Mr. Congcong Ren | AI Award | Best Researcher Award

Mr. Congcong Ren, Henan University of Science and Technology, China

Mr. Congcong Ren is a dedicated Master’s student in Vehicle and Traffic Engineering at Henan University of Science and Technology, with a Bachelor’s degree in Mechanical and Electrical Engineering from Henan Agricultural University. His expertise spans deep learning, algorithm development, and software testing, with practical experience in developing intelligent vehicles and defect detection systems. Mr. Ren has contributed to projects like an intelligent small car and wire rope defect detection, and he has gained hands-on experience during internships at Iflytek and Zeekr. His technical proficiency includes Python, PyTorch, and HIL test software, complemented by multiple school-level awards for innovation and entrepreneurship.

Professional Profile:

Orcid

Suitability for the Award

Mr. Congcong Ren is a highly suitable candidate for the Best Researcher Award based on the following points:

1. Innovative Research:
   • His work on nighttime pedestrian detection and trajectory tracking addresses critical safety concerns in autonomous and intelligent vehicle systems. The use of fusion techniques combining visual and radar data showcases innovation in enhancing vehicle safety.
2. Practical Experience:
   • His participation in significant projects like the intelligent small car and wire rope defect detection demonstrates his ability to apply theoretical knowledge to real-world challenges. These projects not only reflect technical skill but also his capability to collaborate effectively with industry partners.
3. Academic and Professional Growth:
   • Mr. Ren’s ongoing master’s studies in artificial intelligence and traffic engineering, combined with his hands-on experience in internships at leading companies like Iflytek and Zeekr, underline his rapid professional development and adaptability in a fast-evolving field.
4. Recognition and Skills:
   • His recognition through scholarships, awards, and publication of SCI papers highlights his academic excellence and contribution to the field. His proficiency in deep learning frameworks, coupled with practical software testing skills, positions him as a strong contender for research excellence.

Summary of Qualifications

1. Educational Background:

   • Bachelor’s Degree in Mechanical and Electrical Engineering – Henan Agricultural University (2018-2022).
     • Major courses included Mechanical Design, Automobile Design, New Energy, and Traffic Engineering.
   • Master’s Degree (ongoing) in Vehicle and Traffic Engineering – Henan University of Science and Technology (2022-2025).
     • Major courses include Principles and Methods of Artificial Intelligence, Traffic Simulation Technology, System Control Theory, and Intelligent Network Technology.

2. Project Experience:

   • Challenge Cup Project (2022-2023): Developed an intelligent small car with adjustable wheelbase and chassis height, integrating camera and millimeter-wave radar data for obstacle detection and avoidance.
   • Wire Rope Defect Detection Project (2023): Collaborated with Luoyang Wilrop Testing Technology Co., LTD. to improve YOLOv5s algorithm for defect detection in wire ropes using industrial camera images, meeting the project’s expected requirements.

3. Internship Experience:

   • Iflytek (2023-2024): Tested large model voice assistant software, proficient in Android Studio and Adobe Audition, and used Python for batch pressure testing.
   • Zeekr (2024): Proficient in HIL test software (ECU-TEST, Canoe, INCA), familiar with software development processes and protocols (LIN/CAN), and involved in new energy vehicle controller testing.

4. Technical Skills:

   • Proficient in Python, PyTorch, Matlab, Simulink, and various HIL test software.
   • Strong capabilities in deep learning, algorithm development, and software testing.
   • Recognized with school-level scholarships and awards, including the innovation and entrepreneurship competition fund.

Publication Top Notes:

1.  Study on Nighttime Pedestrian Trajectory-Tracking from the Perspective of Driving Blind Spots –  (2024).

2.  Nighttime Pedestrian Detection Based on a Fusion of Visual Information and Millimeter-Wave Radar –  (2023).

Both articles reflect his focus on advanced technologies in vehicle safety, particularly in challenging environments like nighttime driving.

Conclusion

Mr. Congcong Ren is an outstanding candidate for the Best Researcher Award, given his solid educational foundation, innovative research contributions in vehicle safety, and substantial practical experience in engineering and software testing. His ability to combine academic research with practical applications, particularly in the field of intelligent vehicle systems, makes him a deserving recipient of this award.