 citations, and an h-index of 9. His research area corresponds with important developments in intelligent transportation, automated driving, LiDAR-based perception, and automotive technology. His documented research record provides a scholarly foundation for consideration under the Innovative Research Award.

References

  1. Elsevier. (n.d.).
    Scopus author details: Bhaskar Anand, Author ID 57209881634.
    Scopus.https://www.scopus.com/authid/detail.uri?authorId=57209881634
  2. Pachamuthu, R., Anand, B., Thakur, A., & Alam, P. (2025).
    LiDAR Technology for Intelligent Transportation and Autonomous Systems.
    CRC Press.DOI:https://doi.org/10.1201/9781003628422
  3. Anand, B., & Rajalakshmi, P. (2025).
    System for Object Georeferencing, Speed, and Heading Detection Using LiDAR-INS/GNSS Mounted on a Mobile Vehicle.
    IEEE Journal of Emerging and Selected Topics in Industrial Electronics, 6(4), 1546–1555.DOI:https://doi.org/10.1109/JESTIE.2025.3572802

Seungchul Ryu | Automotive Artificial Intelligence | Research Excellence Award

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Dr. Seungchul Ryu | Automotive Artificial Intelligence | Research Excellence Award

Senior  Researcher | Forvia  | Canada

Dr. Seungchul Ryu is a senior researcher with extensive expertise in automotive-focused computer vision, image processing, and machine learning, delivering impactful innovations that bridge academic research and industrial deployment. His professional experience spans leading roles across academia and global mobility technology companies, contributing to intelligent cockpit systems, advanced driver-assistance solutions, and robust perception under extreme conditions. His research interests center on vision-aware safety systems, human–machine interaction, and applied AI for real-world automotive environments. He demonstrates strong research skills in algorithm development, system integration, patent generation, and large-scale project leadership. His work has earned industry recognition, innovation awards, and editorial and technical committee roles, reflecting sustained scientific influence and leadership. Overall, his contributions continue to shape next-generation intelligent mobility through high-impact research and innovation. He has achieved 344 Citations 33 Documents 7h-index.

 

Citation Metrics (Scopus)

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View Google Scholar Profile
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Featured Publications


No-Reference Quality Assessment for Stereoscopic Images Based on Binocular Quality Perception

– IEEE Transactions on Circuits and Systems for Video Technology, 2013

DASC: Dense Adaptive Self-Correlation Descriptor for Multi-Modal and Multi-Spectral Correspondence

– IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2015


Stereoscopic Image Quality Metric Based on Binocular Perception Model

– IEEE International Conference on Image Processing (ICIP), 2012

Depth Perception and Motion Cue Based 3D Video Quality Assessment

– IEEE International Symposium on Broadband Multimedia Systems, 2012

Zhen Guo | Automotive Robotics | Research Excellence Award

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Dr. Zhen Guo | Automotive Robotics | Research Excellence Award

Doctoral student |  Wuhan University of Technology |  China

Dr. Zhen Guo is an emerging researcher in intelligent fault diagnosis and industrial artificial intelligence, with work spanning advanced data-driven methods for complex mechanical and robotic systems. The academic profile reflects strong training in engineering research with a focus on applying deep learning and statistical learning to real-world reliability challenges. Professional experience includes active service as a peer reviewer for leading international journals and conferences in engineering informatics, artificial intelligence, measurement science, and mechanical systems, demonstrating recognition within the scholarly community. Research interests center on deep learning–based fault diagnosis, rotating machinery health monitoring, robotics, anomaly detection, imbalance learning, few-shot and incremental learning, and transfer learning, particularly for wind turbines, gearboxes, and autonomous systems. Research skills encompass convolutional and attention-based neural networks, adversarial learning, feature extraction, time-series analysis, imbalance data handling, and intelligent condition monitoring frameworks. Contributions are evidenced by publications in high-impact journals such as Renewable Energy, IEEE Transactions on Instrumentation and Measurement, Expert Systems with Applications, Measurement, Ocean Engineering, and Scientific Reports, addressing both theoretical modeling and practical deployment. Awards and honors are reflected through consistent publication in top-tier venues and trusted reviewer roles. Overall, the work demonstrates a coherent trajectory toward robust, scalable, and intelligent diagnostic solutions for next-generation industrial and transportation systems. He has achieved 152 Citations 11 Documents 7h-index.

Citation Metrics (Scopus)

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View ORCID Profile
View Scopus Profile

Featured Publications

Hongbo Wang | Vehicle Dynamics | Best Researcher Award

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Prof. Hongbo Wang | Vehicle Dynamics | Best Researcher Award

