About

Graduated from the Electrical and Computer Engineering AI program at National Yang Ming Chiao Tung University in 2022.

Has experience developing automotive vision ADAS systems and deploying them to embedded operating systems at MobilDrive. The skills used in this experience include C/C++, cross-compiling, edge AI device deployment, and software system design.

In graduate school, research focused on robotics, self-driving cars, mathematical algorithms, and deep learning. The master's thesis was accepted for publication at the ICRA (IEEE International Conference on Robotics and Automation).

Skills

programming languages

C/C++

90%

Python

80%

Shell

50%

Others

50%
Develope environment and tools

Linux

CMake

TDA4-J784S4
(embeddeding system)

OpenVX

Git

Robot Operating System (ROS)

Pytorch

WORK EXPERIMENCE



2023.06 - 2024.05

MobilDrive

System & Algorithm engineer

1. System designed & deployment on embedded system
2. Algorithms designed & deployment on embedded system


Keywords: C++, cross compile, CMake, deep learning model deployment, multithreading, OpenVX, TDA4-J784S4, git, bash

Projects

Publications

GNN-based Point Cloud Maps Feature Extraction and Residual Feature Fusion for 3D Object Detection
LiDAR detection of long-range vehicles is challenging because very few and sparse points are measured in long distances and vehicles with similar shapes of targets could lead to false positives easily. To tackle these challenges, we take the environment information (HD maps) into account and construct a GNN-based feature extraction of point cloud maps to increase the receptive fields of learning map features. Residual feature fusion is proposed to fuse the features from PVRCNN and the map features from GNN. Our approach is evaluated on NuScenes dataset. It achieves a 24.78% average precision improvement for long-range objects at 40-50 meters, the farthest areas with ground truth annotation. Our approach also has a 4.22% reduction of false positives in the entire sensing areas.



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Reconstruction and Synthesis of Lidar Point Clouds of Spray
Lidars are commonly used on autonomous vehicles, but their performance can be significantly affected by adverse weather. Therefore, we produce a lot of spray data and combine them into lidar object detection model to erase the effect of spray. In this paper, we propose the first data-driven method combined with simulation to reconstruct and synthesize spray data. We compare the performance of vehicle detection models trained with and without augmented data. The model trained with augmented data achieves significant performance improvement given real-world spray-affected point cloud data.



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Contact

zxc741852741@gmail.com

www.linkedin.com/in/wei-hsiang-liao-a20362243

+886 975204740