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 and Prof. Lu Gan. I’m broadly interested in Perception and Planning. My current research focuses on CUDA-based motion generation and Multimodal sensor fusion.

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

• Autonomous Driving
• Robot Perception
• Robot Learning

Selected Research

Towards Tighter Convex Relaxation of Mixed-integer Programs: Leveraging Logic Network Flow for Task and Motion Planning

Xuan Lin, Yandong Luo, Jiming Ren, Weijun Xie, and Ye Zhao

The International Journal of Robotics Research (IJRR), under review, Georgia Institute of Technology

[Paper] [Multi-Agent] [Drag Recovery] [Biped Locomotion] [Search & Rescue]

GPU-Accelerated Differential Evolution for Robotic Planning and Control

Yandong Luo, Lu Gan and Ye Zhao

Propose a general CUDA-accelerated optimization framework for real-time planning and control in robotics. By combining Bézier-parameterized control with SHADE-based differential evolution, the method enables efficient search in hybrid, non-convex, and non-differentiable spaces. It achieves significant speed-ups across cart-pole, bipedal locomotion, and autonomous driving tasks, and will be submitted to IEEE RA-L in October 2025.

[GitHub Page] [Part of Draft]

Multi-Sensor Fusion for Open-Vocabulary Gaussian-based 3D Scene Understanding

Lingjun Zhao, Yandong Luo, Lu Gan

Self-supervised 3D scene understanding via multi-modal Gaussian fusion. Supports open-vocabulary segmentation and occupancy prediction using LiDAR, radar, and camera inputs.

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

Interaction-Aware LSTM-Based Trajectory Prediction Framework for Ego Vehicle Path Forecasting

Predict the future trajectory of an ego vehicle by leveraging its past motion and interactions with nearby vehicles using a self-attention LSTM. With grid-based encoding and masked loss, the model produces accurate, socially-aware predictions in dynamic traffic environments.

[Github repo][Github Page]

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