Image Augmentation With Simulated Road Debris for AV Detection
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current autonomous vehicle systems face challenges in training deep neural networks to accurately detect road debris and objects due to the scarcity of relevant training data, as collecting real-world images of road debris is difficult and dangerous, and requires extensive computational power and human effort.
Innovation Solution
The system uses real-world images augmented with simulated objects to train machine learning models, allowing for a high volume of diverse training data that maintains rich detail, enabling more accurate object detection and reduced training time, and includes features like zero-shot learning to handle previously unseen objects.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If real-world images of road debris are collected for training, then the training data reflects actual driving conditions, but the amount of useful training data is severely limited due to safety concerns and the rarity of debris on roads
Solution Approach 1:
The patent creates synthetic copies of road debris objects using 3D models and renders them into training images. These synthetic debris objects are inserted into real-world road images, generating abundant training data that replicates actual driving conditions without requiring physical debris to be present on roads.
Solution Approach 2:
The patent introduces an intermediary synthetic rendering system that bridges the gap between real-world road images and training data requirements. This intermediary process generates realistic debris images by combining 3D models with real road backgrounds, eliminating the need to physically collect debris-filled road scenes.
2Reliability
If physical vehicles are used to capture training data by driving around with road objects, then real-world scenarios are obtained, but this approach is dangerous and difficult to implement
Solution Approach 1:
Instead of physically placing debris on roads with real vehicles, the patent creates digital copies of debris through 3D modeling and rendering. These synthetic debris objects are then composited into real road images, achieving realistic training scenarios without any physical safety risks.
Solution Approach 2:
The patent replaces the mechanical process of physically driving vehicles with debris on roads with a computational rendering process. Synthetic debris images are generated and inserted into real road images through image processing, eliminating the need for dangerous physical operations.
3Measurement precision
If thousands of training data instances are collected to sufficiently train a DNN, then detection accuracy improves, but the time and effort required for data collection becomes excessive
Solution Approach 1:
The patent performs preliminary actions by pre-rendering numerous synthetic debris images with varying parameters (types, sizes, orientations, lighting conditions) before training begins. This pre-computation of diverse training images eliminates the need for time-consuming data collection during the training process.
Solution Approach 2:
The patent systematically varies parameters of synthetic debris objects (type, size, orientation, position, lighting, weather conditions) to generate diverse training images. This parameter-based generation creates thousands of training instances rapidly, avoiding the time required for manual data collection.
4Reliability
If competing interests of safety and accuracy are pursued in autonomous driving systems, then both safety and detection accuracy are improved, but the system development becomes increasingly onerous
Solution Approach 1:
The patent creates a universal synthetic data generation system that serves multiple functions: generating diverse debris types, varying environmental conditions, creating different lighting scenarios, and producing annotated training data all through a single rendering pipeline. This multi-functionality reduces development complexity while maintaining high safety and accuracy standards.
Data Source
AI summary
In various examples, systems and methods are disclosed that preserve rich, detail-centric information from a real-world image by augmenting the real-world image with simulated objects to train a machine learning model to detect objects in an input image. The machine learning model may be trained, in deployment, to detect objects and determine bounding shapes to encapsulate detected objects. The machine learning model may further be trained to determine the type of road object encountered, calculate hazard ratings, and calculate confidence percentages. In deployment, detection of a road object, determination of a corresponding bounding shape, identification of road object type, and/or calculation of a hazard rating by the machine learning model may be used as an aid for determining next steps regarding the surrounding environment—e.g., navigating around the road debris, driving over the road debris, or coming to a complete stop—in a variety of autonomous machine applications.