Auto-Labeling System with Agentic AI for Perception in Auton-omous Driving

  • Type:Master's thesis
  • Date:Immediately
  • Supervisor:

    Lei Wan

Background:
In autonomous driving, high-qual-ity labeled data is essential for training perception systems, ena-bling them to accurately detect and track objects in complex environ-ments. However, manual annota-tion is a highly labor-intensive, time-consuming, and expensive process, particularly when dealing with large-scale multi-modal datasets such as LiDAR and RGB, as well as collaborative perception datasets involving multiple perspectives. To address these challenges, automated auto-labeling sys-tems have been developed to improve efficiency and reduce dependency on human annotators. Recent breakthroughs in Large Language Models (LLMs) and Vision-Language Models (VLMs), alongside Agentic AI frameworks, offer transformative potential.This thesis aims to design and im-plement an innovative auto-labeling system that harnesses Agentic AI to advance multi-modal per-ception in autonomous driving. The focus will be on improving annotation accuracy, robustness to challenging conditions, and scalability across LiDAR and RGB datasets.

Your Tasks:
  • Analysis of SOTA in Auto-Labeling of Multi-Modal Datset
  • Build a system to annotate multi-modal data (RGB, Thermal, LiDAR) using LLMs and VLMs for contextual understanding and validation.
  • Validation with Real and Simulated Data to assess performance.
  • Benchmark the system’s accuracy and efficiency against existing SOTA methods.

Your Profile:
  • Strong background in machine learning and computer vision.
  • Experience with multi-modal sensor data processing (e.g., camera images, LiDAR point clouds, GPS time-series).
  • Knowledge of deep learning frameworks (e.g., PyTorch or Tensorflow) and practical experi-ence of LLM/VLM
  • Ability to work independently and tackle complex, open-ended research problems.

Start date: Immediately
Duration: As per the applicable examination regulations.

If you are interested or have any questions regarding this thesis position, feel free to contact.