Synthetic Ground Truth Drive Data for Map Evaluation
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Solution Overview
Problem
The challenge of obtaining accurate and cost-effective labeled data for evaluating automated map making processes is significant due to the scarcity and susceptibility to error of traditional ground truth data, which is costly, time-consuming, and prone to inconsistencies.
Innovation Solution
A system generates synthetic ground truth drive and sensor observation data using simulation characteristics to create simulated drive paths and sensor observations, allowing for precise evaluation of map building processes by comparing estimated positions to known ground truth locations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional ground truth data is used for evaluating automated map making processes, then the evaluation can be performed with real-world data, but the data is costly, time-consuming, and prone to inconsistencies
Solution Approach 1:
The patent creates synthetic ground truth data by copying the structure and characteristics of real ground truth data through simulation. The system generates simulated drive paths and sensor observations that replicate real-world conditions while eliminating the time-consuming data collection process, thus maintaining reliability while reducing time loss
Solution Approach 2:
The patent performs preliminary action by pre-generating synthetic ground truth data before actual evaluation needs arise. The simulation framework allows ground truth data to be created in advance with controlled characteristics, eliminating the need for time-consuming real-world data collection when evaluation is needed
2Measurement precision
If traditional ground truth data is collected manually, then the data reflects real-world conditions, but the process is costly and prone to human error
Solution Approach 1:
The patent uses copying by creating synthetic replicas of real ground truth data through simulation engines. These synthetic copies maintain the measurement precision and real-world characteristics needed for accurate evaluation while dramatically reducing the cost and human error associated with manual data collection
Solution Approach 2:
The simulation framework enables self-service by automatically generating ground truth data without human intervention. The system autonomously creates simulated drive paths, sensor observations, and ground truth labels, eliminating manual labor costs and human error while maintaining data quality
3Productivity
If more annotated data is collected to improve model training, then machine learning performance improves, but the cost and time required for data annotation increases significantly
Solution Approach 1:
The patent applies copying by generating multiple synthetic datasets that replicate the diversity and complexity of real annotated data. These synthetic copies provide abundant training data for improving model training quality without requiring proportional increases in time-consuming manual annotation efforts
Solution Approach 2:
The system performs preliminary action by pre-generating large volumes of synthetic annotated data in advance. This allows extensive data to be available for model training without the time loss of annotating equivalent real data, as the synthetic data can be generated automatically and scaled as needed
4Ease of manufacture
If synthetic data is generated to reduce costs, then data generation becomes more cost-effective, but the data may lack the realism of real-world data
Solution Approach 1:
The patent uses parameter changes by adjusting simulation parameters to match real-world statistical characteristics and distributions. The synthetic data generation process incorporates realistic noise models, sensor error characteristics, and environmental conditions, ensuring that cost-effective synthetic data maintains the realism and reliability needed for accurate evaluation
Data Source
AI summary
An approach is provided for generating synthetic ground truth drive and sensor observation data. The approach, for instance, receiving, by a processor, a first input specifying ground truth data indicating one or more ground truth locations of one or more map features. The approach also involves receiving a second input specifying one or more simulation characteristics. The approach further involves generating simulated drive data based on the ground truth data and the one or more simulation characteristics. For example, the simulated drive data includes (a) one or more simulated drive paths within a region of interest encompassing the one or more ground truth locations and (b) one or more simulated sensor observations of the one or more map features. The approach further involves providing the simulated drive data as an output.