Dr . Rui He | Computer Vision for Robotics and Autonomous Systems | Best Researcher Award

Professor at National Key Laboratory of Automotive Chassis Integration and Bionics, Jilin University, China

Associate Professor He Rui is a prominent academic and researcher at the National Key Laboratory of Automotive Chassis Integration and Bionics, Jilin University. With a specialized focus on advanced braking systems, autonomous driving technologies, and driver behavior analysis, he stands at the forefront of intelligent vehicle systems research. His career is marked by a strong integration of theoretical innovation and practical application, demonstrated through leadership in national and industrial research projects and the development of multiple patented technologies. Dr. He has published over 30 scholarly articles and holds more than 20 invention patents, showcasing a high level of scientific productivity and innovation. His guidance as a doctoral supervisor also reflects his deep commitment to nurturing future researchers in the field. Acknowledged for his contributions to visual perception, trajectory planning, and chassis-by-wire control, Dr. He Rui continues to drive transformative advancements in the evolving landscape of automotive engineering and intelligent mobility.

Professional Profile

Education🎓

He Rui holds a robust academic background rooted in mechanical and automotive engineering, having pursued his higher education at esteemed institutions in China. He completed his undergraduate studies in vehicle engineering, laying a strong foundation in dynamics, control, and systems integration. He later obtained his Master’s and Doctoral degrees in automotive engineering, with a research focus on intelligent vehicle systems, including sensor-based perception and integrated chassis control. His doctoral work, in particular, explored advanced concepts in vehicle dynamics and control algorithms tailored to autonomous systems. Throughout his academic journey, Dr. He acquired a deep understanding of interdisciplinary technologies involving mechanical systems, computer vision, and artificial intelligence. His education reflects a well-rounded and comprehensive training that blends traditional automotive knowledge with emerging technologies, effectively preparing him to lead innovative research in smart mobility. His continuous pursuit of knowledge and research excellence positions him as a key figure in the automotive academic community.

Professional Experience📝

Dr. He Rui currently serves as an Associate Professor and doctoral supervisor at the National Key Laboratory of Automotive Chassis Integration and Bionics, Jilin University. He has actively led and contributed to various research projects funded by the National Natural Science Foundation of China and major automotive companies such as Dongfeng Motor and SAIC Motor. His portfolio includes pivotal roles in projects related to chassis control, autonomous intelligent driving systems, and integrated modeling methods for electric vehicles. These engagements have enabled him to bridge academic research with industrial implementation. His career demonstrates a commitment to pushing the boundaries of automotive control technologies, especially in intelligent perception and driver-vehicle interaction. In addition to research, Dr. He plays a significant role in mentoring postgraduate students, contributing to curriculum development, and fostering interdisciplinary collaborations. His professional path reflects a balance of theoretical advancement and practical application in the field of intelligent automotive systems.

Research Interest🔎

Dr. He Rui’s research interests lie at the intersection of automotive engineering and intelligent systems. He is primarily focused on the development of advanced chassis-by-wire systems, visual perception for autonomous driving, and analysis of driver behavior for improved human-vehicle interaction. His work explores how artificial intelligence, computer vision, and dynamic control strategies can be integrated into vehicle systems to enhance safety, efficiency, and driving experience. He is particularly interested in intelligent trajectory planning and how vehicles can autonomously adapt to real-world driving conditions using data-driven models. Another major research thrust involves understanding and modeling driver behavior under extreme conditions, such as tire blowouts or sudden braking, to improve control algorithms. These diverse interests underscore his commitment to solving critical challenges in the transition toward intelligent and autonomous mobility. Dr. He’s multidisciplinary approach has led to impactful research that supports both theoretical exploration and real-world implementation.

