Junyi GuTallinn University of Technology | TTU · Department of Mechanical and Industrial Engineering
Junyi Gu
Master of Science
Researcher (PhD candidate) at Tallinn University of Technology,
About
9
Publications
691
Reads
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19
Citations
Introduction
I am a researcher and Ph.D. candidate at Tallinn University of Technology. My Ph.D. topic focuses on advanced sensor perception and fusion for autonomous driving. My research interests and experiences include Artificial Intelligence (for sensor-fusion-oriented traffic objects detection and segmentation), Robotics Engineering (ROS and Autoware), SALM (vision and LiDAR based), and System Administration.
Additional affiliations
March 2021 - June 2021
Position
- Research Intern
Description
- Working on intensity-based high speed camera integration to SLAM algorithm.
August 2020 - October 2023
Position
- Researcher (PhD candidate)
Description
- Working at the Autonomous Vehicles Lab as the researcher. Focused on the autonomous driving, Artificial Intellgence, robotics, and radar imaging technologies.
October 2023 - April 2024
Position
- Visiting Researcher
Description
- Working for the Teach-and-Repeat Visual Placement Recognition system, to add on the collision avoidance functions that rely on LiDAR sensor.
Education
September 2020 - August 2024
September 2018 - June 2020
September 2014 - June 2017
Publications
Publications (9)
Critical research about camera-and-LiDAR-based semantic object segmentation for autonomous driving significantly benefited from the recent development of deep learning. Specifically, the vision transformer is the novel ground-breaker that successfully brought the multi-head-attention mechanism to computer vision applications. Therefore, we propose...
Autonomous driving vehicles rely on sensors for the robust perception of their surroundings. Such vehicles are equipped with multiple perceptive sensors with a high level of redundancy to ensure safety and reliability in any driving condition. However, multi-sensor, such as camera, LiDAR, and radar systems raise requirements related to sensor calib...
Autonomous driving vehicles rely on sensors for the robust perception of surroundings. Such vehicles are equipped with multiple perceptive sensors with a high level of redundancy to ensure safety and reliability in any driving condition. However, multi-sensor, such as camera, LiDAR and radar, systems bring up the requirements related to sensor cali...
Object segmentation is still considered a challenging problem in autonomous driving, particularly in consideration of real-world conditions. Following this line of research, this paper approaches the problem of object segmentation using LiDAR–camera fusion and semi-supervised learning implemented in a fully convolutional neural network. Our method...
The presentation slides of the IOP Conf. Series: Materials Science and Engineering doi:10.1088/1757-899X/1140/1/012006
In recent years, with the advancement in sensor technologies, computing technologies and artificial intelligence, the long-sought autonomous vehicles (AVs) have become a reality. Many AVs today are already driving on the roads. Still, we have not reached full autonomy. Sensors which allow AVs to perceive the surroundings are keys to the success of...
Paper for conference Modern Materials and Manufacturing (MMM 2021), 27th-29th April 2021, Tallinn, Estonia
