Annotated Traffic Area Data for Autonomous Driving
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Solution Overview
Problem
Current methods for generating annotation data for autonomous driving are costly and prone to errors due to 'label noise' from human annotators, requiring extensive quality control and unnecessary annotation of entire traffic areas, rather than just relevant sections used by vehicles.
Innovation Solution
A method that reads in traffic area data and automatically detected position data to associate annotation data sets using machine learning methods, focusing on areas actually used by road users, thereby reducing complexity and error by limiting annotations to relevant areas and using neighboring data sets to assess usability of adjacent sections.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If human annotators manually annotate entire traffic areas, then comprehensive coverage is achieved, but annotation cost and time increase significantly
Solution Approach 1:
The patent extracts only the relevant portions of traffic areas that are actually used by road users (vehicle paths, pedestrian zones) and annotates these specific sections rather than the entire traffic area. This is achieved by combining sensor data with map data to identify and annotate only the necessary regions, significantly reducing annotation scope while maintaining comprehensive coverage of actual usage areas.
Solution Approach 2:
The system uses sensor data from road users (vehicles, pedestrians) to automatically identify and mark the areas they traverse. The road users themselves provide the data needed for annotation through their own movement and sensor outputs, eliminating the need for separate manual annotation processes for these areas.
2Measurement precision
If human annotators manually label traffic areas, then detailed information is provided, but errors occur due to label noise and estimation requirements
Solution Approach 1:
The patent replaces manual human annotation with an automated system that combines sensor data processing, machine learning classification, and map data integration. This automated mechanism eliminates human estimation errors and label noise while providing consistent, reliable annotations through objective data processing and algorithmic classification.
Solution Approach 2:
The system incorporates quality control through multiple validation layers including machine learning-based classification that can identify and correct annotation errors, and cross-referencing with sensor data and map information to verify annotation accuracy before final output.
3Quantity of substance
If annotations are provided for all traffic areas, then complete data coverage is achieved, but complexity of data processing increases
Solution Approach 1:
The patent extracts and processes only the subset of traffic area data that corresponds to actual road user paths and usage areas. By filtering out irrelevant areas (parking zones, unused sidewalks) and focusing only on dynamically used sections, the system reduces data processing complexity while maintaining complete coverage of actually traversed areas.
Solution Approach 2:
The system segments the traffic area into distinct zones based on road user usage patterns, separating actively used paths from inactive areas. This segmentation allows the processing system to apply different handling rules to different segments, reducing overall complexity by processing only relevant segments in detail while summarizing or excluding inactive areas.
Data Source
AI summary
A method for providing annotated traffic area data. The method includes a step of reading in traffic area data that in each case represent a section of a traffic area used by a road user, and reading in automatically detected position data of the road user in the traffic area. In addition, the method includes a step of associating in each case at least one annotation data set with the traffic area data at which the road user is situated at the moment, corresponding to the detected position data, in order to obtain the annotated traffic area data that signal a use option and/or movement option of the traffic area, represented by the traffic area data, by another road user, in particular the annotation data set having been generated using a machine learning method and/or a classifier based on a machine learning algorithm.