Award and Honor🏆

While specific awards and honors have not been listed in the profile, Dr. He Rui’s achievements speak to a high level of professional recognition. His leadership in multiple nationally funded research projects and industry collaborations with top automotive manufacturers such as Dongfeng and SAIC reflect his esteemed status in the field. He has authored more than 30 peer-reviewed papers and holds over 20 invention patents, demonstrating consistent innovation and contribution to automotive technology. His position as a doctoral supervisor and associate professor at a prestigious institution like Jilin University further reinforces his academic credibility. It’s highly likely that he has received institutional accolades, commendations from industry partners, and recognition for his research outputs. These accomplishments collectively underscore a career marked by excellence, leadership, and a strong impact on the advancement of intelligent vehicle systems. Further formal honors would only enhance an already distinguished academic and research profile.

Research Skill🔬

Dr. He Rui possesses an impressive set of research skills that span across automotive engineering, intelligent control systems, and artificial intelligence. His expertise in chassis-by-wire technologies allows him to design and develop next-generation braking and steering systems with high reliability and precision. He has strong capabilities in computer vision and sensor fusion, which are essential for enabling autonomous vehicle perception. Dr. He is also proficient in developing and applying advanced control algorithms for vehicle trajectory planning, especially under uncertain or complex driving conditions. He excels in integrating experimental testing with simulation environments, supporting both theoretical research and applied development. His skills include modeling driver behavior using machine learning techniques and incorporating it into vehicle control strategies. Furthermore, he has proven experience in leading large-scale research projects, writing scientific publications, and filing patents. These comprehensive research abilities make him a valuable contributor to the evolution of intelligent transportation technologies.

Conclusion💡

He Rui is a highly suitable candidate for the Best Researcher Award, particularly in fields such as automotive innovation, autonomous systems, and intelligent control technologies. His project leadership, prolific output, and patent record strongly support his candidacy. With further emphasis on international exposure and societal narratives, his profile would be even more competitive at global award levels.

Publications Top Noted✍

Title: Research on vehicle trajectory prediction methods in dense and heterogeneous urban traffic
Authors: Sumin Zhang, Ri Bai, Rui He, Zhiwei Meng, Yupeng Chang, Yongshuai Zhi
Year: 2025
Citation: Transportation Letters, DOI: 10.1080/19427867.2024.2403818
Title: Research on Vehicle Trajectory Prediction Methods in Urban Main Road Scenarios
Authors: Sumin Zhang, Ri Bai, Rui He, Zhiwei Meng, Yupeng Chang, Yongshuai Zhi, Ning Sun
Year: 2024
Citation: IEEE Transactions on Intelligent Transportation Systems, DOI: 10.1109/tits.2024.3419037
Title: A skip feature enhanced multi-source fusion framework for switch state detection
Authors: Sumin Zhang, Ri Bai, Rui He, Zhiwei Meng, Yongshuai Zhi
Year: 2024
Citation: International Journal of Rail Transportation, DOI: 10.1080/23248378.2024.2372729
Title: Decision-making of active collision avoidance system based on comprehensive evaluation method of dangerous scenarios
Authors: Rui He, Zhiwei Meng, Sumin Zhang, Zhi Yang, Yongshuai Zhi, Jiaxiang Qin
Year: 2024
Citation: Journal of Automobile Engineering, DOI: 10.1177/09544070221137398
Title: IDPNet: a light-weight network and its variants for human pose estimation
Authors: Huan Liu, Jian Wu, Rui He
Year: 2024
Citation: The Journal of Supercomputing, DOI: 10.1007/s11227-023-05691-5
Title: Skeleton-based multi-stream adaptive-attentional sub-graph convolution network for action recognition
Authors: Huan Liu, Jian Wu, Haokai Ma, Yuqi Yan, Rui He
Year: 2024
Citation: Multimedia Tools and Applications, DOI: 10.1007/s11042-023-15778-z
Title: LEES-Net: Fast, lightweight unsupervised curve estimation network for low-light image enhancement and exposure suppression
Authors: Xuanhe Li, Rui He, Jian Wu, Hu Yan, Xianfeng Chen
Year: 2023
Citation: Displays, DOI: 10.1016/j.displa.2023.102550
Title: GIVA: Interaction-aware trajectory prediction based on GRU-Improved VGG-Attention Mechanism model for autonomous vehicles
Authors: Zhiwei Meng, Rui He, Jiaming Wu, Sumin Zhang, Ri Bai, Yongshuai Zhi
Year: 2023
Citation: Journal of Automobile Engineering, DOI: 10.1177/09544070231207669
Title: Center point to pose: Multiple views 3D human pose estimation for multi-person
Authors: Huan Liu, Jian Wu, Rui He
Year: 2022
Citation: PLOS ONE, DOI: 10.1371/journal.pone.0274450
Title: Monocular Vision SLAM Research for Parking Environment with Low Light
Authors: Sumin Zhang, Yongshuai Zhi, Shouyi Lu, Ze Lin, Rui He
Year: 2022
Citation: International Journal of Automotive Technology, DOI: 10.1007/s12239-022-0063-5
Title: Speed and Accuracy Tradeoff for LiDAR Data Based Road Boundary Detection
Authors: Guojun Wang, Jian Wu, Rui He, Bin Tian
Year: 2021
Citation: IEEE/CAA Journal of Automatica Sinica, DOI: 10.1109/jas.2020.1003414

