VAE Road Feature Decay Prediction for Reliable Autonomous Detection
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Solution Overview
Problem
Current methods for 3D road geometry modeling and feature detection in autonomous vehicles are resource-intensive, time-consuming, and costly, and struggle with unreliable feature detection due to feature decay over time, which can impact autonomous driving accuracy and efficiency.
Innovation Solution
A method using a variational autoencoder network to learn an implicit transformation and interpolate feature space decay from a single new image, allowing for the generation of revised images that account for feature weathering, enabling accurate detection and autonomous control even as features deteriorate.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional methods for 3D road geometry modeling and feature detection are used, then comprehensive feature detection can be achieved, but the process becomes resource-intensive, time-consuming, and costly
Solution Approach 1:
The patent uses Generative Adversarial Networks (GANs) to create synthetic weathered images that copy and simulate the appearance of decayed road features. These synthetic copies are then used to train perception systems, eliminating the need for costly and time-consuming collection of real-world weathered feature data while maintaining detection reliability
Solution Approach 2:
The system performs preliminary training using synthetic weathered images generated before actual deployment. By pre-training the perception system with artificially created decayed feature images, the system prepares in advance for various decay scenarios, reducing the need for resource-intensive real-time processing and extensive field data collection
2Measurement precision
If feature detection systems are designed to detect weathered features, then detection accuracy under decay conditions improves, but the complexity of training data collection and processing increases
Solution Approach 1:
Instead of collecting diverse real-world weathered feature data which would require complex field operations and data management systems, the patent uses GANs to generate synthetic copies of weathered features. This copying approach maintains measurement precision by creating realistic decay scenarios while dramatically simplifying the training system complexity
Solution Approach 2:
The GAN-based system is self-service in that it automatically generates the training data it needs without requiring external data collection operations. The system serves its own training data requirements by synthesizing weathered feature images programmatically, eliminating the need for complex field data collection infrastructure and manual data processing pipelines
3Productivity
If autonomous vehicles use current feature detection methods, then navigation can be performed, but reliability deteriorates when features decay over time
Solution Approach 1:
The system performs preliminary training with synthetic weathered images to prepare the perception system for decay conditions before actual navigation. This pre-exposure to simulated decay scenarios enables the system to maintain reliable feature detection during navigation, preventing the reliability deterioration that occurs with current methods
Solution Approach 2:
By using synthetic copies of weathered features for training, the system learns to recognize decayed features without requiring actual decayed features to be present during navigation. This copying approach ensures that navigation efficiency is maintained while reliability under decay conditions is significantly improved
Data Source
AI summary
An apparatus, method and computer program product are provided for predicting feature space decay using variational auto-encoder networks. Methods may include: receiving a first image of a road segment including a feature disposed along the road segment; applying a loss function to the feature of the first image; generating a revised image, where the revised image includes a weathered iteration of the feature; generating a predicted image using interpolation between the image and the revised image of a partially weathered iteration of the feature; receiving a user image, where the user image is received from a vehicle traveling along the road segment; correlating a feature in the user image to the partially weathered iteration of the feature in the predicted image; and establishing that the feature in the user image is the feature disposed along the road segment.