Professor  |  Hefei University of Technology  | China

Prof. Hongbo Wang is a distinguished scholar whose work in intelligent vehicle dynamics and control has shaped both academic research and industrial applications, consistently advancing methodologies in vehicle motion modeling, intelligent control algorithms, and automated driving technologies. With extensive experience leading more than forty national, provincial, and municipal projects, his professional contributions span high-impact investigations into off-road vehicle technology, autonomous driving stability, integrated chassis control, and intelligent mobility systems. His research interests focus on vehicle dynamic behavior analysis, intelligent control strategies, computational modeling, and multi-source information fusion for advanced driving systems, supported by strong research skills in algorithm development, real-time system implementation, experimental platform construction, and interdisciplinary engineering integration. His portfolio of scientific achievements includes more than one hundred academic publications, thirty authorized invention patents, six software copyrights, participation in two provincial standard developments, and the publication of two influential monographs, positioning him as a leading contributor to the field’s evolution. His professional experience is complemented by service roles such as External Expert of the Off-Road Vehicle Technology Branch of the Chinese Society of Automotive Engineers, Member of the Vehicle Control and Intelligentization Technical Committee of the Chinese Association of Automation, and Director of the Anhui Society of Automotive Engineers, reflecting broad recognition across national organizations. His awards include the First Prize of the Science and Technology Award of the Chinese Society of Automotive Engineers, the Second Prize of the Machinery Industry Technology Invention Award, and multiple Anhui Provincial Teaching Achievement Awards, underscoring excellence in both research innovation and educational leadership. Overall, his career demonstrates a sustained commitment to advancing intelligent automotive technologies and fostering academic growth in the engineering community. He has achieved 532 Citations,  63 Documents , 12 h-index.

Profile:  Scopus

Featured Publications 

1. Wang, H., et al. (2025). Multi-objective parallel human–machine steering coordination control strategy of intelligent vehicles path tracking based on deep reinforcement learning. Chinese Journal of Mechanical Engineering (English Edition).

2. Wang, H., et al. (2025). Trajectory tracking multi-constraint model predictive control of unmanned vehicles based on sideslip stiffness estimation with XGBoost algorithm. Proceedings of the Institution of Mechanical Engineers, Part D: Journal of Automobile Engineering.

3. Wang, H., et al. (2025). Yaw stability control of tractor vehicle based on nonsingular fast terminal sliding mode. Proceedings of the Institution of Mechanical Engineers, Part D: Journal of Automobile Engineering.

4. Wang, H., et al. (2025). Intelligent vehicle path tracking coordinated optimization based on dual-steering cooperative game with fault-tolerant function. Applied Mathematical Modelling.

5. Wang, H., et al. (2025). Identification of intrusion obstacles for underground locomotives based on the fusion of LiDAR and wireless positioning technology. International Journal of Vehicle Performance.

Professor Hongbo Wang’s research advances intelligent vehicle control by integrating reinforcement learning, predictive modeling, and human–machine cooperation to enhance safety, stability, and autonomy. His work contributes directly to next-generation intelligent transportation systems, improves industrial vehicle technologies, and supports global innovation in automated mobility.

Ding Peng | Autonomous Vehicles | Best Researcher Award

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Assist. Prof. Dr. Ding Peng | Autonomous Vehicles | Best Researcher Award

Associate Professor | Wuxi University of Technology |China

Assist. Prof. Dr. Ding Peng is an accomplished academic and researcher currently serving as an Associate Professor at Wuxi University of Technology, formerly known as Wuxi Institute of Technology, China. He is also a key member of the Jiangsu Province Engineering Research Center for Energy Saving and Safety of New Energy Vehicles, where he plays a vital role in promoting innovation and sustainability in the automotive sector. Dr. Peng obtained his degree in Vehicle Engineering from Chongqing University which laid a strong foundation for his expertise in intelligent vehicle systems and new energy technologies. His professional experience includes working as a Design Engineer at King Long United Automotive Industry (Suzhou) Co., Ltd., where he participated in the design and development of commercial buses, followed by a distinguished academic career at Wuxi University of Technology . Over the years, Dr. Peng has demonstrated exceptional competence in teaching and research, covering courses such as Automobile Structure, Automobile Theory, Principles of Automatic Control, and Intelligent and Connected Vehicle Technologies. His research interests focus on thermal management technologies for new energy vehicles, autonomous vehicle control strategies, and intelligent connected vehicle (V2X) technologies. His research skills encompass modeling, simulation, data fusion, control algorithms, and system optimization, emphasizing practical integration between academia and industry. Dr. Peng has led numerous enterprise and government-funded projects, published several high-impact academic papers, and secured multiple national patents, showcasing his dedication to advancing innovation in smart and sustainable mobility. His awards and honors include recognition for his leadership in research excellence, academic innovation, and contributions to engineering education. Driven by a vision of merging scientific theory with real-world application, he continues to nurture the next generation of engineers while advancing intelligent vehicle technologies. He has achieved 29 citations , authored 7 scientific papers, and holds an h-index of 2 .

Profiles:  Scopus | ORCID

Featured Publications

  1. Ding, P. (2025). A Cooperative Control Strategy for Predicting Passing Capacity and Intelligent Obstacle Avoidance in Autonomous Vehicles Based on Multisensor Fusion. Journal of Energy Storage.

  2. Ding, P. (2024). A distributed multiple-heat source staged heating method in an electric vehicle. International Journal of Vehicle Performance and Energy Systems.

  3. Ding, P. (2024). Distributed multi-heat-source hybrid heating system based on waste heat recovery for electric vehicles. Journal of Thermal Science and Energy Engineering.

  4. Ding, P. (2023). Research on interactive coupled preheating method utilizing engine-motor cooling waste heat in hybrid powertrains. Applied Thermal Engineering