Introduction of Computer Vision for Robotics and Autonomous

Introduction: Computer Vision for Robotics and Autonomous Systems is a multidisciplinary field at the intersection of computer vision, robotics, and artificial intelligence. It focuses on equipping robots and autonomous systems with the ability to perceive and understand their environment through visual information. This research area plays a pivotal role in enabling robots to navigate, interact with objects, and make informed decisions in real-world settings, making it a critical component of the burgeoning field of robotics and autonomy.

Subtopics in Computer Vision for Robotics and Autonomous Systems:

Visual SLAM (Simultaneous Localization and Mapping): This subfield is concerned with developing algorithms that allow robots to simultaneously build maps of their surroundings while localizing themselves within these maps using visual data. It's crucial for autonomous navigation.
Object Detection and Tracking for Robotics: Research in this area focuses on enabling robots to detect and track objects in their environment, facilitating tasks like pick-and-place operations, object manipulation, and collision avoidance.
3D Perception and Reconstruction: Techniques for extracting three-dimensional information from 2D images, enabling robots to create accurate 3D models of their surroundings. This is vital for tasks like object manipulation and navigation in complex environments.
Visual Servoing: Visual servo control involves using visual feedback to control the motion and orientation of robots, allowing them to perform tasks with precision, such as grasping objects and following paths.
Human-Robot Interaction and Gesture Recognition: Research in this subtopic explores methods for robots to understand and respond to human gestures and visual cues, making them more capable of interacting with humans in various contexts, from healthcare to service robotics.
Scene Understanding and Semantic Segmentation: Algorithms that provide robots with a higher-level understanding of the scenes they perceive, including recognizing objects, understanding their relationships, and inferring semantic information about the environment.
Visual Perception in Unstructured Environments: Research in this area focuses on equipping robots with the ability to operate in unstructured and dynamic environments, such as outdoor spaces or disaster response scenarios, where traditional navigation methods may not suffice.
Deep Learning for Visual Perception: Leveraging deep neural networks for tasks like object recognition, scene understanding, and decision-making, to improve the perception capabilities of robots.
Multi-Sensor Fusion: Integrating visual information with data from other sensors, such as LiDAR, radar, or IMUs, to create a more comprehensive and robust perception system for robotics.
Autonomous Drone Navigation: Specific to aerial robotics, this subfield focuses on enabling drones to autonomously navigate and interact with their environment using computer vision techniques, opening up applications in surveillance, agriculture, and delivery services.

Computer Vision for Robotics and Autonomous Systems research is pivotal in advancing the capabilities of autonomous robots and systems, with potential applications in industries ranging from manufacturing and agriculture to healthcare and transportation. These subtopics represent the diverse challenges and opportunities within this exciting field of study.

Object Detection and Recognition

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Image Processing and Enhancement

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Computer Vision for Robotics and Autonomous Systems

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3D Computer Vision

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Medical Image Analysis

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Video Analysis

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Deep Learning for Computer Vision

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Applications of Computer Vision

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Human-Computer Interaction

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Biometrics and Security

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Rui He | Computer Vision for Robotics and Autonomous Systems | Best Researcher Award