Multisensor Annotation Pipeline for Accurate Autonomous Driving Ground Truth
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Solution Overview
Problem
Conventional techniques for generating ground truth data for deep neural networks in autonomous driving face challenges in accurately labeling sparse data from LiDAR and RADAR sensors, leading to errors and inefficiencies in training perception systems due to difficulties in distinguishing objects in top-down views and misalignment of sensor modalities.
Innovation Solution
An annotation pipeline that synchronizes and aligns data from multiple sensors, decomposes annotation tasks into modular steps, and provides contextual information across sensor modalities to enhance label accuracy and efficiency, enabling the production of high-quality 2D and 3D ground truth data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional separate labeling processes are used for LiDAR and camera data, then annotation throughput is maintained, but label accuracy deteriorates due to sparse data and inability to distinguish objects in top-down views
Solution Approach 1:
The patent combines LiDAR and camera annotation processes into a unified workflow where annotators simultaneously view both sensor modalities. This integration allows annotators to leverage camera imagery for accurate object identification while annotating LiDAR data, resolving the contradiction by maintaining high label accuracy without sacrificing annotation throughput through cross-modal contextual support.
Solution Approach 2:
The patent introduces camera images as an intermediary that mediates the annotation of LiDAR data. By presenting camera views alongside LiDAR point clouds, the system provides contextual information that helps annotators accurately identify and label objects in sparse LiDAR data, thereby improving label accuracy while maintaining efficient annotation workflow.
2Reliability
If multiple sensor modalities are processed separately, then computational resources are conserved, but ground truth data quality deteriorates due to misalignment and lack of contextual information
Solution Approach 1:
The patent performs preliminary synchronization and alignment of LiDAR and camera data before the annotation process. By pre-aligning temporal and spatial coordinates of multiple sensor modalities, the system ensures that annotators receive coherent, contextually linked data, improving ground truth quality without requiring intensive computational resources during the actual annotation workflow.
3Measurement precision
If detailed contextual information is provided across sensor modalities, then annotation accuracy improves, but cognitive load and processing complexity increase
Solution Approach 1:
The patent segments the annotation process into distinct, manageable stages: initial LiDAR annotation, subsequent camera-based verification, and iterative refinement. This segmentation allows annotators to focus on specific tasks at each stage rather than processing all contextual information simultaneously, improving annotation accuracy while managing cognitive load through structured workflow decomposition.
Data Source
AI summary
An annotation pipeline may be used to produce 2D and/or 3D ground truth data for deep neural networks, such as autonomous or semi-autonomous vehicle perception networks. Initially, sensor data may be captured with different types of sensors and synchronized to align frames of sensor data that represent a similar world state. The aligned frames may be sampled and packaged into a sequence of annotation scenes to be annotated. An annotation project may be decomposed into modular tasks and encoded into a labeling tool, which assigns tasks to labelers and arranges the order of inputs using a wizard that steps through the tasks. During the tasks, each type of sensor data in an annotation scene may be simultaneously presented, and information may be projected across sensor modalities to provide useful contextual information. After all annotation tasks have been completed, the resulting ground truth data may be exported in any suitable format.