Dr. Gu JunYi Claude

Dr. Gu JunYi Claude

Postdoctoral Researcher, Ph.D.

Biography

I am currently a postdoc at University of Gothenburg. My postdoctoral research focuses on the multi-module traffic event detection with the assistance of different technologies such as sensor fusion, optical flow, time series modelling, etc.

I received my Ph.D. at Tallinn University of Technology on November 2024. My doctoral research field lies in the sensory perception and dataset for autonomous systems. In detail, my research covers two topics:

i) Multi-modal training datasets and generic dataset collection framework.

ii) Deep-learning-based sensor fusion for traffic object segmentation.

Other of my research experience spans SLAM, mobile robotics, and computer vision.

My teaching and supervision experience related to the practical engineering of robotics, where ROS and Autoware are the primary focuses of the course and project.

I received the M.S. degree in Engineering with robotics and Computer Engineering specialization from the University of Tartu in 2020; and the B.E. degree from the University of Shanghai for Science and Technology in 2017.

Scholarships

  • 2018-2019 and 2019-2020 Dora Plus programme scholarships for Master’s studies at the University of Tartu, Estonia.
  • Full tuition waiver scholarship for Master’s degree studies at the University of Tartu, Estonia.
  • Achievement scholarship for 2018-2019 academic year of Master’s studies at the University of Tartu, Estonia.

Recent Publications

(2026). PCICF: a Pedestrian Crossing Identification and Classification Framework. ICSE-SEIP 2026.

PDF Cite Code Dataset Poster DOI

(2025). A Novel Vision Transformer for Camera Lidar Fusion Based Traffic Object Segmentation. ICAART 2025.

PDF Cite DOI

(2024). CLFT: Camera-Lidar Fusion Transformer for Semantic Segmentation in Autonomous Driving. IEEE Transactions on Intelligent Vehicles.

PDF Cite Code Project DOI

(2024). Robot Bus Low Level Control System Transformation to an Open-Source Solution. AIP Conference Proceedings.

PDF Cite DOI

(2023). End to End Multimodal Sensor Dataset Collection Framework for Autonomous Vehicles. Sensors.

PDF Cite Code Dataset DOI

(2022). Object Segmentation for Autonomous Driving Using IseAuto Data. Electronics.

PDF Cite Dataset Project DOI

(2021). Range Sensor Overview and Blind-Zone Reduction of Autonomous Vehicle Shuttles. IOP Conference Series: Materials Science and Engineering.

PDF Cite DOI

Contact