About me

Hi! This is Yandong Luo, a master student in Computer Science at Georgia Institute of Technology, where I’m advised by Prof. Zhao Ye. I’m broadly interested in Perception and Planning. My current research focuses on CUDA-based motion generation for Loco-manipulation.

Before coming to GaTech, I was working at Tusimple company for L2 and L4 trajectory planning and decision making of autonomous trucks operating in the bustling port of Shanghai, China. I obtained my master’s degree at the University of Illinois at Urbana Champaign, where I conducted research on perception and planning for autonomous vehicles under the supervision of Prof. Bob Norris. I completed my bachelor’s degree in Mechanical Engieering at Dongguan University of Technology, where I was advised by Prof. Jianwen Guo. During my undergraduate period, I worked on multi-agent systems.

Working Experience

Tusimple Algorithm Engineer – Path Planning & Decision Making

L2 Lateral Trajectory Optimization

L4 Game Theory for Lane Change

[cut in] [lane change]

Yelink Autonomous Vehicle Engineer

Shenzhen Subway Autonomous Braking System

[subway]

Research Interest

• Robot Learning
• Autonomous Driving
• Robot Perception

Selected Research

Fault diagnosis of industrial robot reducer by an extreme learning machine with a level-based learning swarm optimizer

Jianwen Guo, Xiaoyan Li, Zhenpeng Lao, Yandong Luo, Jiapeng Wu, and Shaohui Zhang

Journal of Advances in Mechanical Engineering, 19 May 2021, Dongguan University of Technology

paper

Swarm Robot Exploration Strategy for Path Formation Tasks Inspired by Physarum polycephalum

Yandong Luo, Jianwen Guo, Zhenpeng Lao, Shaohui Zhang, Xiaohui Yan

Journal of Complexity, 19 May 2021, Dongguan University of Technology

paper

Selected Project

CUDA Parallel Acceleration of Footstep Planner and Controller

Implemented a CUDA-parallel Differential Evolution algorithm to accelerate footstep planning, reducing solve time from 70s (Gurobi) to 0.66s by reparameterizing Bezier-based CoM trajectories and optimizing control inputs.

Nash Equilibrium-Based Planning for Hierarchical Lane Changing

By modeling lane-changing as a hierarchical game between trajectory and behavior layers, and considering both safety and comfort in the utility function, the system solves for the Nash equilibrium to find the optimal lane-changing strategy.

3D Object Detection and Tracking for Autonomous Vehicle

Developed a 3D object detection and tracking pipeline for autonomous vehicles. Integrated 3D IoU-based association, Hungarian matching, and Kalman filtering for robust multi-object tracking.

[Github repo] [Vehicle Test][Tracking Test][KITTI Test]

Path Planning of Reverse Parallel Parking

Implemented a reverse parallel parking planner by projecting 3D perception data into a grid map, generating paths using hybrid A* with steering constraints and Reeds-Shepp heuristics. Applied curvature- and obstacle-aware smoothing for feasible, vehicle-compliant trajectories.

[Github repo] [Night Test] [Vehicle Test][SLAM Test][Simulation Test]

Skill

• Programming Languages: C/C++, CUDA C++, Python, MATLAB, Java
• Frameworks and Tools: PyTorch, ROS, Pybullet, GTSAM, Linux, Docker, Git
• Languages: English, Mandarin, Cantonese