3D Intersection Prediction From 2D Images With Geometric Constraints
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Solution Overview
Problem
Conventional systems for autonomous driving face challenges in accurately predicting 3D intersection structures from 2D image data, due to the need for manual labeling of HD maps, inaccuracies from flat ground assumptions, and the complexity of generating reliable 3D ground truth data.
Innovation Solution
The use of deep neural networks (DNNs) to predict 3D intersection structures directly from 2D image data, leveraging live perception capabilities to detect 3D locations of intersection structures, and employing loss functions for 2D ground truth data and 3D geometric consistency to improve prediction accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If deep neural networks are trained to predict intersection information in 3D world-space using 3D ground truth data from LIDAR, then the prediction accuracy is improved, but the training complexity and time increase significantly
Solution Approach 1:
The patent uses 2D image data as a simplified copy or representation of the 3D world, training the DNN to predict 3D intersection structures directly from 2D inputs. This avoids the complex process of collecting and annotating 3D LIDAR ground truth data while maintaining prediction accuracy, as the network learns to infer 3D structures from the 2D visual information it receives from cameras.
Solution Approach 2:
The patent replaces the mechanical/physical process of 3D data collection using LIDAR sensors with a computational approach using 2D image processing and deep learning. Instead of physically capturing 3D point clouds and manually annotating them, the system uses 2D camera images processed through a trained DNN to predict 3D intersection structures, substituting a simpler sensing and processing mechanism.
2Ease of manufacture
If 2D to 3D coordinate conversion is performed using flat ground assumption, then the processing simplicity is improved, but the accuracy deteriorates due to road slopes and grades
Solution Approach 1:
The patent trains the DNN to predict 3D world-space coordinates directly from 2D image inputs, effectively learning the dimensional transformation within the network architecture. This allows the system to account for road slopes and grades by learning 3D geometric relationships from 2D images during training, rather than relying on simplified flat-ground assumptions in the conversion process.
3Measurement precision
If manual labeling of HD maps is performed to identify intersections, then the map accuracy is improved, but the time consumption and labor requirements increase
Solution Approach 1:
The system enables vehicles to autonomously predict and identify intersection structures using the trained DNN, without requiring manual map updates or human labeling efforts. The DNN processes 2D images from onboard cameras to detect 3D intersection information in real-time, allowing the vehicle to serve itself by independently identifying navigation-critical features without external human intervention or pre-labeled HD maps.
Solution Approach 2:
The DNN is trained in advance on a dataset of 2D images with annotated 3D intersection structures, performing the complex labeling task beforehand during the training phase. Once trained, the network can rapidly predict intersection information during actual vehicle operation without requiring real-time manual labeling, having already learned the mapping relationships during offline training.
4Measurement precision
If LIDAR data is annotated to generate 3D ground truth information, then the training data quality is improved, but the annotation complexity and cost increase
Solution Approach 1:
The patent uses 2D image data as a substitute for 3D LIDAR data during training, recognizing that 2D images are easier and less expensive to annotate. By training the DNN to predict 3D intersection structures from 2D inputs, the system avoids the need to collect, process, and manually annotate complex 3D point cloud data, while still achieving accurate predictions through the learned 2D-to-3D mapping.
Data Source
AI summary
In various examples, a three-dimensional (3D) intersection structure may be predicted using a deep neural network (DNN) based on processing two-dimensional (2D) input data. To train the DNN to accurately predict 3D intersection structures from 2D inputs, the DNN may be trained using a first loss function that compares 3D outputs of the DNN—after conversion to 2D space—to 2D ground truth data and a second loss function that analyzes the 3D predictions of the DNN in view of one or more geometric constraints—e.g., geometric knowledge of intersections may be used to penalize predictions of the DNN that do not align with known intersection and/or road structure geometries. As such, live perception of an autonomous or semi-autonomous vehicle may be used by the DNN to detect 3D locations of intersection structures from 2D inputs.