Synthetic Scene Generation for Autonomous Vehicle Path Optimization
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Solution Overview
Problem
Autonomous vehicles face challenges in effectively navigating construction zones due to the scarcity and high cost of real-world construction zone data, which limits the training of machine learning models for scene understanding and path optimization.
Innovation Solution
The implementation of synthetic scene data generation using a roadgraph solver to create optimized paths that avoid obstacles, allowing for the training of machine learning models without the need for real annotated data, by generating synthetic construction zones with ground-truth annotations and using techniques like discrete and continuous path optimization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If real-world construction zone data is collected and manually labeled for training machine learning models, then the quality and accuracy of scene understanding improves, but the cost and time required for data collection and annotation increases significantly
Solution Approach 1:
The patent creates synthetic copies of construction zone scenes through simulation environments rather than collecting real-world data. The system generates virtual representations of construction zones with artificial objects, road graphs, and environmental conditions that replicate real scenarios, eliminating the need for time-consuming manual data collection and annotation while maintaining training effectiveness
Solution Approach 2:
The system performs preliminary generation of synthetic training data before actual model training begins. By pre-generating diverse construction zone scenarios, road graphs, and annotated data through simulation, the system prepares comprehensive training datasets in advance, avoiding the need for time-consuming real-world data collection during the modeling phase
2Quantity of substance
If more real-world construction zone data is collected to increase training data scale and diversity, then the model's navigation capability improves, but the cost and feasibility of data collection deteriorates
Solution Approach 1:
The patent uses synthetic copying to generate unlimited diverse training data through simulation environments. The system can create numerous variations of construction zones, road layouts, and environmental conditions programmatically, achieving large-scale diverse training data without the feasibility constraints of real-world data collection
Solution Approach 2:
The system varies parameters such as road graph configurations, object positions, environmental conditions, and scene characteristics to generate diverse synthetic training data. By systematically changing these parameters in simulation, the system achieves data diversity and scale that would be prohibitively expensive and complex to obtain through real-world collection
3Measurement precision
If manual labeling is used to create ground-truth annotations for training data, then the precision of path optimization improves, but the cost and complexity of the process increases
Solution Approach 1:
The patent automatically generates ground-truth annotations by copying and adapting road graph data and scene configurations from the simulation environment. The synthetic data generation process inherently produces precise annotations through programmatic creation of road graphs, object positions, and environmental features, eliminating manual labeling complexity while maintaining precision
Data Source
AI summary
A system includes a memory device, and a processing device, operatively coupled to the memory device, to receive a set of input data including a roadgraph. The roadgraph includes an autonomous vehicle driving path. The processing device is further to determine that the autonomous vehicle driving path is affected by one or more obstacles, identify a set of candidate paths that avoid the one or more obstacles, each candidate path of the set of candidate paths being associated with a cost value, select, from the set of candidate paths, a candidate path with an optimal cost value to obtain a selected candidate path, generate a synthetic scene based on the selected candidate path, and train a machine learning model to navigate an autonomous vehicle based on the synthetic scene.